Delete app.py

app.py

@@ -1,225 +0,0 @@
-import re
-import json
-import base64
-import requests
-import torch
-import nest_asyncio
-from fastapi import HTTPException
-from pydantic import BaseModel
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-from sentence_transformers import SentenceTransformer, models
-import gradio as gr
-
-# Apply nest_asyncio to allow async operations in the notebook/Spaces
-nest_asyncio.apply()
-
-############################################
-# Configuration
-############################################
-
-# Replace with your actual tokens
-HF_TOKEN = "YOUR_HF_TOKEN"
-GITHUB_TOKEN = "YOUR_GITHUB_TOKEN"
-
-############################################
-# GitHub API Functions
-############################################
-
-def extract_repo_info(github_url: str):
-    pattern = r"github\.com/([^/]+)/([^/]+)"
-    match = re.search(pattern, github_url)
-    if match:
-        owner = match.group(1)
-        repo = match.group(2).replace('.git', '')
-        return owner, repo
-    else:
-        raise ValueError("Invalid GitHub URL provided.")
-
-def get_repo_metadata(owner: str, repo: str):
-    headers = {'Authorization': f'token {GITHUB_TOKEN}'}
-    repo_url = f"https://api.github.com/repos/{owner}/{repo}"
-    response = requests.get(repo_url, headers=headers)
-    return response.json()
-
-def get_repo_tree(owner: str, repo: str, branch: str):
-    headers = {'Authorization': f'token {GITHUB_TOKEN}'}
-    tree_url = f"https://api.github.com/repos/{owner}/{repo}/git/trees/{branch}?recursive=1"
-    response = requests.get(tree_url, headers=headers)
-    return response.json()
-
-def get_file_content(owner: str, repo: str, file_path: str):
-    headers = {'Authorization': f'token {GITHUB_TOKEN}'}
-    content_url = f"https://api.github.com/repos/{owner}/{repo}/contents/{file_path}"
-    response = requests.get(content_url, headers=headers)
-    data = response.json()
-    if 'content' in data:
-        return base64.b64decode(data['content']).decode('utf-8')
-    else:
-        return None
-
-############################################
-# Embedding Functions
-############################################
-
-def preprocess_text(text: str) -> str:
-    cleaned_text = text.strip()
-    cleaned_text = re.sub(r'\s+', ' ', cleaned_text)
-    return cleaned_text
-
-def load_embedding_model(model_name: str = 'huggingface/CodeBERTa-small-v1') -> SentenceTransformer:
-    transformer_model = models.Transformer(model_name)
-    pooling_model = models.Pooling(transformer_model.get_word_embedding_dimension(),
-                                   pooling_mode_mean_tokens=True)
-    model = SentenceTransformer(modules=[transformer_model, pooling_model])
-    return model
-
-def generate_embedding(text: str, model_name: str = 'huggingface/CodeBERTa-small-v1') -> list:
-    processed_text = preprocess_text(text)
-    model = load_embedding_model(model_name)
-    embedding = model.encode(processed_text)
-    return embedding
-
-############################################
-# LLM Integration Functions
-############################################
-
-def is_detailed_query(query: str) -> bool:
-    keywords = ["detail", "detailed", "thorough", "in depth", "comprehensive", "extensive"]
-    return any(keyword in query.lower() for keyword in keywords)
-
-def generate_prompt(query: str, context_snippets: list) -> str:
-    context = "\n\n".join(context_snippets)
-    if is_detailed_query(query):
-        instruction = "Provide an extremely detailed and thorough explanation of at least 500 words."
-    else:
-        instruction = "Answer concisely."
-    
-    prompt = (
-        f"Below is some context from a GitHub repository:\n\n"
-        f"{context}\n\n"
-        f"Based on the above, {instruction}\n{query}\n"
-        f"Answer:"
-    )
-    return prompt
-
-def get_llm_response(prompt: str, model_name: str = "meta-llama/Llama-2-7b-chat-hf", max_new_tokens: int = None) -> str:
-    if max_new_tokens is None:
-        max_new_tokens = 1024 if is_detailed_query(prompt) else 256
-
-    torch.cuda.empty_cache()
-    
-    # Load tokenizer and model with authentication using the 'token' parameter.
