Upload app.py

app.py

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+import os
+import re
+import json
+from datetime import datetime
+from typing import List, Dict, Any, Optional, Literal
+
+from fastapi import FastAPI, Request, BackgroundTasks
+from fastapi.middleware.cors import CORSMiddleware
+import gradio as gr
+import uvicorn
+from pydantic import BaseModel
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Configuration
+WEBHOOK_SECRET = os.getenv("WEBHOOK_SECRET", "716f77a91d0415cd0e3ed9dc8d188fc9ee53b11a8661e161a86f669f598a8016")
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Simple storage for processed tag operations
+tag_operations_store: List[Dict[str, Any]] = []
+
+# Common ML tags that we recognize for auto-tagging
+RECOGNIZED_TAGS = {
+    "pytorch",
+    "tensorflow",
+    "jax",
+    "transformers",
+    "diffusers",
+    "text-generation",
+    "text-classification",
+    "question-answering",
+    "text-to-image",
+    "image-classification",
+    "object-detection",
+    "fill-mask",
+    "token-classification",
+    "translation",
+    "summarization",
+    "feature-extraction",
+    "sentence-similarity",
+    "zero-shot-classification",
+    "image-to-text",
+    "automatic-speech-recognition",
+    "audio-classification",
+    "voice-activity-detection",
+    "depth-estimation",
+    "image-segmentation",
+    "video-classification",
+    "reinforcement-learning",
+    "tabular-classification",
+    "tabular-regression",
+    "time-series-forecasting",
+    "graph-ml",
+    "robotics",
+    "computer-vision",
+    "nlp",
+    "cv",
+    "multimodal",
+}
+
+
+class WebhookEvent(BaseModel):
+    event: Dict[str, str]
+    comment: Dict[str, Any]
+    discussion: Dict[str, Any]
+    repo: Dict[str, str]
+
+
+app = FastAPI(title="HF Tagging Bot")
+app.add_middleware(CORSMiddleware, allow_origins=["*"])
+
+
+def extract_tags_from_text(text: str) -> List[str]:
+    """Extract potential tags from discussion text"""
+    text_lower = text.lower()
+
+    # Look for explicit tag mentions like "tag: pytorch" or "#pytorch"
+    explicit_tags = []
+
+    # Pattern 1: "tag: something" or "tags: something"
+    tag_pattern = r"tags?:\s*([a-zA-Z0-9-_,\s]+)"
+    matches = re.findall(tag_pattern, text_lower)
+    for match in matches:
+        # Split by comma and clean up
+        tags = [tag.strip() for tag in match.split(",")]
+        explicit_tags.extend(tags)
+
+    # Pattern 2: "#hashtag" style
+    hashtag_pattern = r"#([a-zA-Z0-9-_]+)"
+    hashtag_matches = re.findall(hashtag_pattern, text_lower)
+    explicit_tags.extend(hashtag_matches)
+
+    # Pattern 3: Look for recognized tags mentioned in natural text
+    mentioned_tags = []
+    for tag in RECOGNIZED_TAGS:
+        if tag in text_lower:
+            mentioned_tags.append(tag)
+
+    # Combine and deduplicate
+    all_tags = list(set(explicit_tags + mentioned_tags))
+
+    # Filter to only include recognized tags or explicitly mentioned ones
+    valid_tags = []
+    for tag in all_tags:
+        if tag in RECOGNIZED_TAGS or tag in explicit_tags:
+            valid_tags.append(tag)
+
+    return valid_tags
+
+
+async def process_tags_directly(all_tags: List[str], repo_name: str) -> List[str]:
+    """Process tags using direct HuggingFace Hub API calls"""
+    print("🔧 Using direct HuggingFace Hub API approach...")
+    result_messages = []
+    
+    if not HF_TOKEN:
+        error_msg = "No HF_TOKEN configured"
+        print(f"❌ {error_msg}")
+        return [error_msg]
+    
+    try:
+        from huggingface_hub import HfApi, model_info, dataset_info, space_info, ModelCard, ModelCardData
+        from huggingface_hub.utils import HfHubHTTPError
+        from huggingface_hub import CommitOperationAdd
+        
+        hf_api = HfApi(token=HF_TOKEN)
+        
+        # First, let's determine what type of repository this is
+        repo_type = None
+        repo_info = None
+        
+        # Try different repository types
+        for repo_type_to_try in ["model", "dataset", "space"]:
+            try:
+                print(f"🔍 Trying to access {repo_name} as {repo_type_to_try}...")
