Update app.py

app.py

@@ -1,742 +1,837 @@
-import os
-import gradio as gr
-import torch
-import re
-import time
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from huggingface_hub import hf_hub_download, snapshot_download
-import json
-from typing import Dict, List, Any, Optional, Union
-
-# Import agent modules
-from agent_reasoning import ReasoningEngine
-from agent_tasks import TaskExecutor
-from agent_memory import MemoryManager
-
-class ResuRankAgent:
-    """Autonomous AI Agent similar to Manus AI
-    
-    This agent can:
-    1. Process user queries and generate responses
-    2. Perform reasoning through chain-of-thought
-    3. Execute tasks based on user instructions
-    4. Maintain conversation context
-    """
-    
-    def __init__(self, model_id="google/flan-t5-large", use_cache=True):
-        """Initialize the ResuRank Agent
-        
-        Args:
-            model_id: Hugging Face model ID to use for the agent
-            use_cache: Whether to use cached models from Hugging Face Hub
-        """
-        self.model_id = model_id
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        print(f"Using device: {self.device}")
-        
-        # Load model and tokenizer from Hugging Face Hub
-        print(f"Loading model {model_id} from Hugging Face Hub...")
-        try:
-            # Use cached models if available
-            if use_cache:
-                print("Using cached models if available...")
-                self.tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir="./.cache")
-                self.model = AutoModelForCausalLM.from_pretrained(
-                    model_id,
-                    torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
-                    low_cpu_mem_usage=True,
-                    device_map="auto",
-                    cache_dir="./.cache"
-                )
-            else:
-                # Download models directly from Hugging Face Hub
-                print("Downloading models from Hugging Face Hub...")
-                self.tokenizer = AutoTokenizer.from_pretrained(model_id)
-                self.model = AutoModelForCausalLM.from_pretrained(
-                    model_id,
-                    torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
-                    low_cpu_mem_usage=True,
-                    device_map="auto"
-                )
-            
-            print(f"Successfully loaded model {model_id}")
-        except Exception as e:
-            print(f"Error loading model: {str(e)}")
-            print("Falling back to smaller model...")
-            fallback_model = "google/flan-t5-base"
-            self.model_id = fallback_model
-            self.tokenizer = AutoTokenizer.from_pretrained(fallback_model, cache_dir="./.cache")
-            self.model = AutoModelForCausalLM.from_pretrained(
-                fallback_model,
-                torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
-                low_cpu_mem_usage=True,
-                device_map="auto",
-                cache_dir="./.cache"
-            )
-        
-        # Initialize agent components
-        self.reasoning_engine = ReasoningEngine(self.model, self.tokenizer, self.device)
-        self.memory_manager = MemoryManager(max_history_length=20)
-        self.task_executor = TaskExecutor(self.reasoning_engine)
-        
-    def process_query(self, query: str, use_reasoning: bool = True) -> Dict[str, Any]:
-        """Process a user query and generate a response
-        
-        Args:
-            query: User query text
-            use_reasoning: Whether to use chain-of-thought reasoning
-            
-        Returns:
-            Dictionary containing response and metadata
-        """
-        # Add query to conversation history
-        self.memory_manager.add_message("user", query)
-        
-        start_time = time.time()
-        
-        # Check if this is a task execution request
-        is_task_request = self._is_task_request(query)
-        
-        # Process the query with appropriate method
-        if is_task_request:
-            # Handle as a task execution request
-            task_result = self.execute_task(query)
-            response = f"I've executed your task. {task_result.get('result', '')}\n\nStatus: {task_result.get('status', 'unknown')}"
-            reasoning = task_result.get('plan', '')
-        elif use_reasoning:
-            # Use chain-of-thought reasoning
-            # Enhance with context from memory
-            facts = self.memory_manager.format_facts_for_prompt()
-            context = self.memory_manager.format_conversation_for_prompt(max_turns=5)
-            
-            # Create an enhanced query with context
-            enhanced_query = f"{facts}\n\nRecent conversation:\n{context}\n\nCurrent query: {query}"
-            
-            result = self.reasoning_engine.chain_of_thought(enhanced_query)
-            response = result["answer"]
-            reasoning = result["reasoning"]
-        else:
-            # Simple response generation without reasoning
-            conversation_prompt = self.memory_manager.format_conversation_for_prompt(max_turns=10)
-            facts_prompt = self.memory_manager.format_facts_for_prompt()
-            
-            prompt = f"{facts_prompt}\n\n{conversation_prompt}\nassistant: "
-            
-            response = self.reasoning_engine.generate_text(prompt)
-            reasoning = None
-        
-        # Add response to conversation history
-        self.memory_manager.add_message("assistant", response)
-        
-        # Extract any important facts from the conversation
-        self._extract_facts(query, response)
-        
-        processing_time = time.time() - start_time
-        
-        return {
-            "response": response,
-            "reasoning": reasoning,
-            "processing_time": processing_time,
-            "timestamp": time.time()
-        }
-        
-    def _is_task_request(self, query: str) -> bool:
-        """Determine if a query is a task execution request
-        
-        Args:
-            query: The user query
-            
-        Returns:
-            True if the query appears to be a task request, False otherwise
-        """
-        # Keywords that suggest a task execution request
-        task_keywords = [
-            "execute", "perform", "run", "do", "complete", "finish", 
-            "task", "job", "work", "action", "operation", "function",
-            "can you", "please", "help me", "i need", "i want"
-        ]
-        
-        # Check if query contains task-related keywords
-        query_lower = query.lower()
-        for keyword in task_keywords:
-            if keyword in query_lower:
-                return True
-                
-        return False
-    
-    def _extract_facts(self, query: str, response: str) -> None:
-        """Extract important facts from the conversation
-        
-        Args:
-            query: User query
-            response: Agent response
-        """
-        # Extract personal information
-        self._extract_personal_info(query)
-        
-        # Extract preferences
-        self._extract_preferences(query)
-        
-        # Extract task-related information
-        self._extract_task_info(query)
-        
-        # Use the reasoning engine to identify important facts
-        self._extract_with_reasoning(query, response)
-    
-    def _extract_personal_info(self, text: str) -> None:
-        """Extract personal information from text
-        
-        Args:
-            text: Text to extract information from
-        """
-        text_lower = text.lower()
-        
-        # Extract name
-        if "my name is" in text_lower or "i am called" in text_lower or "i'm called" in text_lower:
-            name_patterns = [
-                r"my name is ([\w\s]+)[.\,]?",
-                r"i am called ([\w\s]+)[.\,]?",
-                r"i'm called ([\w\s]+)[.\,]?"
-            ]
-            
-            for pattern in name_patterns:
-                name_match = re.search(pattern, text_lower)
-                if name_match:
-                    name = name_match.group(1).strip()
-                    self.memory_manager.add_important_fact(f"User's name is {name}", "user")
-                    break
-        
-        # Extract location
-        if "i am from" in text_lower or "i'm from" in text_lower or "i live in" in text_lower:
-            location_patterns = [
-                r"i am from ([\w\s]+)[.\,]?",
-                r"i'm from ([\w\s]+)[.\,]?",
-                r"i live in ([\w\s]+)[.\,]?"
