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"""
Chat handling logic for Universal MCP Client - Fixed Version with File Upload Support
"""
import re
import logging
import traceback
from datetime import datetime
from typing import Dict, Any, List, Tuple, Optional
import gradio as gr
from gradio import ChatMessage
from gradio_client import Client
import time
import json
import httpx
from config import AppConfig
from mcp_client import UniversalMCPClient
logger = logging.getLogger(__name__)
class ChatHandler:
"""Handles chat interactions with HF Inference Providers and MCP servers using ChatMessage dataclass"""
def __init__(self, mcp_client: UniversalMCPClient):
self.mcp_client = mcp_client
# Initialize the file uploader client for converting local files to public URLs
try:
self.uploader_client = Client("abidlabs/file-uploader")
logger.info("✅ File uploader client initialized")
except Exception as e:
logger.error(f"Failed to initialize file uploader: {e}")
self.uploader_client = None
def _upload_file_to_gradio_server(self, file_path: str) -> str:
"""Upload a file to the Gradio server and get a public URL"""
if not self.uploader_client:
logger.error("File uploader client not initialized")
return file_path
try:
# Open file in binary mode as your peer discovered
with open(file_path, "rb") as f_:
files = [("files", (file_path.split("/")[-1], f_))]
r = httpx.post(
self.uploader_client.upload_url,
files=files,
)
r.raise_for_status()
result = r.json()
uploaded_path = result[0]
# Construct the full public URL
public_url = f"{self.uploader_client.src}/gradio_api/file={uploaded_path}"
logger.info(f"✅ Uploaded {file_path} -> {public_url}")
return public_url
except Exception as e:
logger.error(f"Failed to upload file {file_path}: {e}")
return file_path # Return original path as fallback
def process_multimodal_message(self, message: Dict[str, Any], history: List) -> Tuple[List[ChatMessage], Dict[str, Any]]:
"""Enhanced MCP chat function with multimodal input support and ChatMessage formatting"""
if not self.mcp_client.hf_client:
error_msg = "❌ HuggingFace token not configured. Please set HF_TOKEN environment variable or login."
history.append(ChatMessage(role="user", content=error_msg))
history.append(ChatMessage(role="assistant", content=error_msg))
return history, gr.MultimodalTextbox(value=None, interactive=False)
if not self.mcp_client.current_provider or not self.mcp_client.current_model:
error_msg = "❌ Please select an inference provider and model first."
history.append(ChatMessage(role="user", content=error_msg))
history.append(ChatMessage(role="assistant", content=error_msg))
return history, gr.MultimodalTextbox(value=None, interactive=False)
# Initialize variables for error handling
user_text = ""
user_files = []
uploaded_file_urls = [] # Store uploaded file URLs
self.file_url_mapping = {} # Add this: Map local paths to uploaded URLs
try:
# Handle multimodal input - message is a dict with 'text' and 'files'
user_text = message.get("text", "") if message else ""
user_files = message.get("files", []) if message else []
# Handle case where message might be a string (backward compatibility)
if isinstance(message, str):
user_text = message
user_files = []
logger.info(f"💬 Processing multimodal message:")
logger.info(f" 📝 Text: {user_text}")
logger.info(f" 📁 Files: {len(user_files)} files uploaded")
logger.info(f" 📋 History type: {type(history)}, length: {len(history)}")
# Convert history to ChatMessage objects if needed
converted_history = []
for i, msg in enumerate(history):
try:
if isinstance(msg, dict):
# Convert dict to ChatMessage for internal processing
logger.info(f" 📝 Converting dict message {i}: {msg.get('role', 'unknown')}")
converted_history.append(ChatMessage(
role=msg.get('role', 'assistant'),
content=msg.get('content', ''),
metadata=msg.get('metadata', None)
))
else:
# Already a ChatMessage
logger.info(f" ✅ ChatMessage {i}: {getattr(msg, 'role', 'unknown')}")
converted_history.append(msg)
except Exception as conv_error:
logger.error(f"Error converting message {i}: {conv_error}")