+                if repo_type_to_try == "model":
+                    repo_info = model_info(repo_id=repo_name, token=HF_TOKEN)
+                elif repo_type_to_try == "dataset":
+                    repo_info = dataset_info(repo_id=repo_name, token=HF_TOKEN)
+                elif repo_type_to_try == "space":
+                    repo_info = space_info(repo_id=repo_name, token=HF_TOKEN)
+                
+                repo_type = repo_type_to_try
+                print(f"✅ Found repository as {repo_type}")
+                break
+                
+            except HfHubHTTPError as e:
+                if "404" in str(e):
+                    print(f"⚠️ Repository not found as {repo_type_to_try}")
+                    continue
+                else:
+                    print(f"❌ Error accessing as {repo_type_to_try}: {e}")
+                    continue
+            except Exception as e:
+                print(f"❌ Unexpected error for {repo_type_to_try}: {e}")
+                continue
+        
+        if not repo_type or not repo_info:
+            error_msg = f"Repository '{repo_name}' not found as model, dataset, or space"
+            print(f"❌ {error_msg}")
+            return [f"Error: {error_msg}"]
+        
+        print(f"📋 Repository type: {repo_type}")
+        current_tags = repo_info.tags if repo_info.tags else []
+        print(f"🏷️ Current tags: {current_tags}")
+        
+        # Process each tag
+        for tag in all_tags:
+            try:
+                # Check if tag already exists
+                if tag in current_tags:
+                    msg = f"Tag '{tag}': Already exists"
+                    print(f"✅ {msg}")
+                    result_messages.append(msg)
+                    continue
+                
+                # Add the new tag
+                print(f"🔧 Adding tag '{tag}' to {repo_type} '{repo_name}'")
+                updated_tags = current_tags + [tag]
+                
+                # Create model card content with updated tags
+                try:
+                    # Load existing model card
+                    print(f"📄 Loading existing model card...")
+                    card = ModelCard.load(repo_name, token=HF_TOKEN, repo_type=repo_type)
+                    if not hasattr(card, "data") or card.data is None:
+                        card.data = ModelCardData()
+                except HfHubHTTPError:
+                    # Create new model card if none exists
+                    print(f"📄 Creating new model card (none exists)")
+                    card = ModelCard("")
+                    card.data = ModelCardData()
+                
+                # Update tags
+                card_dict = card.data.to_dict()
+                card_dict["tags"] = updated_tags
+                card.data = ModelCardData(**card_dict)
+                
+                # Create a pull request with the updated model card
+                pr_title = f"Add '{tag}' tag"
+                pr_description = f"""
+## Add tag: {tag}
+
+This PR adds the `{tag}` tag to the {repo_type} repository.
+
+**Changes:**
+- Added `{tag}` to {repo_type} tags
+- Updated from {len(current_tags)} to {len(updated_tags)} tags
+
+**Current tags:** {", ".join(current_tags) if current_tags else "None"}
+**New tags:** {", ".join(updated_tags)}
+"""
+                
+                print(f"🚀 Creating PR with title: {pr_title}")
+                
+                # Create commit with updated model card
+                commit_info = hf_api.create_commit(
+                    repo_id=repo_name,
+                    repo_type=repo_type,
+                    operations=[
+                        CommitOperationAdd(
+                            path_in_repo="README.md", 
+                            path_or_fileobj=str(card).encode("utf-8")
+                        )
+                    ],
+                    commit_message=pr_title,
+                    commit_description=pr_description,
+                    token=HF_TOKEN,
+                    create_pr=True,
+                )
+                
+                # Extract PR URL from commit info
+                pr_url = getattr(commit_info, 'pr_url', str(commit_info))
+                
+                print(f"✅ PR created successfully! URL: {pr_url}")
+                msg = f"Tag '{tag}': PR created - {pr_url}"
+                result_messages.append(msg)
+                
+            except Exception as tag_error:
+                error_msg = f"Tag '{tag}': Error - {str(tag_error)}"
+                print(f"❌ {error_msg}")
+                result_messages.append(error_msg)
+        
+        return result_messages
+        
+    except Exception as e:
+        error_msg = f"Direct API processing failed: {str(e)}"
+        print(f"❌ {error_msg}")
+        return [error_msg]
+
+
+async def process_webhook_comment(webhook_data: Dict[str, Any]):
+    """Process webhook to detect and add tags"""
+    print("🏷️ Starting process_webhook_comment...")