-    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, token=HF_TOKEN)
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name,
-        device_map="auto",
-        use_safetensors=False,
-        trust_remote_code=True,
-        torch_dtype=torch.float16,
-        token=HF_TOKEN
-    )
-    
-    text_gen = pipeline("text-generation", model=model, tokenizer=tokenizer)
-    outputs = text_gen(prompt, max_new_tokens=max_new_tokens, do_sample=True, temperature=0.7)
-    full_response = outputs[0]['generated_text']
-    
-    marker = "Answer:"
-    if marker in full_response:
-        answer = full_response.split(marker, 1)[1].strip()
-    else:
-        answer = full_response.strip()
-    
-    return answer
-
-############################################
-# Gradio Interface Functions
-############################################
-
-def load_repo_contents(github_url: str):
-    try:
-        owner, repo = extract_repo_info(github_url)
-    except Exception as e:
-        return f"Error: {str(e)}"
-    repo_data = get_repo_metadata(owner, repo)
-    default_branch = repo_data.get("default_branch", "main")
-    tree_data = get_repo_tree(owner, repo, default_branch)
-    if "tree" not in tree_data:
-        return "Error: Could not fetch repository tree."
-    file_list = [item["path"] for item in tree_data["tree"] if item["type"] == "blob"]
-    return file_list
-
-def get_file_content_for_choice(github_url: str, file_choice: int):
-    try:
-        owner, repo = extract_repo_info(github_url)
-    except Exception as e:
-        return str(e)
-    repo_data = get_repo_metadata(owner, repo)
-    default_branch = repo_data.get("default_branch", "main")
-    tree_data = get_repo_tree(owner, repo, default_branch)
-    if "tree" not in tree_data:
-        return "Error: Could not fetch repository tree."
-    file_list = [item["path"] for item in tree_data["tree"] if item["type"] == "blob"]
-    if file_choice < 1 or file_choice > len(file_list):
-        return "Error: Invalid file choice."
-    selected_file = file_list[file_choice - 1]
-    content = get_file_content(owner, repo, selected_file)
-    return content, selected_file
-
-def chat_with_file(github_url: str, file_choice: int, user_query: str):
-    result = get_file_content_for_choice(github_url, file_choice)
-    if isinstance(result, str):
-        return result  # Error message
-    file_content, selected_file = result
-    preprocessed = preprocess_text(file_content)
-    context_snippet = preprocessed[:1000]  # use first 1000 characters as context
-    prompt = generate_prompt(user_query, [context_snippet])
-    llm_response = get_llm_response(prompt)
-    return f"File: {selected_file}\n\nLLM Response:\n{llm_response}"
-
-############################################
-# Gradio Interface Setup
-############################################
-
-with gr.Blocks() as demo:
-    gr.Markdown("# RepoChat - Chat with Repository Files")
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### Repository Information")
-            github_url_input = gr.Textbox(label="GitHub Repository URL", placeholder="https://github.com/username/repository")
-            load_repo_btn = gr.Button("Load Repository Contents")
-            file_dropdown = gr.Dropdown(label="Select a File", interactive=True)
-            repo_content_output = gr.Textbox(label="File Content", interactive=False, lines=10)
-        with gr.Column(scale=2):
-            gr.Markdown("### Chat Interface")
-            chat_query_input = gr.Textbox(label="Your Query", placeholder="Type your query here")
-            chat_output = gr.Textbox(label="Chatbot Response", interactive=False, lines=10)
-            chat_btn = gr.Button("Send Query")
-    
-    # When clicking "Load Repository Contents", update file dropdown
-    def update_file_dropdown(github_url):
-        files = load_repo_contents(github_url)
-        return files
-    
-    load_repo_btn.click(fn=update_file_dropdown, inputs=[github_url_input], outputs=[file_dropdown])
-    
-    # When file selection changes, update file content display
-    def update_repo_content(github_url, file_choice):
-        if not file_choice:
-            return "No file selected."
-        try:
-            file_index = int(file_choice)
-        except:
-            file_index = 1
-        content, _ = get_file_content_for_choice(github_url, file_index)
-        return content
-    
-    file_dropdown.change(fn=update_repo_content, inputs=[github_url_input, file_dropdown], outputs=[repo_content_output])
-    
-    # When sending a chat query, process it
-    def process_chat(github_url, file_choice, chat_query):
-        return chat_with_file(github_url, int(file_choice), chat_query)
-    
-    chat_btn.click(fn=process_chat, inputs=[github_url_input, file_dropdown, chat_query_input], outputs=[chat_output])
-    
-demo.launch()