-            ]
-            
-            for pattern in location_patterns:
-                location_match = re.search(pattern, text_lower)
-                if location_match:
-                    location = location_match.group(1).strip()
-                    self.memory_manager.add_important_fact(f"User is from {location}", "user")
-                    break
-        
-        # Extract profession/occupation
-        if "i work as" in text_lower or "i am a" in text_lower or "i'm a" in text_lower:
-            profession_patterns = [
-                r"i work as a[n]? ([\w\s]+)[.\,]?",
-                r"i am a[n]? ([\w\s]+)[.\,]?",
-                r"i'm a[n]? ([\w\s]+)[.\,]?"
-            ]
-            
-            for pattern in profession_patterns:
-                profession_match = re.search(pattern, text_lower)
-                if profession_match:
-                    profession = profession_match.group(1).strip()
-                    self.memory_manager.add_important_fact(f"User works as a {profession}", "user")
-                    break
-    
-    def _extract_preferences(self, text: str) -> None:
-        """Extract user preferences from text
-        
-        Args:
-            text: Text to extract information from
-        """
-        text_lower = text.lower()
-        
-        # Extract likes
-        if "i like" in text_lower or "i love" in text_lower or "i enjoy" in text_lower:
-            like_patterns = [
-                r"i like ([\w\s]+)[.\,]?",
-                r"i love ([\w\s]+)[.\,]?",
-                r"i enjoy ([\w\s]+)[.\,]?"
-            ]
-            
-            for pattern in like_patterns:
-                like_match = re.search(pattern, text_lower)
-                if like_match:
-                    like = like_match.group(1).strip()
-                    self.memory_manager.add_important_fact(f"User likes {like}", "user")
-                    break
-        
-        # Extract dislikes
-        if "i don't like" in text_lower or "i hate" in text_lower or "i dislike" in text_lower:
-            dislike_patterns = [
-                r"i don't like ([\w\s]+)[.\,]?",
-                r"i hate ([\w\s]+)[.\,]?",
-                r"i dislike ([\w\s]+)[.\,]?"
-            ]
-            
-            for pattern in dislike_patterns:
-                dislike_match = re.search(pattern, text_lower)
-                if dislike_match:
-                    dislike = dislike_match.group(1).strip()
-                    self.memory_manager.add_important_fact(f"User dislikes {dislike}", "user")
-                    break
-    
-    def _extract_task_info(self, text: str) -> None:
-        """Extract task-related information from text
-        
-        Args:
-            text: Text to extract information from
-        """
-        text_lower = text.lower()
-        
-        # Extract goals
-        if "my goal is" in text_lower or "i want to" in text_lower or "i need to" in text_lower:
-            goal_patterns = [
-                r"my goal is to ([\w\s]+)[.\,]?",
-                r"i want to ([\w\s]+)[.\,]?",
-                r"i need to ([\w\s]+)[.\,]?"
-            ]
-            
-            for pattern in goal_patterns:
-                goal_match = re.search(pattern, text_lower)
-                if goal_match:
-                    goal = goal_match.group(1).strip()
-                    self.memory_manager.add_important_fact(f"User's goal is to {goal}", "user")
-                    break
-    
-    def _extract_with_reasoning(self, query: str, response: str) -> None:
-        """Use the reasoning engine to extract important facts
-        
-        Args:
-            query: User query
-            response: Agent response
-        """
-        # Only use this for longer queries to avoid unnecessary processing
-        if len(query) < 50:
-            return
-        
-        extraction_prompt = f"""Extract important facts from this conversation:
-
-User: {query}
-Assistant: {response}
-
-List of important facts (one per line):
-1. """
-        
-        try:
-            facts_text = self.reasoning_engine.generate_text(extraction_prompt, max_length=256)
-            
-            # Parse the facts
-            for line in facts_text.split('\n'):
-                line = line.strip()
-                if line and (line[0].isdigit() or line.startswith('- ')):
-                    # Remove numbering or bullet points
-                    fact = re.sub(r'^\d+\.\s*|^-\s*', '', line).strip()
-                    if fact and len(fact) > 10:  # Only add substantial facts
-                        self.memory_manager.add_important_fact(fact, "inference")
-        except Exception as e:
-            print(f"Error extracting facts with reasoning: {str(e)}")
-            # Continue without adding facts
-    
-
-    
-    def execute_task(self, task_description: str) -> Dict[str, Any]:
-        """Execute a task based on the description
-        
-        Args:
-            task_description: Description of the task to execute
-            
-        Returns:
-            Dictionary containing task results and status
-        """
-        return self.task_executor.execute_task(task_description)
-    
-    def get_status(self) -> Dict[str, Any]:
-        """Get the current status of the agent
-        
-        Returns:
-            Dictionary containing agent status information
-        """
-        memory_stats = self.memory_manager.get_memory_stats()
-        task_status = self.task_executor.get_task_status()
-        
-        return {
-            "model_id": self.model_id,
-            "device": self.device,
-            "conversation_turns": memory_stats["conversation_turns"],
-            "important_facts": memory_stats["important_facts"],
-            "current_task": task_status["current_task"],
-            "task_status": task_status["status"]
-        }
-    
-    def clear_conversation(self) -> None:
-        """Clear the conversation history"""
-        self.memory_manager.clear_conversation_history()
-        
-    def process_document(self, document_text: str, document_type: str = "resume") -> Dict[str, Any]:
-        """Process a document (like a resume) and extract information
-        
-        Args:
-            document_text: The text content of the document
-            document_type: The type of document (e.g., "resume", "job_description")
-            
-        Returns:
-            Dictionary containing extracted information and analysis
-        """
-        self.memory_manager.store_session_data(f"last_{document_type}", document_text)
-        start_time = time.time()
-        
-        # Create a prompt for document analysis
-        analysis_prompt = f"""I need to analyze this {document_type} document and extract key information:
-
-{document_text}
-
-Detailed analysis:"""
-        
-        # Generate analysis using reasoning engine
-        analysis = self.reasoning_engine.generate_text(analysis_prompt, max_length=1024)
-        
-        # Extract structured information based on document type
-        if document_type.lower() == "resume":
-            extraction_prompt = f"""Based on this resume:
-{document_text}
-
-Extract the following information in a structured format:
-1. Name:
-2. Contact Information:
-3. Education:
-4. Work Experience:
-5. Skills:
-6. Projects:
-7. Certifications:
-8. Languages:
-9. Key Strengths:
-"""
-        elif document_type.lower() == "job_description":
-            extraction_prompt = f"""Based on this job description:
-{document_text}
-
-Extract the following information in a structured format:
-1. Job Title:
-2. Company:
-3. Location:
-4. Required Skills:
-5. Required Experience:
-6. Education Requirements:
-7. Responsibilities:
-8. Benefits:
-9. Key Qualifications:
-"""
-        else:
-            extraction_prompt = f"""Extract key information from this document:
-{document_text}
-
-Key information:
-1. """
-        
-        # Generate structured extraction
-        structured_info = self.reasoning_engine.generate_text(extraction_prompt, max_length=1024)
-        
-        # Add important facts to memory
-        self._extract_document_facts(document_text, document_type, structured_info)
