file removed

app.py

@@ -1,130 +0,0 @@
-from huggingface_hub import hf_hub_download
-import logging
-import sys
-import gradio as gr
-from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt
-from llama_index.llms import LlamaCPP
-from llama_index.llms.llama_utils import (
-    messages_to_prompt,
-    completion_to_prompt,
-)
-
-MODELS_PATH = "./models"
-
-mistral_model_path = hf_hub_download(
-    repo_id= "TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
-    filename="mistral-7b-instruct-v0.2.Q4_K_M.gguf",
-    resume_download=True,
-    cache_dir=MODELS_PATH,)
-
-
-llm = LlamaCPP(
-    # You can pass in the URL to a GGML model to download it automatically
-    # model_url=model_url,
-    # optionally, you can set the path to a pre-downloaded model instead of model_url
-    model_path=mistral_model_path,
-    temperature=0.1,
-    max_new_tokens=256,
-    # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
-    context_window=3900,
-    # kwargs to pass to __call__()
-    generate_kwargs={},
-    # kwargs to pass to __init__()
-    # set to at least 1 to use GPU
-    model_kwargs={"n_gpu_layers": -1},
-    # transform inputs into Llama2 format
-    messages_to_prompt=messages_to_prompt,
-    completion_to_prompt=completion_to_prompt,
-    verbose=True,
-)
-
-
-def model_initialization(model):
-    if(model !=""):
-       gr.Info("model downloading and configuration process has been started, please wait...")
-    MODELS_PATH = "./models"
-    repo_id=""
-    filename=""
-    if(model=="Llama-2-13B-chat"):
-      repo_id="TheBloke/Llama-2-13B-chat-GGUF"
-      filename="llama-2-13b-chat.Q4_K_M.gguf"
-    elif(model=="Mistral-7B-Instruct-v0.2") :
-      repo_id="TheBloke/Mistral-7B-Instruct-v0.2-GGUF"
-      filename="mistral-7b-instruct-v0.2.Q4_K_M.gguf"
-    elif(model=="zephyr-7B-beta"):
-      repo_id="TheBloke/zephyr-7B-beta-GGUF "
-      filename="zephyr-7b-beta.Q4_K_M.gguf"
-    elif(model=="vicuna-7B-v1.5"):
-      repo_id="TheBloke/vicuna-7B-v1.5-GGUF"
-      filename="vicuna-7b-v1.5.Q4_K_M.gguf"
-    elif(model=="Falcon-7B-Instruct"):
-      repo_id="TheBloke/Falcon-7B-Instruct-GGML"
-      filename="falcon-7b-instruct.ggccv1.q4_1.bin"
-    elif(model=="CodeLlama-7B"):
-      repo_id="TheBloke/CodeLlama-7B-GGUF"
-      filename="codellama-7b.Q4_K_M.gguf"
-    else:
-      gr.Warning("please select at least one model")
-
-
-    mistral_model_path = hf_hub_download(
-    repo_id= repo_id,
-    filename= filename,
-    resume_download=True,
-    cache_dir=MODELS_PATH,)
-
-    llm = LlamaCPP(
-    # You can pass in the URL to a GGML model to download it automatically
-    # model_url=model_url,
-    # optionally, you can set the path to a pre-downloaded model instead of model_url
-    model_path=mistral_model_path,
-    temperature=0.1,
-    max_new_tokens=256,
-    # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
-    context_window=3900,
-    # kwargs to pass to __call__()
-    generate_kwargs={},
-    # set to at least 1 to use GPU
-    model_kwargs={"n_gpu_layers": -1},
-    # transform inputs into Llama2 format
-    messages_to_prompt=messages_to_prompt,
-    completion_to_prompt=completion_to_prompt,
-    verbose=True,
-)
-    gr.Info("model has been configured and ready to chat")
-    return "model has been configured and ready to chat, your current model is "+model
-
-def predict(message, history):
-    messages = []
-    answer = []
-    response = llm.stream_complete(message)
-    for bot_response in response:
-        token = bot_response.delta
-        answer.append(token)
-        final_answer = " ".join(answer)
-        yield final_answer
-
-with gr.Blocks() as UI:
-
-         models=gr.Dropdown(["CodeLlama-7B","Llama-2-13B-chat","Falcon-7B-Instruct" "Mistral-7B-Instruct-v0.2", "zephyr-7B-beta",
-                       "vicuna-7B-v1.5"],value=["CodeLlama-7B","Llama-2-13B-chat","Falcon-7B-Instruct" "Mistral-7B-Instruct-v0.2", "zephyr-7B-beta",
-                       "vicuna-7B-v1.5"], label="please select at least one model", info="default model is Mistral-7B-Instruct-v0.2")
-         textInfo = gr.Textbox(value="current model is Mistral-7B-Instruct-v0.2",label="Model Status");
-          # Chatbot interface
-         chatUI= gr.ChatInterface(
-                            predict,
-                            title="Open Source LLM ChatBot",
-                            description="Ask any question",
-                            theme="soft",
-                            examples=["Hello", "are you LLM model?", "how can i finetune a pre-trained LLM model?","How can i build a chatbot using local open-souce LLM ?"],
-                            cache_examples=False,
-                            submit_btn="Send Message",
-                            retry_btn=None,
-                            undo_btn="Delete Previous",
-                            clear_btn="Clear",
-                        )
-
-         models.change(fn=model_initialization,inputs=[models],outputs=[textInfo])
-
-if __name__ == "__main__":
-    UI.launch(debug=True) # launch app