logger.error(f"Message content: {msg}")
# Skip problematic messages
continue
history = converted_history
# Upload files and get public URLs
for file_path in user_files:
logger.info(f" 📄 Local File: {file_path}")
try:
# Upload file to get public URL
uploaded_url = self._upload_file_to_gradio_server(file_path)
# Store the mapping
self.file_url_mapping[file_path] = uploaded_url
logger.info(f" ✅ Uploaded File URL: {uploaded_url}")
# Add to history with public URL
history.append(ChatMessage(role="user", content={"path": uploaded_url}))
except Exception as upload_error:
logger.error(f"Failed to upload file {file_path}: {upload_error}")
# Fallback to local path with warning
history.append(ChatMessage(role="user", content={"path": file_path}))
logger.warning(f"⚠️ Using local path for {file_path} - MCP servers may not be able to access it")
# Add text message if provided
if user_text and user_text.strip():
history.append(ChatMessage(role="user", content=user_text))
# If no text and no files, return early
if not user_text.strip() and not user_files:
return history, gr.MultimodalTextbox(value=None, interactive=False)
# Create messages for HF Inference API
messages = self._prepare_hf_messages(history, uploaded_file_urls)
# Process the chat and get structured responses
response_messages = self._call_hf_api(messages, uploaded_file_urls)
# Add all response messages to history
history.extend(response_messages)
return history, gr.MultimodalTextbox(value=None, interactive=False)
except Exception as e:
error_msg = f"❌ Error: {str(e)}"
logger.error(f"Chat error: {e}")
logger.error(traceback.format_exc())
# Add user input to history if it exists
if user_text and user_text.strip():
history.append(ChatMessage(role="user", content=user_text))
if user_files:
for file_path in user_files:
history.append(ChatMessage(role="user", content={"path": file_path}))
history.append(ChatMessage(role="assistant", content=error_msg))
return history, gr.MultimodalTextbox(value=None, interactive=False)
def _prepare_hf_messages(self, history: List, uploaded_file_urls: List[str] = None) -> List[Dict[str, Any]]:
"""Convert history (ChatMessage or dict) to HuggingFace Inference API format"""
messages = []
# Get optimal context settings for current model/provider
if self.mcp_client.current_model and self.mcp_client.current_provider:
context_settings = AppConfig.get_optimal_context_settings(
self.mcp_client.current_model,
self.mcp_client.current_provider,
len(self.mcp_client.get_enabled_servers())
)
max_history = context_settings['recommended_history_limit']
else:
max_history = 20 # Fallback
# Convert history to HF API format (text only for context)
recent_history = history[-max_history:] if len(history) > max_history else history
for msg in recent_history:
# Handle both ChatMessage objects and dictionary format for backward compatibility
if hasattr(msg, 'role'): # ChatMessage object
role = msg.role
content = msg.content
elif isinstance(msg, dict) and 'role' in msg: # Dictionary format
role = msg.get('role')
content = msg.get('content')
else:
continue # Skip invalid messages
if role in ["user", "assistant"]:
# Convert any non-string content to string description for context
if isinstance(content, dict):
if "path" in content:
file_path = content.get('path', 'unknown')
# Check if it's a public URL or local path
if file_path.startswith('http'):
# It's already a public URL
if AppConfig.is_image_file(file_path):
content = f"[User uploaded an image: {file_path}]"
elif AppConfig.is_audio_file(file_path):
content = f"[User uploaded an audio file: {file_path}]"
elif AppConfig.is_video_file(file_path):
content = f"[User uploaded a video file: {file_path}]"
else:
content = f"[User uploaded a file: {file_path}]"
else:
# Local path - mention it's not accessible to remote servers
content = f"[User uploaded a file (local path, not accessible to remote servers): {file_path}]"
else:
content = f"[Object: {str(content)[:50]}...]"
elif isinstance(content, (list, tuple)):
content = f"[List: {str(content)[:50]}...]"
