Upload app.py

app.py

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+import gradio as gr
+from gtts import gTTS
+import tempfile
+import os
+import pandas as pd
+import numpy as np
+import faiss
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+import whisper
+from groq import Groq
+
+# Load data and models
+questions = pd.read_csv('non_technical_interview_questions.csv', encoding='unicode_escape')[['Question']]
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+question_embeddings = np.vstack([model.encode(q) for q in questions['Question']])
+dimension = question_embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(question_embeddings)
+generation_model = pipeline("text2text-generation", model="google/flan-t5-large", num_beams=1)
+client = Groq(api_key="GROQ_API_KEY")
+
+# Helper functions
+def get_questions(job_title, job_description, top_k=5):
+    text = f'{job_title}: {job_description}'
+    text_embedding = model.encode(text).reshape(1, -1)
+    _, indices = index.search(text_embedding, top_k)
+    similar_questions = questions.iloc[indices[0]]
+    return similar_questions['Question'].tolist()
+
+def text_to_speech(question_text):
+    tts = gTTS(text=question_text, lang='en')
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
+    tts.save(temp_file.name)
+    return temp_file.name
+
+def audio_to_text(audio_file):
+    whisper_model = whisper.load_model("base")
+    result = whisper_model.transcribe(audio_file)
+    return result['text']
+
+def generate_feedback_from_llm(question, user_answer):
+    response = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": (
+                    "You are an experienced interview coach specializing in preparing candidates for real-world job interviews. "
+                    "Your goal is to provide concise, actionable feedback that helps the user improve their answers quickly. "
+                    "Please focus on the following points: \n"
+                    "1. Strengths: Highlight key strengths in the candidate's answer. Focus on one or two positive aspects (e.g., problem-solving, clear communication). \n"
+                    "2. Areas for Improvement: Mention one or two quick improvements, like being more concise, adding specific examples, or avoiding jargon. \n"
+                    "3. What a Best Answer Looks Like: Provide a brief example of an ideal answer that addresses the same question with clarity and impact. \n"
+                    "4. English Proficiency: Check for major grammar or sentence structure issues and provide quick tips for improvement. \n"
+                    "5. Interview Readines: In one sentence, assess if the answer is ready for a real interview or if it needs a little more refinement. \n"
+                    "6. Quick Tips: Offer practical, quick tips on how to improve the candidate’s overall interview performance. These could include advice on body language, confidence, tone, or other interview techniques.\n"
+                    "Keep your feedback clear and to the point, focusing on the most impactful changes the user can make to improve their interview performance."
+                    "Your feedback should always be respectful, professional, and constructive, focused on preparing the candidate to perform confidently and concisely in real-world job interviews."
+                )
+            },
+            {"role": "user", "content": f"Question: {question}\nAnswer: {user_answer}\n\nProvide feedback on the quality of the answer, noting strengths and suggestions for improvement."}
+        ],
+        model="llama3-8b-8192",
+    )
+    return response.choices[0].message.content
+
+# Gradio app logic
+def start_interview(job_title, job_description):
+    question_queue = get_questions(job_title, job_description)
+    current_question_index = 0
+    question_audio = text_to_speech(question_queue[current_question_index])
+    return question_queue, current_question_index, question_audio
+
+def next_question(question_queue, current_question_index):
+    current_question_index += 1
+    if current_question_index < len(question_queue):
+        question_audio = text_to_speech(question_queue[current_question_index])
+    else:
+        question_audio = None
+    return current_question_index, question_audio
+
+def transcribe_and_feedback(answer_audio, question_audio):
+    question_text = audio_to_text(question_audio)
+    user_answer = audio_to_text(answer_audio)
+    feedback = generate_feedback_from_llm(question_text, user_answer)
+    return user_answer, feedback
+
+# Gradio UI components
+with gr.Blocks() as demo:
+    gr.Markdown("### Job Interview Practice App")
+    
+    with gr.Row():
+        job_title = gr.Textbox(label="Job Title", placeholder="e.g., Data Scientist")
+        job_description = gr.Textbox(label="Job Description", lines=5, placeholder="Describe the role requirements.")
+        start_button = gr.Button("Start Interview")
+    
+    with gr.Row():
+        question_audio = gr.Audio(label="Question", type="filepath", interactive=False)
+        next_button = gr.Button("Next Question")
+
+    with gr.Row():
+        answer_audio = gr.Audio(label="Your Answer", type="filepath")
+    
+    with gr.Row():
+        response_text = gr.Textbox(label="Transcription of Your Answer", interactive=False)
+        feedback_text = gr.Textbox(label="Feedback", interactive=False)
+    
+    # Define workflow logic
+    question_queue = gr.State()
+    current_question_index = gr.State()
+
+    start_button.click(start_interview, [job_title, job_description], [question_queue, current_question_index, question_audio])
+    next_button.click(next_question, [question_queue, current_question_index], [current_question_index, question_audio])
+    
+    # Answer transcription and feedback generation
+    answer_audio.change(transcribe_and_feedback, [answer_audio, question_audio], [response_text, feedback_text])
+
+demo.launch()