Update app.py

app.py

@@ -1,524 +1,524 @@
-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 huggingface_hub.inference._mcp.agent import Agent
-from dotenv import load_dotenv
-
-load_dotenv()
-
-# Configuration
-WEBHOOK_SECRET = os.getenv("WEBHOOK_SECRET", "5a775af722adc63d0b895454e3fb7a50cbc62bfa3f97e37d50d1a986c91d8781")
-HF_TOKEN = os.getenv("HF_TOKEN")
-HF_MODEL = os.getenv("HF_MODEL", "microsoft/DialoGPT-medium")
-# Use a valid provider literal from the documentation
-DEFAULT_PROVIDER: Literal["hf-inference"] = "hf-inference"
-HF_PROVIDER = os.getenv("HF_PROVIDER", DEFAULT_PROVIDER)
-
-# Simple storage for processed tag operations
-tag_operations_store: List[Dict[str, Any]] = []
-
-# Agent instance
-agent_instance: Optional[Agent] = None
-
-# 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=["*"])
-
-
-async def get_agent():
-    """Get or create Agent instance"""
-    print("🤖 get_agent() called...")
-    global agent_instance
-    if agent_instance is None and HF_TOKEN:
-        print("🔧 Creating new Agent instance...")
-        print(f"🔑 HF_TOKEN present: {bool(HF_TOKEN)}")
-        print(f"🤖 Model: {HF_MODEL}")
-        print(f"🔗 Provider: {DEFAULT_PROVIDER}")
-
-        try:
-            agent_instance = Agent(
-                model=HF_MODEL,
-                provider=DEFAULT_PROVIDER,
-                api_key=HF_TOKEN,
-                servers=[
-                    {
-                        "type": "stdio",
-                        "config": {
-                            "command": "python",
-                            "args": ["mcp_server.py"],
-                            "cwd": ".",  # Ensure correct working directory
-                            "env": {"HF_TOKEN": HF_TOKEN} if HF_TOKEN else {},
-                        },
-                    }
-                ],
-            )
-            print("✅ Agent instance created successfully")
-            print("🔧 Loading tools...")
-            await agent_instance.load_tools()
-            print("✅ Tools loaded successfully")
-        except Exception as e:
-            print(f"❌ Error creating/loading agent: {str(e)}")
-            agent_instance = None
-    elif agent_instance is None:
-        print("❌ No HF_TOKEN available, cannot create agent")
-    else:
-        print("✅ Using existing agent instance")
-
-    return agent_instance
-
-
-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_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"]
-        # Author is an object with "id" field
-        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:
-            print("🤖 Getting agent instance...")
-            agent = await get_agent()
-            if not agent:
-                msg = "Error: Agent not configured (missing HF_TOKEN)"
-                print(f"❌ {msg}")
-                result_messages.append(msg)
-            else:
-                print("✅ Agent instance obtained successfully")
-
-                # Process all tags in a single conversation with the agent
-                try:
-                    # Create a comprehensive prompt for the agent
-                    user_prompt = f"""
-I need to add the following tags to the repository '{repo_name}': {", ".join(all_tags)}
-
-For each tag, please:
-1. Check if the tag already exists on the repository using get_current_tags
-2. If the tag doesn't exist, add it using add_new_tag
-3. Provide a summary of what was done for each tag
-
-Please process all {len(all_tags)} tags: {", ".join(all_tags)}
-"""
-
-                    print("💬 Sending comprehensive prompt to agent...")
-                    print(f"📝 Prompt: {user_prompt}")
-
-                    # Let the agent handle the entire conversation
-                    conversation_result = []
-
-                    try:
-                        async for item in agent.run(user_prompt):
-                            # The agent yields different types of items
-                            item_str = str(item)
-                            conversation_result.append(item_str)
-
-                            # Log important events
-                            if (
-                                "tool_call" in item_str.lower()
-                                or "function" in item_str.lower()
-                            ):
-                                print(f"🔧 Agent using tools: {item_str[:200]}...")