-        
-        processing_time = time.time() - start_time
-        
-        return {
-            "document_type": document_type,
-            "analysis": analysis,
-            "structured_info": structured_info,
-            "processing_time": processing_time,
-            "timestamp": time.time()
-        }
-    
-    def _extract_document_facts(self, document_text: str, document_type: str, structured_info: str) -> None:
-        """Extract important facts from a document and add them to memory
-        
-        Args:
-            document_text: The text content of the document
-            document_type: The type of document
-            structured_info: Structured information extracted from the document
-        """
-        # Extract key facts based on document type
-        if document_type.lower() == "resume":
-            # Extract name if present
-            name_match = re.search(r"Name:\s*([\w\s]+)\n", structured_info)
-            if name_match:
-                name = name_match.group(1).strip()
-                self.memory_manager.add_important_fact(f"Document contains resume for {name}", "document")
-            
-            # Extract skills
-            skills_match = re.search(r"Skills:\s*([\w\s,\.\-\+]+)\n", structured_info)
-            if skills_match:
-                skills = skills_match.group(1).strip()
-                self.memory_manager.add_important_fact(f"Resume shows skills in: {skills}", "document")
-            
-            # Extract education
-            education_match = re.search(r"Education:\s*([\w\s,\.\-\+]+)\n", structured_info)
-            if education_match:
-                education = education_match.group(1).strip()
-                self.memory_manager.add_important_fact(f"Resume shows education: {education}", "document")
-        
-        elif document_type.lower() == "job_description":
-            # Extract job title
-            title_match = re.search(r"Job Title:\s*([\w\s]+)\n", structured_info)
-            if title_match:
-                title = title_match.group(1).strip()
-                self.memory_manager.add_important_fact(f"Document contains job description for {title}", "document")
-            
-            # Extract required skills
-            skills_match = re.search(r"Required Skills:\s*([\w\s,\.\-\+]+)\n", structured_info)
-            if skills_match:
-                skills = skills_match.group(1).strip()
-                self.memory_manager.add_important_fact(f"Job requires skills in: {skills}", "document")
-        
-        # Add general document fact
-        self.memory_manager.add_important_fact(f"Processed a {document_type} document", "system")
-    
-    def rank_resumes(self, job_description: str, resumes: List[str]) -> Dict[str, Any]:
-        """Rank multiple resumes against a job description
-        
-        Args:
-            job_description: The job description text
-            resumes: List of resume texts to rank
-            
-        Returns:
-            Dictionary containing rankings and analysis
-        """
-        start_time = time.time()
-        
-        # Process the job description first
-        job_result = self.process_document(job_description, "job_description")
-        job_analysis = job_result["structured_info"]
-        
-        # Process each resume
-        resume_results = []
-        for i, resume in enumerate(resumes):
-            result = self.process_document(resume, "resume")
-            resume_results.append({
-                "index": i,
-                "text": resume,
-                "analysis": result["structured_info"]
-            })
-        
-        # Create a ranking prompt
-        ranking_prompt = f"""I need to rank these resumes based on how well they match the job description.
-
-Job Description Analysis:
-{job_analysis}
-
-Resumes:
-"""
-        
-        for i, result in enumerate(resume_results):
-            ranking_prompt += f"\nResume {i+1}:\n{result['analysis']}\n"
-        
-        ranking_prompt += "\nRank these resumes from best to worst match for the job, with detailed reasoning for each:"
-        
-        # Generate the ranking analysis
-        ranking_analysis = self.reasoning_engine.generate_text(ranking_prompt, max_length=2048)
-        
-        # Generate a numerical scoring for each resume
-        scoring_prompt = f"""Based on my analysis of how well these resumes match the job description:
-{ranking_analysis}
-
-Assign a numerical score from 0-100 for each resume, where 100 is a perfect match:
-
-Resume 1 Score:"""
-        
-        scores_text = self.reasoning_engine.generate_text(scoring_prompt, max_length=512)
-        
-        # Parse scores (simple regex approach)
-        scores = []
-        for i in range(len(resume_results)):
-            score_match = re.search(f"Resume {i+1} Score:\s*(\d+)", scores_text)
-            if score_match:
-                scores.append(int(score_match.group(1)))
-            else:
-                # Default score if parsing fails
-                scores.append(50)
-        
-        # Create the final rankings
-        rankings = []
-        for i, score in enumerate(scores):
-            rankings.append({
-                "resume_index": i,
-                "score": score,
-                "resume_text": resumes[i][:100] + "..."  # Truncated for readability
-            })
-        
-        # Sort by score (descending)
-        rankings.sort(key=lambda x: x["score"], reverse=True)
-        
-        processing_time = time.time() - start_time
-        
-        return {
-            "rankings": rankings,
-            "analysis": ranking_analysis,
-            "job_description": job_description,
-            "processing_time": processing_time
-        }
-
-# Create the Gradio interface
-def create_interface():
-    # Initialize the agent with a suitable model for Hugging Face Spaces
-    # Using a smaller model by default for better performance in Spaces
-    agent = ResuRankAgent(model_id="google/flan-t5-base", use_cache=True)
-    
-    with gr.Blocks(title="ResuRank AI Agent") as interface:
-        gr.Markdown("# ResuRank AI Agent")
-        gr.Markdown("An autonomous AI agent that can process queries, perform reasoning, and execute tasks.")
-        
-        with gr.Tab("Chat"):
-            chatbot = gr.Chatbot(height=400)
-            msg = gr.Textbox(label="Your message", placeholder="Ask me anything...")
-            with gr.Row():
-                submit_btn = gr.Button("Submit")
-                clear_btn = gr.Button("Clear")
-            
-            reasoning_checkbox = gr.Checkbox(label="Use reasoning", value=True)
-            
-            if reasoning_checkbox.value:
-                reasoning_output = gr.Textbox(label="Reasoning", interactive=False)
-            else:
-                reasoning_output = gr.Textbox(label="Reasoning", interactive=False, visible=False)
-            
-            def respond(message, chat_history, use_reasoning):
-                if not message.strip():
-                    return chat_history, "", ""
-                
-                # Process the query
-                result = agent.process_query(message, use_reasoning=use_reasoning)
-                
-                # Update chat history
-                chat_history.append((message, result["response"]))
-                
-                return chat_history, "", result.get("reasoning", "")
-            
-            def clear_chat():
-                agent.clear_conversation()
-                return [], "", ""
-            
-            # Set up event handlers
-            submit_btn.click(respond, [msg, chatbot, reasoning_checkbox], [chatbot, msg, reasoning_output])
-            msg.submit(respond, [msg, chatbot, reasoning_checkbox], [chatbot, msg, reasoning_output])
-            clear_btn.click(clear_chat, None, [chatbot, msg, reasoning_output])
-            reasoning_checkbox.change(lambda x: gr.update(visible=x), reasoning_checkbox, reasoning_output)
-        
-        with gr.Tab("Task Execution"):
-            task_input = gr.Textbox(label="Task Description", placeholder="Describe the task to execute...")