elif content is None:
content = "[Empty]"
else:
content = str(content)
messages.append({
"role": role,
"content": content
})
return messages
def _call_hf_api(self, messages: List[Dict[str, Any]], uploaded_file_urls: List[str] = None) -> List[ChatMessage]:
"""Call HuggingFace Inference API and return structured ChatMessage responses"""
# Check if we have enabled MCP servers to use
enabled_servers = self.mcp_client.get_enabled_servers()
if not enabled_servers:
return self._call_hf_without_mcp(messages)
else:
return self._call_hf_with_mcp(messages, uploaded_file_urls)
def _call_hf_without_mcp(self, messages: List[Dict[str, Any]]) -> List[ChatMessage]:
"""Call HF Inference API without MCP servers"""
logger.info("💬 No MCP servers available, using regular HF Inference chat")
system_prompt = self._get_native_system_prompt()
# Add system prompt to messages
if messages and messages[0].get("role") == "system":
messages[0]["content"] = system_prompt + "\n\n" + messages[0]["content"]
else:
messages.insert(0, {"role": "system", "content": system_prompt})
# Get optimal token settings
if self.mcp_client.current_model and self.mcp_client.current_provider:
context_settings = AppConfig.get_optimal_context_settings(
self.mcp_client.current_model,
self.mcp_client.current_provider,
0 # No MCP servers
)
max_tokens = context_settings['max_response_tokens']
else:
max_tokens = 8192
# Use HF Inference API
try:
response = self.mcp_client.generate_chat_completion(messages, **{"max_tokens": max_tokens})
response_text = response.choices[0].message.content
if not response_text:
response_text = "I understand your request and I'm here to help."
return [ChatMessage(role="assistant", content=response_text)]
except Exception as e:
logger.error(f"HF Inference API call failed: {e}")
return [ChatMessage(role="assistant", content=f"❌ API call failed: {str(e)}")]
def _call_hf_with_mcp(self, messages: List[Dict[str, Any]], uploaded_file_urls: List[str] = None) -> List[ChatMessage]:
"""Call HF Inference API with MCP servers and return structured responses"""
# Enhanced system prompt with multimodal and MCP instructions
system_prompt = self._get_mcp_system_prompt(uploaded_file_urls)
# Add system prompt to messages
if messages and messages[0].get("role") == "system":
messages[0]["content"] = system_prompt + "\n\n" + messages[0]["content"]
else:
messages.insert(0, {"role": "system", "content": system_prompt})
# Get optimal token settings
enabled_servers = self.mcp_client.get_enabled_servers()
if self.mcp_client.current_model and self.mcp_client.current_provider:
context_settings = AppConfig.get_optimal_context_settings(
self.mcp_client.current_model,
self.mcp_client.current_provider,
len(enabled_servers)
)
max_tokens = context_settings['max_response_tokens']
else:
max_tokens = 8192
# Debug logging
logger.info(f"📤 Sending {len(messages)} messages to HF Inference API")
logger.info(f"🔧 Using {len(self.mcp_client.servers)} MCP servers")
logger.info(f"🤖 Model: {self.mcp_client.current_model} via {self.mcp_client.current_provider}")
logger.info(f"📏 Max tokens: {max_tokens}")
start_time = time.time()
try:
# Pass file mapping to MCP client
if hasattr(self, 'file_url_mapping'):
self.mcp_client.chat_handler_file_mapping = self.file_url_mapping
# Call HF Inference with MCP tool support - using optimal max_tokens
response = self.mcp_client.generate_chat_completion_with_mcp_tools(messages, **{"max_tokens": max_tokens})
return self._process_hf_response(response, start_time)
except Exception as e:
logger.error(f"HF Inference API call with MCP failed: {e}")
return [ChatMessage(role="assistant", content=f"❌ API call failed: {str(e)}")]
def _process_hf_response(self, response, start_time: float) -> List[ChatMessage]:
"""Process HF Inference response with simplified media handling and nested errors"""
chat_messages = []
try:
response_text = response.choices[0].message.content
if not response_text:
response_text = "I understand your request and I'm here to help."