+
+    try:
+        comment_content = webhook_data["comment"]["content"]
+        discussion_title = webhook_data["discussion"]["title"]
+        repo_name = webhook_data["repo"]["name"]
+        discussion_num = webhook_data["discussion"]["num"]
+        comment_author = webhook_data["comment"]["author"].get("id", "unknown")
+
+        print(f"📝 Comment content: {comment_content}")
+        print(f"📰 Discussion title: {discussion_title}")
+        print(f"📦 Repository: {repo_name}")
+
+        # Extract potential tags from the comment and discussion title
+        comment_tags = extract_tags_from_text(comment_content)
+        title_tags = extract_tags_from_text(discussion_title)
+        all_tags = list(set(comment_tags + title_tags))
+
+        print(f"🔍 Comment tags found: {comment_tags}")
+        print(f"🔍 Title tags found: {title_tags}")
+        print(f"🏷️ All unique tags: {all_tags}")
+
+        result_messages = []
+
+        if not all_tags:
+            msg = "No recognizable tags found in the discussion."
+            print(f"❌ {msg}")
+            result_messages.append(msg)
+        else:
+            # Skip agent entirely and use direct API approach
+            print("🔧 Using direct HuggingFace Hub API processing...")
+            result_messages = await process_tags_directly(all_tags, repo_name)
+
+        # Store the interaction
+        base_url = "https://huggingface.co"
+        discussion_url = f"{base_url}/{repo_name}/discussions/{discussion_num}"
+
+        interaction = {
+            "timestamp": datetime.now().isoformat(),
+            "repo": repo_name,
+            "discussion_title": discussion_title,
+            "discussion_num": discussion_num,
+            "discussion_url": discussion_url,
+            "original_comment": comment_content,
+            "comment_author": comment_author,
+            "detected_tags": all_tags,
+            "results": result_messages,
+        }
+
+        tag_operations_store.append(interaction)
+        final_result = " | ".join(result_messages)
+        print(f"💾 Stored interaction and returning result: {final_result}")
+        return final_result
+
+    except Exception as e:
+        error_msg = f"❌ Fatal error in process_webhook_comment: {str(e)}"
+        print(error_msg)
+        import traceback
+        print(f"❌ Traceback: {traceback.format_exc()}")
+        return error_msg
+
+
+@app.post("/webhook")
+async def webhook_handler(request: Request, background_tasks: BackgroundTasks):
+    """Handle HF Hub webhooks"""
+    webhook_secret = request.headers.get("X-Webhook-Secret")
+    if webhook_secret != WEBHOOK_SECRET:
+        print("❌ Invalid webhook secret")
+        return {"error": "Invalid webhook secret"}
+
+    payload = await request.json()
+    print(f"📥 Received webhook payload: {json.dumps(payload, indent=2)}")
+
+    event = payload.get("event", {})
+    scope = event.get("scope")
+    action = event.get("action")
+
+    print(f"🔍 Event details - scope: {scope}, action: {action}")
+
+    # Check if this is a discussion comment creation
+    scope_check = scope == "discussion"
+    action_check = action == "create"
+    not_pr = not payload["discussion"]["isPullRequest"]
+    scope_check = scope_check and not_pr
+    print(f"✅ not_pr: {not_pr}")
+    print(f"✅ scope_check: {scope_check}")
+    print(f"✅ action_check: {action_check}")
+
+    if scope_check and action_check:
+        # Verify we have the required fields
+        required_fields = ["comment", "discussion", "repo"]
+        missing_fields = [field for field in required_fields if field not in payload]
+
+        if missing_fields:
+            error_msg = f"Missing required fields: {missing_fields}"
+            print(f"❌ {error_msg}")
+            return {"error": error_msg}
+
+        print(f"🚀 Processing webhook for repo: {payload['repo']['name']}")
+        background_tasks.add_task(process_webhook_comment, payload)
+        return {"status": "processing"}
+
+    print(f"⏭️ Ignoring webhook - scope: {scope}, action: {action}")
+    return {"status": "ignored"}
+
+
+async def simulate_webhook(
+    repo_name: str, discussion_title: str, comment_content: str
+) -> str:
+    """Simulate webhook for testing"""
+    if not all([repo_name, discussion_title, comment_content]):
+        return "Please fill in all fields."