-            execute_btn = gr.Button("Execute Task")
-            
-            with gr.Row():
-                with gr.Column():
-                    plan_output = gr.Textbox(label="Execution Plan", interactive=False)
-                with gr.Column():
-                    results_output = gr.Textbox(label="Task Results", interactive=False)
-            
-            task_status = gr.Textbox(label="Task Status", value="idle", interactive=False)
-            
-            def execute_task(task_description):
-                if not task_description.strip():
-                    return "No task provided.", "", "idle"
-                
-                # Execute the task
-                result = agent.execute_task(task_description)
-                
-                return result.get("plan", ""), result.get("result", ""), result.get("status", "")
-            
-            # Set up event handlers
-            execute_btn.click(execute_task, task_input, [plan_output, results_output, task_status])
-        
-        with gr.Tab("Agent Status"):
-            status_btn = gr.Button("Refresh Status")
-            
-            with gr.Row():
-                with gr.Column():
-                    model_info = gr.Textbox(label="Model Information", interactive=False)
-                with gr.Column():
-                    conversation_info = gr.Textbox(label="Conversation Information", interactive=False)
-            
-            def update_status():
-                status = agent.get_status()
-                model_text = f"Model ID: {status['model_id']}\nDevice: {status['device']}"
-                conversation_text = f"Conversation Length: {status['conversation_turns']} turns\nImportant Facts: {len(status['important_facts'])}\nCurrent Task: {status['current_task'] or 'None'}\nTask Status: {status['task_status']}"
-                
-                return model_text, conversation_text
-            
-            # Set up event handlers
-            status_btn.click(update_status, None, [model_info, conversation_info])
-            
-            # Initialize status on load
-            model_info.value, conversation_info.value = update_status()
-    
-        with gr.Tab("Document Processing"):
-            with gr.Row():
-                with gr.Column():
-                    document_input = gr.Textbox(label="Document Text", placeholder="Paste resume or job description text here...", lines=10)
-                    document_type = gr.Radio(["resume", "job_description", "other"], label="Document Type", value="resume")
-                    process_btn = gr.Button("Process Document")
-                
-            with gr.Row():
-                with gr.Column():
-                    analysis_output = gr.Textbox(label="Document Analysis", interactive=False, lines=10)
-                with gr.Column():
-                    structured_output = gr.Textbox(label="Structured Information", interactive=False, lines=10)
-            
-            def process_document(document_text, doc_type):
-                if not document_text.strip():
-                    return "No document provided.", ""
-                
-                # Process the document
-                result = agent.process_document(document_text, doc_type)
-                
-                return result.get("analysis", ""), result.get("structured_info", "")
-            
-            # Set up event handlers
-            process_btn.click(process_document, [document_input, document_type], [analysis_output, structured_output])
-        
-        with gr.Tab("Resume Ranking"):
-            with gr.Row():
-                with gr.Column():
-                    job_description_input = gr.Textbox(label="Job Description", placeholder="Paste job description here...", lines=8)
-            
-            with gr.Row():
-                with gr.Column():
-                    resume1_input = gr.Textbox(label="Resume 1", placeholder="Paste first resume here...", lines=6)
-                with gr.Column():
-                    resume2_input = gr.Textbox(label="Resume 2", placeholder="Paste second resume here...", lines=6)
-            
-            with gr.Row():
-                with gr.Column():
-                    resume3_input = gr.Textbox(label="Resume 3 (Optional)", placeholder="Paste third resume here...", lines=6)
-                with gr.Column():
-                    resume4_input = gr.Textbox(label="Resume 4 (Optional)", placeholder="Paste fourth resume here...", lines=6)
-            
-            rank_btn = gr.Button("Rank Resumes")
-            
-            ranking_output = gr.Textbox(label="Ranking Results", interactive=False, lines=15)
-            
-            def rank_resumes(job_desc, resume1, resume2, resume3, resume4):
-                if not job_desc.strip() or not resume1.strip() or not resume2.strip():
-                    return "Please provide at least a job description and two resumes."
-                
-                # Collect all non-empty resumes
-                resumes = [r for r in [resume1, resume2, resume3, resume4] if r.strip()]
-                
-                # Rank the resumes
-                result = agent.rank_resumes(job_desc, resumes)
-                
-                # Format the results
-                output = "Resume Rankings (Best to Worst Match):\n\n"
-                
-                for i, rank in enumerate(result["rankings"]):
-                    resume_num = rank["resume_index"] + 1
-                    score = rank["score"]
-                    output += f"{i+1}. Resume {resume_num} - Score: {score}/100\n"
-                
-                output += "\nDetailed Analysis:\n" + result["analysis"]
-                
-                return output
-            
-            # Set up event handlers
-            rank_btn.click(rank_resumes, [job_description_input, resume1_input, resume2_input, resume3_input, resume4_input], ranking_output)
-    
-    return interface
-
-# Launch the interface when run directly
-if __name__ == "__main__":
-    interface = create_interface()
-    interface.launch()
+import os
+import gradio as gr
+import torch
+import re
+import time
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from huggingface_hub import hf_hub_download, snapshot_download
+import json
+from typing import Dict, List, Any, Optional, Union
+
+# Import agent modules
+from agent_reasoning import ReasoningEngine
+from agent_tasks import TaskExecutor
+from agent_memory import MemoryManager
+
+class ResuRankAgent:
+    """Autonomous AI Agent similar to Manus AI
+    
+    This agent can:
+    1. Process user queries and generate responses
+    2. Perform reasoning through chain-of-thought
+    3. Execute tasks based on user instructions
+    4. Maintain conversation context
+    """
+    
+    def __init__(self, model_id="distilgpt2", use_cache=True, test_mode=False):
+        """Initialize the ResuRank Agent
+        
+        Args:
+            model_id: Hugging Face model ID to use for the agent
+            use_cache: Whether to use cached models from Hugging Face Hub
+            test_mode: Whether to run in test mode with minimal resources
+        """
+        self.model_id = model_id
+        self.test_mode = test_mode
+        
+        # Use CPU for test mode, otherwise check for CUDA
+        if test_mode:
+            self.device = "cpu"
+            print("Running in test mode on CPU with minimal resources")
+        else:
+            self.device = "cuda" if torch.cuda.is_available() else "cpu"
+            print(f"Using device: {self.device}")
+        
+        # Load model and tokenizer from Hugging Face Hub
+        print(f"Loading model {model_id} from Hugging Face Hub...")