-                            elif "content" in item_str and len(item_str) < 500:
-                                print(f"💭 Agent response: {item_str}")
-
-                        # Extract the final response from the conversation
-                        full_response = " ".join(conversation_result)
-                        print(f"📋 Agent conversation completed successfully")
-
-                        # Try to extract meaningful results for each tag
-                        for tag in all_tags:
-                            tag_mentioned = tag.lower() in full_response.lower()
-
-                            if (
-                                "already exists" in full_response.lower()
-                                and tag_mentioned
-                            ):
-                                msg = f"Tag '{tag}': Already exists"
-                            elif (
-                                "pr" in full_response.lower()
-                                or "pull request" in full_response.lower()
-                            ):
-                                if tag_mentioned:
-                                    msg = f"Tag '{tag}': PR created successfully"
-                                else:
-                                    msg = (
-                                        f"Tag '{tag}': Processed "
-                                        "(PR may have been created)"
-                                    )
-                            elif "success" in full_response.lower() and tag_mentioned:
-                                msg = f"Tag '{tag}': Successfully processed"
-                            elif "error" in full_response.lower() and tag_mentioned:
-                                msg = f"Tag '{tag}': Error during processing"
-                            else:
-                                msg = f"Tag '{tag}': Processed by agent"
-
-                            print(f"✅ Result for tag '{tag}': {msg}")
-                            result_messages.append(msg)
-
-                    except Exception as agent_error:
-                        print(f"⚠️ Agent streaming failed: {str(agent_error)}")
-                        print("🔄 Falling back to direct MCP tool calls...")
-
-                        # Import the MCP server functions directly as fallback
-                        try:
-                            import sys
-                            import importlib.util
-
-                            # Load the MCP server module
-                            spec = importlib.util.spec_from_file_location(
-                                "mcp_server", "./mcp_server.py"
-                            )
-                            mcp_module = importlib.util.module_from_spec(spec)
-                            spec.loader.exec_module(mcp_module)
-
-                            # Use the MCP tools directly for each tag
-                            for tag in all_tags:
-                                try:
-                                    print(
-                                        f"🔧 Directly calling get_current_tags for '{tag}'"
-                                    )
-                                    current_tags_result = mcp_module.get_current_tags(
-                                        repo_name
-                                    )
-                                    print(
-                                        f"📄 Current tags result: {current_tags_result}"
-                                    )
-
-                                    # Parse the JSON result
-                                    import json
-
-                                    tags_data = json.loads(current_tags_result)
-
-                                    if tags_data.get("status") == "success":
-                                        current_tags = tags_data.get("current_tags", [])
-                                        if tag in current_tags:
-                                            msg = f"Tag '{tag}': Already exists"
-                                            print(f"✅ {msg}")
-                                        else:
-                                            print(
-                                                f"🔧 Directly calling add_new_tag for '{tag}'"
-                                            )
-                                            add_result = mcp_module.add_new_tag(
-                                                repo_name, tag
-                                            )
-                                            print(f"📄 Add tag result: {add_result}")
-
-                                            add_data = json.loads(add_result)
-                                            if add_data.get("status") == "success":
-                                                pr_url = add_data.get("pr_url", "")
-                                                msg = f"Tag '{tag}': PR created - {pr_url}"
-                                            elif (
-                                                add_data.get("status")
-                                                == "already_exists"
-                                            ):
-                                                msg = f"Tag '{tag}': Already exists"
-                                            else:
-                                                msg = f"Tag '{tag}': {add_data.get('message', 'Processed')}"
-                                            print(f"✅ {msg}")
-                                    else:
-                                        error_msg = tags_data.get(
-                                            "error", "Unknown error"
-                                        )
-                                        msg = f"Tag '{tag}': Error - {error_msg}"
-                                        print(f"❌ {msg}")
-
-                                    result_messages.append(msg)
-
-                                except Exception as direct_error:
-                                    error_msg = f"Tag '{tag}': Direct call error - {str(direct_error)}"
-                                    print(f"❌ {error_msg}")
-                                    result_messages.append(error_msg)
-
-                        except Exception as fallback_error:
-                            error_msg = (
-                                f"Fallback approach failed: {str(fallback_error)}"
-                            )
-                            print(f"❌ {error_msg}")
-                            result_messages.append(error_msg)
-
-                except Exception as e:
-                    error_msg = f"Error during agent processing: {str(e)}"
-                    print(f"❌ {error_msg}")
-                    result_messages.append(error_msg)
-
-        # 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)
-        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 when mentioned in discussions*")
-
-        gr.Markdown("""
-        ## How it works:
-        - Monitors HuggingFace Hub discussions
-        - Detects tag mentions in comments (e.g., "tag: pytorch", 
-          "#transformers")
-        - Automatically adds recognized tags to the model 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/model-name",
-            )
-            sim_title = gr.Textbox(
-                label="Discussion Title",
-                value="Add pytorch tag",
-                placeholder="Discussion title",
-            )
-            sim_comment = gr.Textbox(
-                label="Comment",
-                lines=3,
-                value="This model 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))}
-        """)
-
-    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("📊 Dashboard: http://localhost:7860/gradio")
-    print("🔗 Webhook: http://localhost:7860/webhook")
-    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)
+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 huggingface_hub.inference._mcp.agent import Agent
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Configuration
+WEBHOOK_SECRET = os.getenv("WEBHOOK_SECRET", "5a775af722adc63d0b895454e3fb7a50cbc62bfa3f97e37d50d1a986c91d8781")
+HF_TOKEN = os.getenv("HF_TOKEN")
+HF_MODEL = os.getenv("HF_MODEL", "microsoft/DialoGPT-medium")
+# Use a valid provider literal from the documentation
+DEFAULT_PROVIDER: Literal["hf-inference"] = "hf-inference"
+HF_PROVIDER = os.getenv("HF_PROVIDER", DEFAULT_PROVIDER)
+
+# Simple storage for processed tag operations
+tag_operations_store: List[Dict[str, Any]] = []
+
+# Agent instance
+agent_instance: Optional[Agent] = None
+
+# 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=["*"])
+
+
+async def get_agent():
+    """Get or create Agent instance"""
+    print("🤖 get_agent() called...")