# Check if this response includes tool execution info
if hasattr(response, '_tool_execution'):
tool_info = response._tool_execution
logger.info(f"🔧 Processing response with tool execution: {tool_info}")
duration = round(time.time() - start_time, 2)
tool_id = f"tool_{tool_info['tool']}_{int(time.time())}"
if tool_info['success']:
tool_result = str(tool_info['result'])
# Extract media URL if present
media_url = self._extract_media_url(tool_result, tool_info.get('server', ''))
# Create tool usage metadata message
chat_messages.append(ChatMessage(
role="assistant",
content="",
metadata={
"title": f"🔧 Used {tool_info['tool']}",
"status": "done",
"duration": duration,
"id": tool_id
}
))
# Add nested success message with the raw result
if media_url:
result_preview = f"✅ Successfully generated media\nURL: {media_url[:100]}..."
else:
result_preview = f"✅ Tool executed successfully\nResult: {tool_result[:200]}..."
chat_messages.append(ChatMessage(
role="assistant",
content=result_preview,
metadata={
"title": "📊 Server Response",
"parent_id": tool_id,
"status": "done"
}
))
# Add LLM's descriptive text if present (before media)
if response_text and not response_text.startswith('{"use_tool"'):
# Clean the response text by removing URLs and tool JSON
clean_response = response_text
if media_url and media_url in clean_response:
clean_response = clean_response.replace(media_url, "").strip()
# Remove any remaining JSON tool call patterns
clean_response = re.sub(r'\{"use_tool"[^}]+\}', '', clean_response).strip()
# Remove all markdown link/image syntax completely
clean_response = re.sub(r'!\[([^\]]*)\]\([^)]*\)', '', clean_response) # Remove image markdown
clean_response = re.sub(r'\[([^\]]*)\]\([^)]*\)', '', clean_response) # Remove link markdown
clean_response = re.sub(r'!\[([^\]]*)\]', '', clean_response) # Remove broken image refs
clean_response = re.sub(r'\[([^\]]*)\]', '', clean_response) # Remove broken link refs
clean_response = re.sub(r'\(\s*\)', '', clean_response) # Remove empty parentheses
clean_response = clean_response.strip() # Final strip
# Only add if there's meaningful text left after cleaning
if clean_response and len(clean_response) > 10:
chat_messages.append(ChatMessage(
role="assistant",
content=clean_response
))
# Handle media content if present
if media_url:
# Add media as a separate message - Gradio will auto-detect type
chat_messages.append(ChatMessage(
role="assistant",
content={"path": media_url}
))
else:
# No media URL found, check if we need to show non-media result
if not response_text or response_text.startswith('{"use_tool"'):
# Only show result if there wasn't descriptive text from LLM
if len(tool_result) > 500:
result_preview = f"Operation completed successfully. Result preview: {tool_result[:500]}..."
else:
result_preview = f"Operation completed successfully. Result: {tool_result}"
chat_messages.append(ChatMessage(
role="assistant",
content=result_preview
))
else:
# Tool execution failed
error_details = tool_info['result']
# Create main tool message with error status
chat_messages.append(ChatMessage(
role="assistant",
content="",
metadata={
"title": f"❌ Used {tool_info['tool']}",
"status": "error",
"duration": duration,
"id": tool_id
}
))
# Add nested error response from server
chat_messages.append(ChatMessage(
role="assistant",
content=f"❌ Tool execution failed\n```\n{error_details}\n```",
metadata={
"title": "📊 Server Response",
"parent_id": tool_id,
"status": "error"
}
))
# Add suggestions as another nested message
chat_messages.append(ChatMessage(
role="assistant",
content="**Suggestions:**\n• Try modifying your request slightly\n• Wait a moment and try again\n• Use a different MCP server if available",
metadata={
"title": "💡 Possible Solutions",
"parent_id": tool_id,
"status": "info"
}
))
else:
# No tool usage, just return the response
chat_messages.append(ChatMessage(
role="assistant",
content=response_text
))
except Exception as e:
logger.error(f"Error processing HF response: {e}")
logger.error(traceback.format_exc())
chat_messages.append(ChatMessage(
role="assistant",
content="I understand your request and I'm here to help."