+        try:
+            # Configure model loading parameters based on mode
+            model_kwargs = {
+                "torch_dtype": torch.float32,  # Use float32 for better compatibility
+            }
+            
+            # Check if Accelerate is available for low_cpu_mem_usage and device_map
+            try:
+                import accelerate
+                model_kwargs["low_cpu_mem_usage"] = True
+                # Add device map only if not in test mode
+                if not test_mode:
+                    model_kwargs["device_map"] = "auto"
+                    if self.device == "cuda":
+                        model_kwargs["torch_dtype"] = torch.float16
+            except ImportError:
+                print("Accelerate library not found, disabling low_cpu_mem_usage and device_map")
+                # Continue without these options
+            
+            # Add cache directory if using cache
+            if use_cache:
+                model_kwargs["cache_dir"] = "./.cache"
+                print("Using cached models if available...")
+                self.tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir="./.cache")
+            else:
+                print("Downloading models from Hugging Face Hub...")
+                self.tokenizer = AutoTokenizer.from_pretrained(model_id)
+            
+            # Load the model with optimized parameters
+            self.model = AutoModelForCausalLM.from_pretrained(model_id, **model_kwargs)
+            
+            print(f"Successfully loaded model {model_id}")
+        except Exception as e:
+            print(f"Error loading model: {str(e)}")
+            print("Falling back to smaller model...")
+            fallback_model = "distilgpt2"  # Use a smaller model as fallback
+            self.model_id = fallback_model
+            
+            try:
+                # Try loading the fallback model with minimal parameters
+                self.tokenizer = AutoTokenizer.from_pretrained(fallback_model, cache_dir="./.cache")
+                self.model = AutoModelForCausalLM.from_pretrained(
+                    fallback_model,
+                    torch_dtype=torch.float32,
+                    low_cpu_mem_usage=True,
+                    cache_dir="./.cache"
+                )
+                print(f"Successfully loaded fallback model {fallback_model}")
+            except Exception as fallback_error:
+                print(f"Error loading fallback model: {str(fallback_error)}")
+                raise RuntimeError("Failed to load both primary and fallback models")
+        
+        # Initialize agent components
+        self.reasoning_engine = ReasoningEngine(self.model, self.tokenizer, self.device)
+        self.memory_manager = MemoryManager(max_history_length=20)
+        self.task_executor = TaskExecutor(self.reasoning_engine)
+        
+    def process_query(self, query: str, use_reasoning: bool = True) -> Dict[str, Any]:
+        """Process a user query and generate a response
+        
+        Args:
+            query: User query text
+            use_reasoning: Whether to use chain-of-thought reasoning
+            
+        Returns:
+            Dictionary containing response and metadata
+        """
+        # Add query to conversation history
+        self.memory_manager.add_message("user", query)
+        
+        start_time = time.time()
+        
+        # Check if this is a task execution request
+        is_task_request = self._is_task_request(query)
+        
+        # Process the query with appropriate method
+        if is_task_request:
+            # Handle as a task execution request
+            task_result = self.execute_task(query)
+            response = f"I've executed your task. {task_result.get('result', '')}\n\nStatus: {task_result.get('status', 'unknown')}"
+            reasoning = task_result.get('plan', '')
+        elif use_reasoning:
+            # Use chain-of-thought reasoning
+            # Enhance with context from memory
+            facts = self.memory_manager.format_facts_for_prompt()
+            context = self.memory_manager.format_conversation_for_prompt(max_turns=5)
+            
+            # Create an enhanced query with context
+            enhanced_query = f"{facts}\n\nRecent conversation:\n{context}\n\nCurrent query: {query}"
+            
+            result = self.reasoning_engine.chain_of_thought(enhanced_query)
+            response = result["answer"]
+            reasoning = result["reasoning"]
+        else:
+            # Simple response generation without reasoning
+            conversation_prompt = self.memory_manager.format_conversation_for_prompt(max_turns=10)
+            facts_prompt = self.memory_manager.format_facts_for_prompt()
+            
+            prompt = f"{facts_prompt}\n\n{conversation_prompt}\nassistant: "
+            
+            response = self.reasoning_engine.generate_text(prompt)
+            reasoning = None
+        
+        # Add response to conversation history
+        self.memory_manager.add_message("assistant", response)
+        
+        # Extract any important facts from the conversation
+        self._extract_facts(query, response)
+        
+        processing_time = time.time() - start_time
+        
+        return {
+            "response": response,
+            "reasoning": reasoning,
+            "processing_time": processing_time,
+            "timestamp": time.time()
+        }
+        
+    def _is_task_request(self, query: str) -> bool:
+        """Determine if a query is a task execution request
+        
+        Args:
+            query: The user query
+            
+        Returns:
+            True if the query appears to be a task request, False otherwise
+        """
+        # Keywords that suggest a task execution request
+        task_keywords = [
+            "execute", "perform", "run", "do", "complete", "finish", 
+            "task", "job", "work", "action", "operation", "function",
+            "can you", "please", "help me", "i need", "i want"
+        ]
+        
+        # Check if query contains task-related keywords
+        query_lower = query.lower()
+        for keyword in task_keywords:
+            if keyword in query_lower:
+                return True
+                
+        return False
+    
+    def _extract_facts(self, query: str, response: str) -> None:
+        """Extract important facts from the conversation
+        
+        Args:
+            query: User query
+            response: Agent response
+        """
+        # Extract personal information
+        self._extract_personal_info(query)
+        
+        # Extract preferences
+        self._extract_preferences(query)
+        
+        # Extract task-related information
+        self._extract_task_info(query)
+        
+        # Use the reasoning engine to identify important facts
+        self._extract_with_reasoning(query, response)
+    
+    def _extract_personal_info(self, text: str) -> None:
+        """Extract personal information from text
+        
+        Args:
+            text: Text to extract information from
+        """
+        text_lower = text.lower()
+        
+        # Extract name
+        if "my name is" in text_lower or "i am called" in text_lower or "i'm called" in text_lower:
+            name_patterns = [
+                r"my name is ([\w\s]+)[.\,]?",
+                r"i am called ([\w\s]+)[.\,]?",
+                r"i'm called ([\w\s]+)[.\,]?"
+            ]
+            
+            for pattern in name_patterns:
+                name_match = re.search(pattern, text_lower)
+                if name_match:
+                    name = name_match.group(1).strip()
+                    self.memory_manager.add_important_fact(f"User's name is {name}", "user")
+                    break
+        
+        # Extract location
+        if "i am from" in text_lower or "i'm from" in text_lower or "i live in" in text_lower:
+            location_patterns = [
+                r"i am from ([\w\s]+)[.\,]?",
+                r"i'm from ([\w\s]+)[.\,]?",
+                r"i live in ([\w\s]+)[.\,]?"
+            ]
+            
+            for pattern in location_patterns:
+                location_match = re.search(pattern, text_lower)
+                if location_match:
+                    location = location_match.group(1).strip()
+                    self.memory_manager.add_important_fact(f"User is from {location}", "user")
+                    break
+        
+        # Extract profession/occupation
+        if "i work as" in text_lower or "i am a" in text_lower or "i'm a" in text_lower:
+            profession_patterns = [
+                r"i work as a[n]? ([\w\s]+)[.\,]?",
+                r"i am a[n]? ([\w\s]+)[.\,]?",
+                r"i'm a[n]? ([\w\s]+)[.\,]?"