+
+    mock_payload = {
+        "event": {"action": "create", "scope": "discussion"},
+        "comment": {
+            "content": comment_content,
+            "author": {"id": "test-user-id"},
+            "id": "mock-comment-id",
+            "hidden": False,
+        },
+        "discussion": {
+            "title": discussion_title,
+            "num": len(tag_operations_store) + 1,
+            "id": "mock-discussion-id",
+            "status": "open",
+            "isPullRequest": False,
+        },
+        "repo": {
+            "name": repo_name,
+            "type": "model",
+            "private": False,
+        },
+    }
+
+    response = await process_webhook_comment(mock_payload)
+    return f"✅ Processed! Results: {response}"
+
+
+def create_gradio_app():
+    """Create Gradio interface"""
+    with gr.Blocks(title="HF Tagging Bot", theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# 🏷️ HF Tagging Bot Dashboard")
+        gr.Markdown("*Automatically adds tags to models, datasets, and spaces when mentioned in discussions*")
+
+        gr.Markdown("""
+        ## How it works:
+        - Monitors HuggingFace Hub discussions
+        - Detects tag mentions in comments (e.g., "tag: pytorch", "#transformers")
+        - Automatically detects repository type (model/dataset/space)
+        - Creates pull requests to add recognized tags to the repository
+        - Supports common ML tags like: pytorch, tensorflow, text-generation, etc.
+        """)
+
+        with gr.Column():
+            sim_repo = gr.Textbox(
+                label="Repository",
+                value="burtenshaw/play-mcp-repo-bot",
+                placeholder="username/repo-name (can be model, dataset, or space)",
+            )
+            sim_title = gr.Textbox(
+                label="Discussion Title",
+                value="Add pytorch tag",
+                placeholder="Discussion title",
+            )
+            sim_comment = gr.Textbox(
+                label="Comment",
+                lines=3,
+                value="This repository should have tags: pytorch, text-generation",
+                placeholder="Comment mentioning tags...",
+            )
+            sim_btn = gr.Button("🏷️ Test Tag Detection")
+
+        with gr.Column():
+            sim_result = gr.Textbox(label="Result", lines=8)
+
+        sim_btn.click(
+            fn=simulate_webhook,
+            inputs=[sim_repo, sim_title, sim_comment],
+            outputs=sim_result,
+        )
+
+        gr.Markdown(f"""
+        ## Recognized Tags:
+        {", ".join(sorted(RECOGNIZED_TAGS))}
+        """)
+
+        # Add recent operations section
+        if tag_operations_store:
+            gr.Markdown("## Recent Operations")
+            for op in tag_operations_store[-5:]:  # Show last 5 operations
+                gr.Markdown(f"""
+                **{op['repo']}** - {op['timestamp'][:19]}
+                - Tags: {', '.join(op['detected_tags'])}
+                - Results: {' | '.join(op['results'][:2])}...
+                """)
+
+    return demo
+
+
+# Mount Gradio app
+gradio_app = create_gradio_app()
+app = gr.mount_gradio_app(app, gradio_app, path="/gradio")
+
+
+if __name__ == "__main__":
+    print("🚀 Starting HF Tagging Bot...")
+    print(f"📊 Dashboard: http://localhost:7860/gradio")
+    print(f"🔗 Webhook: http://localhost:7860/webhook")
+    print(f"🔑 HF_TOKEN configured: {bool(HF_TOKEN)}")
+    print("🔧 Using direct HuggingFace Hub API (Windows compatible)")
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)