))
return chat_messages
def _extract_media_url(self, result_text: str, server_name: str) -> Optional[str]:
"""Extract media URL from MCP response with improved pattern matching"""
if not isinstance(result_text, str):
return None
logger.info(f"🔍 Extracting media from result: {result_text[:500]}...")
# Try JSON parsing first
try:
if result_text.strip().startswith('[') or result_text.strip().startswith('{'):
data = json.loads(result_text.strip())
# Handle array format
if isinstance(data, list) and len(data) > 0:
item = data[0]
if isinstance(item, dict):
# Check for nested media structure
for media_type in ['audio', 'video', 'image']:
if media_type in item and isinstance(item[media_type], dict):
if 'url' in item[media_type]:
url = item[media_type]['url'].strip('\'"')
logger.info(f"🎯 Found {media_type} URL in JSON: {url}")
return url
# Check for direct URL
if 'url' in item:
url = item['url'].strip('\'"')
logger.info(f"🎯 Found direct URL in JSON: {url}")
return url
# Handle object format
elif isinstance(data, dict):
# Check for nested media structure
for media_type in ['audio', 'video', 'image']:
if media_type in data and isinstance(data[media_type], dict):
if 'url' in data[media_type]:
url = data[media_type]['url'].strip('\'"')
logger.info(f"🎯 Found {media_type} URL in JSON: {url}")
return url
# Check for direct URL
if 'url' in data:
url = data['url'].strip('\'"')
logger.info(f"🎯 Found direct URL in JSON: {url}")
return url
except json.JSONDecodeError:
pass
# Check for Gradio file URLs (common pattern)
gradio_patterns = [
r'https://[^/]+\.hf\.space/gradio_api/file=/[^/]+/[^/]+/[^\s"\'<>,]+',
r'https://[^/]+\.hf\.space/file=[^\s"\'<>,]+',
r'/gradio_api/file=/[^\s"\'<>,]+'
]
for pattern in gradio_patterns:
match = re.search(pattern, result_text)
if match:
url = match.group(0).rstrip('\'",:;')
logger.info(f"🎯 Found Gradio file URL: {url}")
return url
# Check for any HTTP URLs with media extensions
url_pattern = r'https?://[^\s"\'<>]+\.(?:mp3|wav|ogg|m4a|flac|aac|opus|wma|mp4|webm|avi|mov|mkv|m4v|wmv|png|jpg|jpeg|gif|webp|bmp|svg)'
match = re.search(url_pattern, result_text, re.IGNORECASE)
if match:
url = match.group(0)
logger.info(f"🎯 Found media URL by extension: {url}")
return url
# Check for data URLs
if result_text.startswith('data:'):
logger.info("🎯 Found data URL")
return result_text
logger.info("❌ No media URL found in result")
return None
def _get_native_system_prompt(self) -> str:
"""Get system prompt for HF Inference without MCP servers"""
model_info = AppConfig.AVAILABLE_MODELS.get(self.mcp_client.current_model, {})
context_length = model_info.get("context_length", 128000)
return f"""You are an AI assistant powered by {self.mcp_client.current_model} via {self.mcp_client.current_provider}. You have native capabilities for:
- **Text Processing**: You can analyze, summarize, translate, and process text directly
- **General Knowledge**: You can answer questions, explain concepts, and have conversations
- **Code Analysis**: You can read, analyze, and explain code
- **Reasoning**: You can perform step-by-step reasoning and problem-solving
- **Context Window**: You have access to {context_length:,} tokens of context
Current time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
Please provide helpful, accurate, and engaging responses to user queries."""