+            ]
+            
+            for pattern in profession_patterns:
+                profession_match = re.search(pattern, text_lower)
+                if profession_match:
+                    profession = profession_match.group(1).strip()
+                    self.memory_manager.add_important_fact(f"User works as a {profession}", "user")
+                    break
+    
+    def _extract_preferences(self, text: str) -> None:
+        """Extract user preferences from text
+        
+        Args:
+            text: Text to extract information from
+        """
+        text_lower = text.lower()
+        
+        # Extract likes
+        if "i like" in text_lower or "i love" in text_lower or "i enjoy" in text_lower:
+            like_patterns = [
+                r"i like ([\w\s]+)[.\,]?",
+                r"i love ([\w\s]+)[.\,]?",
+                r"i enjoy ([\w\s]+)[.\,]?"
+            ]
+            
+            for pattern in like_patterns:
+                like_match = re.search(pattern, text_lower)
+                if like_match:
+                    like = like_match.group(1).strip()
+                    self.memory_manager.add_important_fact(f"User likes {like}", "user")
+                    break
+        
+        # Extract dislikes
+        if "i don't like" in text_lower or "i hate" in text_lower or "i dislike" in text_lower:
+            dislike_patterns = [
+                r"i don't like ([\w\s]+)[.\,]?",
+                r"i hate ([\w\s]+)[.\,]?",
+                r"i dislike ([\w\s]+)[.\,]?"
+            ]
+            
+            for pattern in dislike_patterns:
+                dislike_match = re.search(pattern, text_lower)
+                if dislike_match:
+                    dislike = dislike_match.group(1).strip()
+                    self.memory_manager.add_important_fact(f"User dislikes {dislike}", "user")
+                    break
+    
+    def _extract_task_info(self, text: str) -> None:
+        """Extract task-related information from text
+        
+        Args:
+            text: Text to extract information from
+        """
+        text_lower = text.lower()
+        
+        # Extract goals
+        if "my goal is" in text_lower or "i want to" in text_lower or "i need to" in text_lower:
+            goal_patterns = [
+                r"my goal is to ([\w\s]+)[.\,]?",
+                r"i want to ([\w\s]+)[.\,]?",
+                r"i need to ([\w\s]+)[.\,]?"
+            ]
+            
+            for pattern in goal_patterns:
+                goal_match = re.search(pattern, text_lower)
+                if goal_match:
+                    goal = goal_match.group(1).strip()
+                    self.memory_manager.add_important_fact(f"User's goal is to {goal}", "user")
+                    break
+    
+    def run_test_case(self) -> Dict[str, Any]:
+        """Run a test case to demonstrate the agent's capabilities with minimal resources
+        
+        This method is useful for testing the agent on resource-constrained environments
+        like Hugging Face Spaces or during development.
+        
+        Returns:
+            Dictionary containing test results and performance metrics
+        """
+        print("Running test case with minimal resources...")
+        start_time = time.time()
+        
+        # Simple test query that doesn't require extensive reasoning
+        test_query = "What can you help me with?"
+        
+        # Process the query with minimal settings
+        test_response = self.process_query(test_query, use_reasoning=False)
+        
+        # Calculate performance metrics
+        processing_time = time.time() - start_time
+        memory_usage = self._estimate_memory_usage()
+        
+        # Return test results
+        return {
+            "status": "success",
+            "model_id": self.model_id,
+            "device": self.device,
+            "test_query": test_query,
+            "test_response": test_response["response"],
+            "processing_time": processing_time,
+            "memory_usage_mb": memory_usage,
+            "timestamp": time.time()
+        }
+    
+    def _estimate_memory_usage(self) -> float:
+        """Estimate the memory usage of the model
+        
+        Returns:
+            Estimated memory usage in MB
+        """
+        try:
+            import psutil
+            process = psutil.Process(os.getpid())
+            memory_info = process.memory_info()
+            return memory_info.rss / (1024 * 1024)  # Convert to MB
+        except ImportError:
+            return 0.0  # Return 0 if psutil is not available
+    
+    def _extract_with_reasoning(self, query: str, response: str) -> None:
+        """Use the reasoning engine to extract important facts
+        
+        Args:
+            query: User query
+            response: Agent response
+        """
+        # Only use this for longer queries to avoid unnecessary processing
+        if len(query) < 50:
+            return
+        
+        extraction_prompt = f"""Extract important facts from this conversation:
+
+User: {query}
+Assistant: {response}
+
+List of important facts (one per line):
+1. """
+        
+        try:
+            facts_text = self.reasoning_engine.generate_text(extraction_prompt, max_length=256)
+            
+            # Parse the facts
+            for line in facts_text.split('\n'):
+                line = line.strip()
+                if line and (line[0].isdigit() or line.startswith('- ')):
+                    # Remove numbering or bullet points
+                    fact = re.sub(r'^\d+\.\s*|^-\s*', '', line).strip()
+                    if fact and len(fact) > 10:  # Only add substantial facts
+                        self.memory_manager.add_important_fact(fact, "inference")
+        except Exception as e:
+            print(f"Error extracting facts with reasoning: {str(e)}")
+            # Continue without adding facts
+    
+
+    
+    def execute_task(self, task_description: str) -> Dict[str, Any]:
+        """Execute a task based on the description
+        
+        Args:
+            task_description: Description of the task to execute
+            
+        Returns:
+            Dictionary containing task results and status
+        """
+        return self.task_executor.execute_task(task_description)
+    
+    def get_status(self) -> Dict[str, Any]:
+        """Get the current status of the agent
+        
+        Returns:
+            Dictionary containing agent status information
+        """
+        memory_stats = self.memory_manager.get_memory_stats()
+        task_status = self.task_executor.get_task_status()
+        
+        return {
+            "model_id": self.model_id,
+            "device": self.device,
+            "conversation_turns": memory_stats["conversation_turns"],
+            "important_facts": memory_stats["important_facts"],
+            "current_task": task_status["current_task"],
+            "task_status": task_status["status"]
+        }
+    
+    def clear_conversation(self) -> None:
+        """Clear the conversation history"""
+        self.memory_manager.clear_conversation_history()
+        
+    def process_document(self, document_text: str, document_type: str = "resume") -> Dict[str, Any]:
+        """Process a document (like a resume) and extract information
+        
+        Args:
+            document_text: The text content of the document
+            document_type: The type of document (e.g., "resume", "job_description")
+            
+        Returns:
+            Dictionary containing extracted information and analysis
+        """
+        self.memory_manager.store_session_data(f"last_{document_type}", document_text)
+        start_time = time.time()
+        
+        # Create a prompt for document analysis
+        analysis_prompt = f"""I need to analyze this {document_type} document and extract key information:
+
+{document_text}
+
+Detailed analysis:"""
+        
+        # Generate analysis using reasoning engine
