Create agent.py

agent.py

@@ -0,0 +1,361 @@
+import os
+import tempfile
+import time
+import re
+import json
+from typing import List, Optional, Dict, Any
+from urllib.parse import urlparse
+import requests
+import yt_dlp
+from bs4 import BeautifulSoup
+from difflib import SequenceMatcher
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_community.utilities import DuckDuckGoSearchAPIWrapper, WikipediaAPIWrapper
+from langchain.agents import Tool, AgentExecutor, ConversationalAgent, initialize_agent, AgentType
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import MessagesPlaceholder
+from langchain.tools import BaseTool, Tool, tool
+from google.generativeai.types import HarmCategory, HarmBlockThreshold
+from PIL import Image
+import google.generativeai as genai
+from pydantic import Field
+
+from smolagents import WikipediaSearchTool
+
+class SmolagentToolWrapper(BaseTool):
+    """Wrapper for smolagents tools to make them compatible with LangChain."""
+    
+    wrapped_tool: object = Field(description="The wrapped smolagents tool")
+    
+    def __init__(self, tool):
+        """Initialize the wrapper with a smolagents tool."""
+        super().__init__(
+            name=tool.name,
+            description=tool.description,
+            return_direct=False,
+            wrapped_tool=tool
+        )
+
+    def _run(self, query: str) -> str:
+        """Use the wrapped tool to execute the query."""
+        try:
+            # For WikipediaSearchTool
+            if hasattr(self.wrapped_tool, 'search'):
+                return self.wrapped_tool.search(query)
+            # For DuckDuckGoSearchTool and others
+            return self.wrapped_tool(query)
+        except Exception as e:
+            return f"Error using tool: {str(e)}"
+    
+    def _arun(self, query: str) -> str:
+        """Async version - just calls sync version since smolagents tools don't support async."""
+        return self._run(query)
+
+
+
+class BasicAgent:
+    def __init__(self, api_key: str, model_name: str = "gemini-2.0-flash"):
+        # Suppress warnings
+        import warnings
+        warnings.filterwarnings("ignore", category=UserWarning)
+        warnings.filterwarnings("ignore", category=DeprecationWarning)
+        warnings.filterwarnings("ignore", message=".*will be deprecated.*")
+        warnings.filterwarnings("ignore", "LangChain.*")
+        
+        self.api_key = api_key
+        self.model_name = model_name
+        
+        # Configure Gemini
+        genai.configure(api_key=api_key)
+        
+        # Initialize the LLM
+        self.llm = self._setup_llm()
+        
+        # Setup tools
+        self.tools = [
+            SmolagentToolWrapper(WikipediaSearchTool()),
+            Tool(
+                name="analyze_video",
+                func=self._analyze_video,
+                description="Analyze YouTube video content directly"
+            ),
+            Tool(
+                name="analyze_image",
+                func=self._analyze_image,
+                description="Analyze image content"
+            ),
+            Tool(
+                name="analyze_table",
+                func=self._analyze_table,
+                description="Analyze table or matrix data"
+            ),
+            Tool(
+                name="analyze_list",
+                func=self._analyze_list,
+                description="Analyze and categorize list items"
+            ),
+            Tool(
+                name="web_search",
+                func=self._web_search,
+                description="Search the web for information"
+            )
+        ]
+        
+        # Setup memory
+        self.memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            return_messages=True
+        )
+        
+        # Initialize agent
+        self.agent = self._setup_agent()
+       
+
+    def run(self, query: str) -> str:
+        """Run the agent on a query with incremental retries."""
+        max_retries = 3
+        base_sleep = 1  # Start with 1 second sleep
+        
+        for attempt in range(max_retries):
+            try:
+
+                # If no match found in answer bank, use the agent
+                response = self.agent.run(query)
+                return response
+
+            except Exception as e:
+                sleep_time = base_sleep * (attempt + 1)  # Incremental sleep: 1s, 2s, 3s
+                if attempt < max_retries - 1:
+                    print(f"Attempt {attempt + 1} failed. Retrying in {sleep_time} seconds...")