+    global agent_instance
+    if agent_instance is None and HF_TOKEN:
+        print("🔧 Creating new Agent instance...")
+        print(f"🔑 HF_TOKEN present: {bool(HF_TOKEN)}")
+        print(f"🤖 Model: {HF_MODEL}")
+        print(f"🔗 Provider: {DEFAULT_PROVIDER}")
+
+        try:
+            agent_instance = Agent(
+                model=HF_MODEL,
+                provider=DEFAULT_PROVIDER,
+                api_key=HF_TOKEN,
+                servers=[
+                    {
+                        "type": "stdio",
+                        "config": {
+                            "command": "python",
+                            "args": ["mcp_server.py"],
+                            "cwd": ".",  # Ensure correct working directory
+                            "env": {"HF_TOKEN": HF_TOKEN} if HF_TOKEN else {},
+                        },
+                    }
+                ],
+            )
+            print("✅ Agent instance created successfully")
+            print("🔧 Loading tools...")
+            await agent_instance.load_tools()
+            print("✅ Tools loaded successfully")
+        except Exception as e:
+            print(f"❌ Error creating/loading agent: {str(e)}")
+            agent_instance = None
+    elif agent_instance is None:
+        print("❌ No HF_TOKEN available, cannot create agent")
+    else:
+        print("✅ Using existing agent instance")
+
+    return agent_instance
+
+
+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_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"]
+        # Author is an object with "id" field
+        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:
+            print("🤖 Getting agent instance...")
+            agent = await get_agent()
+            if not agent:
+                msg = "Error: Agent not configured (missing HF_TOKEN)"
+                print(f"❌ {msg}")
+                result_messages.append(msg)
+            else:
+                print("✅ Agent instance obtained successfully")
+
+                # Process all tags in a single conversation with the agent
+                try:
+                    # Create a comprehensive prompt for the agent
+                    user_prompt = f"""
+I need to add the following tags to the repository '{repo_name}': {", ".join(all_tags)}
+
+For each tag, please:
+1. Check if the tag already exists on the repository using get_current_tags
+2. If the tag doesn't exist, add it using add_new_tag
+3. Provide a summary of what was done for each tag
+
+Please process all {len(all_tags)} tags: {", ".join(all_tags)}
+"""
+
+                    print("💬 Sending comprehensive prompt to agent...")
+                    print(f"📝 Prompt: {user_prompt}")
+
+                    # Let the agent handle the entire conversation
+                    conversation_result = []
+
+                    try:
+                        async for item in agent.run(user_prompt):
+                            # The agent yields different types of items
+                            item_str = str(item)
+                            conversation_result.append(item_str)
+
+                            # Log important events
+                            if (
+                                "tool_call" in item_str.lower()
+                                or "function" in item_str.lower()
+                            ):
+                                print(f"🔧 Agent using tools: {item_str[:200]}...")