+        analysis = self.reasoning_engine.generate_text(analysis_prompt, max_length=1024)
+        
+        # Extract structured information based on document type
+        if document_type.lower() == "resume":
+            extraction_prompt = f"""Based on this resume:
+{document_text}
+
+Extract the following information in a structured format:
+1. Name:
+2. Contact Information:
+3. Education:
+4. Work Experience:
+5. Skills:
+6. Projects:
+7. Certifications:
+8. Languages:
+9. Key Strengths:
+"""
+        elif document_type.lower() == "job_description":
+            extraction_prompt = f"""Based on this job description:
+{document_text}
+
+Extract the following information in a structured format:
+1. Job Title:
+2. Company:
+3. Location:
+4. Required Skills:
+5. Required Experience:
+6. Education Requirements:
+7. Responsibilities:
+8. Benefits:
+9. Key Qualifications:
+"""
+        else:
+            extraction_prompt = f"""Extract key information from this document:
+{document_text}
+
+Key information:
+1. """
+        
+        # Generate structured extraction
+        structured_info = self.reasoning_engine.generate_text(extraction_prompt, max_length=1024)
+        
+        # Add important facts to memory
+        self._extract_document_facts(document_text, document_type, structured_info)
+        
+        processing_time = time.time() - start_time
+        
+        return {
+            "document_type": document_type,
+            "analysis": analysis,
+            "structured_info": structured_info,
+            "processing_time": processing_time,
+            "timestamp": time.time()
+        }
+    
+    def _extract_document_facts(self, document_text: str, document_type: str, structured_info: str) -> None:
+        """Extract important facts from a document and add them to memory
+        
+        Args:
+            document_text: The text content of the document
+            document_type: The type of document
+            structured_info: Structured information extracted from the document
+        """
+        # Extract key facts based on document type
+        if document_type.lower() == "resume":
+            # Extract name if present
+            name_match = re.search(r"Name:\s*([\w\s]+)\n", structured_info)
+            if name_match:
+                name = name_match.group(1).strip()
+                self.memory_manager.add_important_fact(f"Document contains resume for {name}", "document")
+            
+            # Extract skills
+            skills_match = re.search(r"Skills:\s*([\w\s,\.\-\+]+)\n", structured_info)
+            if skills_match:
+                skills = skills_match.group(1).strip()
+                self.memory_manager.add_important_fact(f"Resume shows skills in: {skills}", "document")
+            
+            # Extract education
+            education_match = re.search(r"Education:\s*([\w\s,\.\-\+]+)\n", structured_info)
+            if education_match:
+                education = education_match.group(1).strip()
+                self.memory_manager.add_important_fact(f"Resume shows education: {education}", "document")
+        
+        elif document_type.lower() == "job_description":
+            # Extract job title
+            title_match = re.search(r"Job Title:\s*([\w\s]+)\n", structured_info)
+            if title_match:
+                title = title_match.group(1).strip()
+                self.memory_manager.add_important_fact(f"Document contains job description for {title}", "document")
+            
+            # Extract required skills
+            skills_match = re.search(r"Required Skills:\s*([\w\s,\.\-\+]+)\n", structured_info)
+            if skills_match:
+                skills = skills_match.group(1).strip()
+                self.memory_manager.add_important_fact(f"Job requires skills in: {skills}", "document")
+        
+        # Add general document fact
+        self.memory_manager.add_important_fact(f"Processed a {document_type} document", "system")
+    
+    def rank_resumes(self, job_description: str, resumes: List[str]) -> Dict[str, Any]:
+        """Rank multiple resumes against a job description
+        
+        Args:
+            job_description: The job description text
+            resumes: List of resume texts to rank
+            
+        Returns:
+            Dictionary containing rankings and analysis
+        """
+        start_time = time.time()
+        
+        # Process the job description first
+        job_result = self.process_document(job_description, "job_description")
+        job_analysis = job_result["structured_info"]
+        
+        # Process each resume
+        resume_results = []
+        for i, resume in enumerate(resumes):
+            result = self.process_document(resume, "resume")
+            resume_results.append({
+                "index": i,
+                "text": resume,
+                "analysis": result["structured_info"]
+            })
+        
+        # Create a ranking prompt
+        ranking_prompt = f"""I need to rank these resumes based on how well they match the job description.
+
+Job Description Analysis:
+{job_analysis}
+
+Resumes:
+"""
+        
+        for i, result in enumerate(resume_results):
+            ranking_prompt += f"\nResume {i+1}:\n{result['analysis']}\n"
+        
+        ranking_prompt += "\nRank these resumes from best to worst match for the job, with detailed reasoning for each:"
+        
+        # Generate the ranking analysis
+        ranking_analysis = self.reasoning_engine.generate_text(ranking_prompt, max_length=2048)
+        
+        # Generate a numerical scoring for each resume
+        scoring_prompt = f"""Based on my analysis of how well these resumes match the job description:
+{ranking_analysis}
+
+Assign a numerical score from 0-100 for each resume, where 100 is a perfect match:
+
+Resume 1 Score:"""
+        
+        scores_text = self.reasoning_engine.generate_text(scoring_prompt, max_length=512)
+        
+        # Parse scores (simple regex approach)
+        scores = []
+        for i in range(len(resume_results)):
+            score_match = re.search(fr"Resume {i+1} Score:\s*(\d+)", scores_text)
+            if score_match:
+                scores.append(int(score_match.group(1)))
+            else:
+                # Default score if parsing fails
+                scores.append(50)
+        
+        # Create the final rankings
+        rankings = []
+        for i, score in enumerate(scores):
+            rankings.append({
+                "resume_index": i,
+                "score": score,
+                "resume_text": resumes[i][:100] + "..."  # Truncated for readability
+            })
+        
+        # Sort by score (descending)
+        rankings.sort(key=lambda x: x["score"], reverse=True)
+        
+        processing_time = time.time() - start_time
+        
+        return {
+            "rankings": rankings,
+            "analysis": ranking_analysis,
+            "job_description": job_description,
+            "processing_time": processing_time
+        }
+
+# Create the Gradio interface
+def create_interface(test_mode=False):
+    """Create the Gradio interface for the ResuRank AI Agent
+    
+    Args:
+        test_mode: Whether to run in test mode with minimal resources
+    """
+    # Initialize the agent with appropriate settings
+    if test_mode:
+        agent = ResuRankAgent(model_id="distilgpt2", use_cache=True, test_mode=True)
+        # Run a test case to verify functionality
+        test_results = agent.run_test_case()
+        print(f"Test results: {test_results}")
+    else:
+        agent = ResuRankAgent(model_id="google/flan-t5-base", use_cache=True)
+    
+    with gr.Blocks(title="ResuRank AI Agent") as interface:
+        gr.Markdown("# ResuRank AI Agent")
+        gr.Markdown("An autonomous AI agent that can process queries, perform reasoning, and execute tasks.")
+        
+        with gr.Tab("Chat"):
+            chatbot = gr.Chatbot(height=400)
+            msg = gr.Textbox(label="Your message", placeholder="Ask me anything...")