+                    time.sleep(sleep_time)
+                    continue
+                return f"Error processing query after {max_retries} attempts: {str(e)}"
+
+        print("Agent processed all queries!")
+
+    def _clean_response(self, response: str) -> str:
+        """Clean up the response from the agent."""
+        # Remove any tool invocation artifacts
+        cleaned = re.sub(r'> Entering new AgentExecutor chain...|> Finished chain.', '', response)
+        cleaned = re.sub(r'Thought:.*?Action:.*?Action Input:.*?Observation:.*?\n', '', cleaned, flags=re.DOTALL)
+        return cleaned.strip()
+
+    def run_interactive(self):
+        print("AI Assistant Ready! (Type 'exit' to quit)")
+        
+        while True:
+            query = input("You: ").strip()
+            if query.lower() == 'exit':
+                print("Goodbye!")
+                break
+            
+            print("Assistant:", self.run(query))
+
+    def _web_search(self, query: str, domain: Optional[str] = None) -> str:
+        """Perform web search with rate limiting and retries."""
+        try:
+            # Use DuckDuckGo API wrapper for more reliable results
+            search = DuckDuckGoSearchAPIWrapper(max_results=5)
+            results = search.run(f"{query} {f'site:{domain}' if domain else ''}")
+            
+            if not results or results.strip() == "":
+                return "No search results found."
+                
+            return results
+
+        except Exception as e:
+            return f"Search error: {str(e)}"
+
+    def _analyze_video(self, url: str) -> str:
+        """Analyze video content using Gemini's video understanding capabilities."""
+        try:
+            # Validate URL
+            parsed_url = urlparse(url)
+            if not all([parsed_url.scheme, parsed_url.netloc]):
+                return "Please provide a valid video URL with http:// or https:// prefix."
+            
+            # Check if it's a YouTube URL
+            if 'youtube.com' not in url and 'youtu.be' not in url:
+                return "Only YouTube videos are supported at this time."
+
+            try:
+                # Configure yt-dlp with minimal extraction
+                ydl_opts = {
+                    'quiet': True,
+                    'no_warnings': True,
+                    'extract_flat': True,
+                    'no_playlist': True,
+                    'youtube_include_dash_manifest': False
+                }
+
+                with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+                    try:
+                        # Try basic info extraction
+                        info = ydl.extract_info(url, download=False, process=False)
+                        if not info:
+                            return "Could not extract video information."
+
+                        title = info.get('title', 'Unknown')
+                        description = info.get('description', '')
+                        
+                        # Create a detailed prompt with available metadata
+                        prompt = f"""Please analyze this YouTube video:
+Title: {title}
+URL: {url}
+Description: {description}
+Please provide a detailed analysis focusing on:
+1. Main topic and key points from the title and description
+2. Expected visual elements and scenes
+3. Overall message or purpose
+4. Target audience"""
+
+                        # Use the LLM with proper message format
+                        messages = [HumanMessage(content=prompt)]
+                        response = self.llm.invoke(messages)
+                        return response.content if hasattr(response, 'content') else str(response)
+
+                    except Exception as e:
+                        if 'Sign in to confirm' in str(e):
+                            return "This video requires age verification or sign-in. Please provide a different video URL."
+                        return f"Error accessing video: {str(e)}"
+
+            except Exception as e:
+                return f"Error extracting video info: {str(e)}"
+
+        except Exception as e:
+            return f"Error analyzing video: {str(e)}"
+
+    def _analyze_table(self, table_data: str) -> str:
+        """Analyze table or matrix data."""
+        try:
+            if not table_data or not isinstance(table_data, str):
+                return "Please provide valid table data for analysis."
+
+            prompt = f"""Please analyze this table:
+{table_data}
+Provide a detailed analysis including:
+1. Structure and format
+2. Key patterns or relationships
+3. Notable findings
+4. Any mathematical properties (if applicable)"""
+
+            messages = [HumanMessage(content=prompt)]
+            response = self.llm.invoke(messages)
+            return response.content if hasattr(response, 'content') else str(response)
+
+        except Exception as e:
+            return f"Error analyzing table: {str(e)}"
+
+    def _analyze_image(self, image_data: str) -> str:
+        """Analyze image content."""