def _get_mcp_system_prompt(self, uploaded_file_urls: List[str] = None) -> str:
"""Get enhanced system prompt for HF Inference with MCP servers"""
model_info = AppConfig.AVAILABLE_MODELS.get(self.mcp_client.current_model, {})
context_length = model_info.get("context_length", 128000)
uploaded_files_context = ""
if uploaded_file_urls:
uploaded_files_context = f"\n\nFILES UPLOADED BY USER (Public URLs accessible to MCP servers):\n"
for i, file_url in enumerate(uploaded_file_urls, 1):
file_name = file_url.split('/')[-1] if '/' in file_url else file_url
if AppConfig.is_image_file(file_url):
file_type = "Image"
elif AppConfig.is_audio_file(file_url):
file_type = "Audio"
elif AppConfig.is_video_file(file_url):
file_type = "Video"
else:
file_type = "File"
uploaded_files_context += f"{i}. {file_type}: {file_name}\n URL: {file_url}\n"
# Get available tools with correct names from enabled servers only
enabled_servers = self.mcp_client.get_enabled_servers()
tools_info = []
for server_name, config in enabled_servers.items():
tools_info.append(f"- **{server_name}**: {config.description}")
return f"""You are an AI assistant powered by {self.mcp_client.current_model} via {self.mcp_client.current_provider}, with access to various MCP tools.
YOUR NATIVE CAPABILITIES:
- **Text Processing**: You can analyze, summarize, translate, and process text directly
- **General Knowledge**: You can answer questions, explain concepts, and have conversations
- **Code Analysis**: You can read, analyze, and explain code
- **Reasoning**: You can perform step-by-step reasoning and problem-solving
- **Context Window**: You have access to {context_length:,} tokens of context
AVAILABLE MCP TOOLS:
You have access to the following MCP servers:
{chr(10).join(tools_info)}
WHEN TO USE MCP TOOLS:
- **Image Generation**: Creating new images from text prompts
- **Image Editing**: Modifying, enhancing, or transforming existing images
- **Audio Processing**: Transcribing audio, generating speech, audio enhancement
- **Video Processing**: Creating or editing videos
- **Text to Speech**: Converting text to audio
- **Specialized Analysis**: Tasks requiring specific models or APIs
TOOL USAGE FORMAT:
When you need to use an MCP tool, respond with JSON in this exact format:
{{"use_tool": true, "server": "exact_server_name", "tool": "exact_tool_name", "arguments": {{"param": "value"}}}}
IMPORTANT: Always describe what you're going to do BEFORE the JSON tool call. For example:
"I'll generate speech for your text using the TTS tool."
{{"use_tool": true, "server": "text to speech", "tool": "Kokoro_TTS_mcp_test_generate_first", "arguments": {{"text": "hello"}}}}
IMPORTANT TOOL NAME MAPPING:
- For TTS server: use tool name "Kokoro_TTS_mcp_test_generate_first"
- For image generation: use tool name "dalle_3_xl_lora_v2_generate"
- For video generation: use tool name "ysharma_ltx_video_distilledtext_to_video"
- For letter counting: use tool name "gradio_app_dummy1_letter_counter"
EXACT SERVER NAMES TO USE:
{', '.join([f'"{name}"' for name in enabled_servers.keys()])}
FILE HANDLING FOR MCP TOOLS:
When using MCP tools with uploaded files, always use the public URLs provided above.
These URLs are accessible to remote MCP servers.
{uploaded_files_context}
MEDIA HANDLING:
When tool results contain media URLs (images, audio, videos), the system will automatically embed them as playable media.
IMPORTANT NOTES:
- Always use the EXACT server names and tool names as specified above
- Use proper JSON format for tool calls
- Include all required parameters in arguments
- For file inputs to MCP tools, use the public URLs provided, not local paths
- ALWAYS provide a descriptive message before the JSON tool call
- After tool execution, you can provide additional context or ask if the user needs anything else
Current time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
Current model: {self.mcp_client.current_model} via {self.mcp_client.current_provider}"""
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