+            with gr.Row():
+                submit_btn = gr.Button("Submit")
+                clear_btn = gr.Button("Clear")
+            
+            reasoning_checkbox = gr.Checkbox(label="Use reasoning", value=True)
+            
+            if reasoning_checkbox.value:
+                reasoning_output = gr.Textbox(label="Reasoning", interactive=False)
+            else:
+                reasoning_output = gr.Textbox(label="Reasoning", interactive=False, visible=False)
+            
+            def respond(message, chat_history, use_reasoning):
+                if not message.strip():
+                    return chat_history, "", ""
+                
+                # Process the query
+                result = agent.process_query(message, use_reasoning=use_reasoning)
+                
+                # Update chat history
+                chat_history.append((message, result["response"]))
+                
+                return chat_history, "", result.get("reasoning", "")
+            
+            def clear_chat():
+                agent.clear_conversation()
+                return [], "", ""
+            
+            # Set up event handlers
+            submit_btn.click(respond, [msg, chatbot, reasoning_checkbox], [chatbot, msg, reasoning_output])
+            msg.submit(respond, [msg, chatbot, reasoning_checkbox], [chatbot, msg, reasoning_output])
+            clear_btn.click(clear_chat, None, [chatbot, msg, reasoning_output])
+            reasoning_checkbox.change(lambda x: gr.update(visible=x), reasoning_checkbox, reasoning_output)
+        
+        with gr.Tab("Task Execution"):
+            task_input = gr.Textbox(label="Task Description", placeholder="Describe the task to execute...")
+            execute_btn = gr.Button("Execute Task")
+            
+            with gr.Row():
+                with gr.Column():
+                    plan_output = gr.Textbox(label="Execution Plan", interactive=False)
+                with gr.Column():
+                    results_output = gr.Textbox(label="Task Results", interactive=False)
+            
+            task_status = gr.Textbox(label="Task Status", value="idle", interactive=False)
+            
+            def execute_task(task_description):
+                if not task_description.strip():
+                    return "No task provided.", "", "idle"
+                
+                # Execute the task
+                result = agent.execute_task(task_description)
+                
+                return result.get("plan", ""), result.get("result", ""), result.get("status", "")
+            
+            # Set up event handlers
+            execute_btn.click(execute_task, task_input, [plan_output, results_output, task_status])
+        
+        with gr.Tab("Agent Status"):
+            status_btn = gr.Button("Refresh Status")
+            
+            with gr.Row():
+                with gr.Column():
+                    model_info = gr.Textbox(label="Model Information", interactive=False)
+                with gr.Column():
+                    conversation_info = gr.Textbox(label="Conversation Information", interactive=False)
+            
+            def update_status():
+                status = agent.get_status()
+                model_text = f"Model ID: {status['model_id']}\nDevice: {status['device']}"
+                
+                # Handle important_facts which might be an integer count or a list
+                important_facts_count = status['important_facts']
+                if isinstance(important_facts_count, list):
+                    important_facts_count = len(important_facts_count)
+                    
+                conversation_text = f"Conversation Length: {status['conversation_turns']} turns\nImportant Facts: {important_facts_count}\nCurrent Task: {status['current_task'] or 'None'}\nTask Status: {status['task_status']}"
+                
+                return model_text, conversation_text
+            
+            # Set up event handlers
+            status_btn.click(update_status, None, [model_info, conversation_info])
+            
+            # Initialize status on load
+            model_info.value, conversation_info.value = update_status()
+    
+        with gr.Tab("Document Processing"):
+            with gr.Row():
+                with gr.Column():
+                    document_input = gr.Textbox(label="Document Text", placeholder="Paste resume or job description text here...", lines=10)
+                    document_type = gr.Radio(["resume", "job_description", "other"], label="Document Type", value="resume")
+                    process_btn = gr.Button("Process Document")
+                
+            with gr.Row():
+                with gr.Column():
+                    analysis_output = gr.Textbox(label="Document Analysis", interactive=False, lines=10)
+                with gr.Column():
+                    structured_output = gr.Textbox(label="Structured Information", interactive=False, lines=10)
+            
+            def process_document(document_text, doc_type):
+                if not document_text.strip():
+                    return "No document provided.", ""
+                
+                # Process the document
+                result = agent.process_document(document_text, doc_type)
+                
+                return result.get("analysis", ""), result.get("structured_info", "")
+            
+            # Set up event handlers
+            process_btn.click(process_document, [document_input, document_type], [analysis_output, structured_output])
+        
+        with gr.Tab("Resume Ranking"):
+            with gr.Row():
+                with gr.Column():
+                    job_description_input = gr.Textbox(label="Job Description", placeholder="Paste job description here...", lines=8)
+            
+            with gr.Row():
+                with gr.Column():
+                    resume1_input = gr.Textbox(label="Resume 1", placeholder="Paste first resume here...", lines=6)
+                with gr.Column():
+                    resume2_input = gr.Textbox(label="Resume 2", placeholder="Paste second resume here...", lines=6)
+            
+            with gr.Row():
+                with gr.Column():
+                    resume3_input = gr.Textbox(label="Resume 3 (Optional)", placeholder="Paste third resume here...", lines=6)
+                with gr.Column():
+                    resume4_input = gr.Textbox(label="Resume 4 (Optional)", placeholder="Paste fourth resume here...", lines=6)
+            
+            rank_btn = gr.Button("Rank Resumes")
+            
+            ranking_output = gr.Textbox(label="Ranking Results", interactive=False, lines=15)
+            
+            def rank_resumes(job_desc, resume1, resume2, resume3, resume4):
+                if not job_desc.strip() or not resume1.strip() or not resume2.strip():
+                    return "Please provide at least a job description and two resumes."
+                
+                # Collect all non-empty resumes
+                resumes = [r for r in [resume1, resume2, resume3, resume4] if r.strip()]
+                
+                # Rank the resumes
+                result = agent.rank_resumes(job_desc, resumes)
+                
+                # Format the results
+                output = "Resume Rankings (Best to Worst Match):\n\n"
+                
+                for i, rank in enumerate(result["rankings"]):
+                    resume_num = rank["resume_index"] + 1
+                    score = rank["score"]
+                    output += f"{i+1}. Resume {resume_num} - Score: {score}/100\n"
+                
+                output += "\nDetailed Analysis:\n" + result["analysis"]
+                
+                return output
+            
+            # Set up event handlers
+            rank_btn.click(rank_resumes, [job_description_input, resume1_input, resume2_input, resume3_input, resume4_input], ranking_output)
+    
+    return interface
+
+# Launch the interface when run directly
+if __name__ == "__main__":
+    import argparse
+    
+    # Parse command line arguments
+    parser = argparse.ArgumentParser(description="ResuRank AI Agent")
+    parser.add_argument("--test", action="store_true", help="Run in test mode with minimal resources")
+    parser.add_argument("--share", action="store_true", help="Share the Gradio interface")
+    args = parser.parse_args()
+    
+    # Create and launch the interface
+    interface = create_interface(test_mode=args.test)
+    interface.launch(share=args.share)