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import gradio as gr
import os
import logging
from typing import List, Dict, Tuple
from analyzer import combine_repo_files_for_llm, handle_load_repository
from hf_utils import download_filtered_space_files
# Setup logger
logger = logging.getLogger(__name__)
def create_repo_explorer_tab() -> Tuple[Dict[str, gr.components.Component], Dict[str, gr.State]]:
"""
Creates the Repo Explorer tab content and returns the component references and state variables.
"""
# State variables for repo explorer
states = {
"repo_context_summary": gr.State(""),
"current_repo_id": gr.State("")
}
gr.Markdown("### 🗂️ Deep Dive into a Specific Repository")
with gr.Row():
with gr.Column(scale=2):
repo_explorer_input = gr.Textbox(
label="📁 Repository ID",
placeholder="microsoft/DialoGPT-medium",
info="Enter a Hugging Face repository ID to explore"
)
with gr.Column(scale=1):
load_repo_btn = gr.Button("🚀 Load Repository", variant="primary", size="lg")
with gr.Row():
repo_status_display = gr.Textbox(
label="📊 Repository Status",
interactive=False,
lines=3,
info="Current repository loading status and basic info"
)
with gr.Row():
with gr.Column(scale=2):
repo_chatbot = gr.Chatbot(
label="🤖 Repository Assistant",
height=400,
type="messages",
avatar_images=(
"https://cdn-icons-png.flaticon.com/512/149/149071.png",
"https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.png"
),
show_copy_button=True,
value=[] # Start empty - welcome message will appear only after repo is loaded
)
with gr.Row():
repo_msg_input = gr.Textbox(
label="💭 Ask about this repository",
placeholder="What does this repository do? How do I use it?",
lines=1,
scale=4,
info="Ask anything about the loaded repository"
)
repo_send_btn = gr.Button("📤 Send", variant="primary", scale=1)
# with gr.Column(scale=1):
# # Repository content preview
# repo_content_display = gr.Textbox(
# label="📄 Repository Content Preview",
# lines=20,
# show_copy_button=True,
# interactive=False,
# info="Overview of the loaded repository structure and content"
# )
# Component references
components = {
"repo_explorer_input": repo_explorer_input,
"load_repo_btn": load_repo_btn,
"repo_status_display": repo_status_display,
"repo_chatbot": repo_chatbot,
"repo_msg_input": repo_msg_input,
"repo_send_btn": repo_send_btn,
# "repo_content_display": repo_content_display
}
return components, states
def handle_repo_user_message(user_message: str, history: List[Dict[str, str]], repo_context_summary: str, repo_id: str) -> Tuple[List[Dict[str, str]], str]:
"""Handle user messages in the repo-specific chatbot."""
if not repo_context_summary.strip():
return history, ""
# Initialize with repository-specific welcome message if empty
if not history:
welcome_msg = f"Hello! I'm your assistant for the '{repo_id}' repository. I have analyzed all the files and created a comprehensive understanding of this repository. I'm ready to answer any questions about its functionality, usage, architecture, and more. What would you like to know?"
history = [{"role": "assistant", "content": welcome_msg}]
if user_message:
history.append({"role": "user", "content": user_message})
return history, ""
def handle_repo_bot_response(history: List[Dict[str, str]], repo_context_summary: str, repo_id: str) -> List[Dict[str, str]]:
"""Generate bot response for repo-specific questions using comprehensive context."""
if not history or history[-1]["role"] != "user" or not repo_context_summary.strip():
return history
user_message = history[-1]["content"]
# Create a specialized prompt using the comprehensive context summary
repo_system_prompt = f"""You are an expert assistant for the Hugging Face repository '{repo_id}'.
You have comprehensive knowledge about this repository based on detailed analysis of all its files and components.
Use the following comprehensive analysis to answer user questions accurately and helpfully:
{repo_context_summary}
Instructions:
- Answer questions clearly and conversationally about this specific repository
- Reference specific components, functions, or features when relevant
- Provide practical guidance on installation, usage, and implementation
- If asked about code details, refer to the analysis above
- Be helpful and informative while staying focused on this repository
- If something isn't covered in the analysis, acknowledge the limitation
Answer the user's question based on your comprehensive knowledge of this repository."""
try:
from openai import OpenAI
client = OpenAI(api_key=os.getenv("modal_api"))
client.base_url = os.getenv("base_url")
response = client.chat.completions.create(
model="Orion-zhen/Qwen2.5-Coder-7B-Instruct-AWQ",
messages=[
{"role": "system", "content": repo_system_prompt},
{"role": "user", "content": user_message}
],
max_tokens=1024,
temperature=0.7
)
bot_response = response.choices[0].message.content
history.append({"role": "assistant", "content": bot_response})
except Exception as e:
logger.error(f"Error generating repo bot response: {e}")
error_response = f"I apologize, but I encountered an error while processing your question: {e}"
history.append({"role": "assistant", "content": error_response})
return history
def initialize_repo_chatbot(repo_status: str, repo_id: str, repo_context_summary: str) -> List[Dict[str, str]]:
"""Initialize the repository chatbot with a welcome message after successful repo loading."""
# Only initialize if repository was loaded successfully
if repo_context_summary.strip() and "successfully" in repo_status.lower():
welcome_msg = f"👋 Welcome! I've successfully analyzed the **{repo_id}** repository.\n\n🧠 **I now have comprehensive knowledge of:**\n• All files and code structure\n• Key features and capabilities\n• Installation and usage instructions\n• Architecture and implementation details\n• Dependencies and requirements\n\n💬 **Ask me anything about this repository!** \nFor example:\n• \"What does this repository do?\"\n• \"How do I install and use it?\"\n• \"What are the main components?\"\n• \"Show me usage examples\"\n\nWhat would you like to know? 🤔"
return [{"role": "assistant", "content": welcome_msg}]
else:
# Keep chatbot empty if loading failed
return []
def setup_repo_explorer_events(components: Dict[str, gr.components.Component], states: Dict[str, gr.State]):
"""Setup event handlers for the repo explorer components."""
# Load repository event
components["load_repo_btn"].click(
fn=handle_load_repository,
inputs=[components["repo_explorer_input"]],
outputs=[components["repo_status_display"], states["repo_context_summary"]]
).then(
fn=lambda repo_id: repo_id,
inputs=[components["repo_explorer_input"]],
outputs=[states["current_repo_id"]]
).then(
fn=initialize_repo_chatbot,
inputs=[components["repo_status_display"], states["current_repo_id"], states["repo_context_summary"]],
outputs=[components["repo_chatbot"]]
)
# Chat message submission events
components["repo_msg_input"].submit(
fn=handle_repo_user_message,
inputs=[components["repo_msg_input"], components["repo_chatbot"], states["repo_context_summary"], states["current_repo_id"]],
outputs=[components["repo_chatbot"], components["repo_msg_input"]]
).then(
fn=handle_repo_bot_response,
inputs=[components["repo_chatbot"], states["repo_context_summary"], states["current_repo_id"]],
outputs=[components["repo_chatbot"]]
)
components["repo_send_btn"].click(
fn=handle_repo_user_message,
inputs=[components["repo_msg_input"], components["repo_chatbot"], states["repo_context_summary"], states["current_repo_id"]],
outputs=[components["repo_chatbot"], components["repo_msg_input"]]
).then(
fn=handle_repo_bot_response,
inputs=[components["repo_chatbot"], states["repo_context_summary"], states["current_repo_id"]],
outputs=[components["repo_chatbot"]]
) |