+        try:
+            if not image_data or not isinstance(image_data, str):
+                return "Please provide a valid image for analysis."
+
+            prompt = f"""Please analyze this image:
+{image_data}
+Focus on:
+1. Visual elements and objects
+2. Colors and composition
+3. Text or numbers (if present)
+4. Overall context and meaning"""
+
+            messages = [HumanMessage(content=prompt)]
+            response = self.llm.invoke(messages)
+            return response.content if hasattr(response, 'content') else str(response)
+
+        except Exception as e:
+            return f"Error analyzing image: {str(e)}"
+
+    def _analyze_list(self, list_data: str) -> str:
+        """Analyze and categorize list items."""
+        if not list_data:
+            return "No list data provided."
+        try:
+            items = [x.strip() for x in list_data.split(',')]
+            if not items:
+                return "Please provide a comma-separated list of items."
+            # Add list analysis logic here
+            return "Please provide the list items for analysis."
+        except Exception as e:
+            return f"Error analyzing list: {str(e)}"
+
+    def _setup_llm(self):
+        """Set up the language model."""
+        # Set up model with video capabilities
+        generation_config = {
+            "temperature": 0.0,
+            "max_output_tokens": 2000,
+            "candidate_count": 1,
+        }
+        
+        safety_settings = {
+            HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+            HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+            HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+            HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+        }
+        
+        return ChatGoogleGenerativeAI(
+            model="gemini-2.0-flash",
+            google_api_key=self.api_key,
+            temperature=0,
+            max_output_tokens=2000,
+            generation_config=generation_config,
+            safety_settings=safety_settings,
+            system_message=SystemMessage(content=(
+                "You are a precise AI assistant that helps users find information and analyze content. "
+                "You can directly understand and analyze YouTube videos, images, and other content. "
+                "When analyzing videos, focus on relevant details like dialogue, text, and key visual elements. "
+                "For lists, tables, and structured data, ensure proper formatting and organization. "
+                "If you need additional context, clearly explain what is needed."
+            ))
+        )
+        
+    def _setup_agent(self) -> AgentExecutor:
+        """Set up the agent with tools and system message."""
+        
+        # Define the system message template
+        PREFIX = """You are a helpful AI assistant that can use various tools to answer questions and analyze content. You have access to tools for web search, Wikipedia lookup, and multimedia analysis.
+TOOLS:
+------
+You have access to the following tools:"""
+
+        FORMAT_INSTRUCTIONS = """To use a tool, use the following format:
+Thought: Do I need to use a tool? Yes
+Action: the action to take, should be one of [{tool_names}]
+Action Input: the input to the action
+Observation: the result of the action
+When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
+Thought: Do I need to use a tool? No
+Final Answer: [your response here]
+Begin! Remember to ALWAYS include 'Thought:', 'Action:', 'Action Input:', and 'Final Answer:' in your responses."""
+
+        SUFFIX = """Previous conversation history:
+{chat_history}
+New question: {input}
+{agent_scratchpad}"""
+
+        # Create the base agent
+        agent = ConversationalAgent.from_llm_and_tools(
+            llm=self.llm,
+            tools=self.tools,
+            prefix=PREFIX,
+            format_instructions=FORMAT_INSTRUCTIONS,
+            suffix=SUFFIX,
+            input_variables=["input", "chat_history", "agent_scratchpad", "tool_names"],
+            handle_parsing_errors=True
+        )
+
+        # Initialize agent executor with custom output handling
+        return AgentExecutor.from_agent_and_tools(
+            agent=agent,
+            tools=self.tools,
+            memory=self.memory,
+            max_iterations=5,
+            verbose=True,
+            handle_parsing_errors=True,
+            return_only_outputs=True  # This ensures we only get the final output
+        )