+                            elif "content" in item_str and len(item_str) < 500:
+                                print(f"💭 Agent response: {item_str}")
+
+                        # Extract the final response from the conversation
+                        full_response = " ".join(conversation_result)
+                        print(f"📋 Agent conversation completed successfully")
+
+                        # Try to extract meaningful results for each tag
+                        for tag in all_tags:
+                            tag_mentioned = tag.lower() in full_response.lower()
+
+                            if (
+                                "already exists" in full_response.lower()
+                                and tag_mentioned
+                            ):
+                                msg = f"Tag '{tag}': Already exists"
+                            elif (
+                                "pr" in full_response.lower()
+                                or "pull request" in full_response.lower()
+                            ):
+                                if tag_mentioned:
+                                    msg = f"Tag '{tag}': PR created successfully"
+                                else:
+                                    msg = (
+                                        f"Tag '{tag}': Processed "
+                                        "(PR may have been created)"
+                                    )
+                            elif "success" in full_response.lower() and tag_mentioned:
+                                msg = f"Tag '{tag}': Successfully processed"
+                            elif "error" in full_response.lower() and tag_mentioned:
+                                msg = f"Tag '{tag}': Error during processing"
+                            else:
+                                msg = f"Tag '{tag}': Processed by agent"
+
+                            print(f"✅ Result for tag '{tag}': {msg}")
+                            result_messages.append(msg)
+
+                    except Exception as agent_error:
+                        print(f"⚠️ Agent streaming failed: {str(agent_error)}")
+                        print("🔄 Falling back to direct MCP tool calls...")
+
+                        # Import the MCP server functions directly as fallback
+                        try:
+                            import sys
+                            import importlib.util
+
+                            # Load the MCP server module
+                            spec = importlib.util.spec_from_file_location(
+                                "mcp_server", "./mcp_server.py"
+                            )
+                            mcp_module = importlib.util.module_from_spec(spec)
+                            spec.loader.exec_module(mcp_module)
+
+                            # Use the MCP tools directly for each tag
+                            for tag in all_tags:
+                                try:
+                                    print(
+                                        f"🔧 Directly calling get_current_tags for '{tag}'"
+                                    )
+                                    current_tags_result = mcp_module.get_current_tags(
+                                        repo_name
+                                    )
+                                    print(
+                                        f"📄 Current tags result: {current_tags_result}"
+                                    )
+
+                                    # Parse the JSON result
+                                    import json
+
+                                    tags_data = json.loads(current_tags_result)
+
+                                    if tags_data.get("status") == "success":
+                                        current_tags = tags_data.get("current_tags", [])
+                                        if tag in current_tags:
+                                            msg = f"Tag '{tag}': Already exists"
+                                            print(f"✅ {msg}")
+                                        else:
+                                            print(
+                                                f"🔧 Directly calling add_new_tag for '{tag}'"
+                                            )
+                                            add_result = mcp_module.add_new_tag(
+                                                repo_name, tag
+                                            )
+                                            print(f"📄 Add tag result: {add_result}")
+
+                                            add_data = json.loads(add_result)
+                                            if add_data.get("status") == "success":
+                                                pr_url = add_data.get("pr_url", "")
+                                                msg = f"Tag '{tag}': PR created - {pr_url}"
+                                            elif (
+                                                add_data.get("status")
+                                                == "already_exists"
+                                            ):
+                                                msg = f"Tag '{tag}': Already exists"
+                                            else:
+                                                msg = f"Tag '{tag}': {add_data.get('message', 'Processed')}"
+                                            print(f"✅ {msg}")
+                                    else:
+                                        error_msg = tags_data.get(
+                                            "error", "Unknown error"
+                                        )
+                                        msg = f"Tag '{tag}': Error - {error_msg}"
+                                        print(f"❌ {msg}")
+
+                                    result_messages.append(msg)
+
+                                except Exception as direct_error:
+                                    error_msg = f"Tag '{tag}': Direct call error - {str(direct_error)}"
+                                    print(f"❌ {error_msg}")
+                                    result_messages.append(error_msg)
+
+                        except Exception as fallback_error:
+                            error_msg = (
+                                f"Fallback approach failed: {str(fallback_error)}"
+                            )
+                            print(f"❌ {error_msg}")
+                            result_messages.append(error_msg)
+
+                except Exception as e:
+                    error_msg = f"Error during agent processing: {str(e)}"
+                    print(f"❌ {error_msg}")
+                    result_messages.append(error_msg)
+
+        # 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)
+        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.comment""
+    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 when mentioned in discussions*")
+
+        gr.Markdown("""
+        ## How it works:
+        - Monitors HuggingFace Hub discussions
+        - Detects tag mentions in comments (e.g., "tag: pytorch", 
+          "#transformers")
+        - Automatically adds recognized tags to the model 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/model-name",
+            )
+            sim_title = gr.Textbox(
+                label="Discussion Title",
+                value="Add pytorch tag",
+                placeholder="Discussion title",
+            )
+            sim_comment = gr.Textbox(
+                label="Comment",
+                lines=3,
+                value="This model 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))}
+        """)
+
+    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("📊 Dashboard: http://localhost:7860/gradio")
+    print("🔗 Webhook: http://localhost:7860/webhook")
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)