Update tools.py

tools.py

@@ -1,697 +1,434 @@
-import os
-import re
-from pathlib import Path
-from typing import Optional, Union, Dict, List, Any
-from enum import Enum
-import requests
-import tempfile
-import ast
-
-from dotenv import load_dotenv
-from langgraph.graph import StateGraph, END
-from langchain.tools import Tool as LangTool
-from langchain_core.runnables import RunnableLambda
-from pathlib import Path
-
-# llm
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from langchain_community.llms import HuggingFacePipeline
-import torch
-
-from langchain.tools import StructuredTool
-
-from tools import (
-    EnhancedSearchTool,
-    EnhancedWikipediaTool,
-    excel_to_markdown,
-    image_file_info,
-    audio_file_info,
-    code_file_read,
-    extract_youtube_info)
-
-# llm_config.py
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from langchain_community.llms import HuggingFacePipeline
-import torch
-
-# Load environment variables
-load_dotenv()
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-QUESTIONS_URL = f"{DEFAULT_API_URL}/questions"
-SUBMIT_URL = f"{DEFAULT_API_URL}/submit"
-FILE_PATH = f"{DEFAULT_API_URL}/files/"
-
-# Initialize LLM
-llm = ChatGoogleGenerativeAI(
-    model=os.getenv("GEMINI_MODEL", "gemini-pro"),
-    google_api_key=os.getenv("GEMINI_API_KEY")
-)
-
-# ----------- Enhanced State Management -----------
-from typing import TypedDict
-
-class AgentState(TypedDict):
-    """Enhanced state tracking for the agent - using TypedDict for LangGraph compatibility"""
-    question: str
-    original_question: str
-    conversation_history: List[Dict[str, str]]
-    selected_tools: List[str]
-    tool_results: Dict[str, Any]
-    final_answer: str
-    current_step: str
-    error_count: int
-    max_errors: int
-
-class AgentStep(Enum):
-    ANALYZE_QUESTION = "analyze_question"
-    SELECT_TOOLS = "select_tools"
-    EXECUTE_TOOLS = "execute_tools"
-    SYNTHESIZE_ANSWER = "synthesize_answer"
-    ERROR_RECOVERY = "error_recovery"
-    COMPLETE = "complete"
-
-# ----------- Helper Functions -----------
-def initialize_state(question: str) -> AgentState:
-    """Initialize agent state with default values"""
-    return {
-        "question": question,
-        "original_question": question,
-        "conversation_history": [],
-        "selected_tools": [],
-        "tool_results": {},
-        "final_answer": "",
-        "current_step": "start",
-        "error_count": 0,
-        "max_errors": 3
-    }
-
-# Initialize vanilla tools
 from langchain.tools import DuckDuckGoSearchResults, WikipediaQueryRun
 from langchain.utilities import WikipediaAPIWrapper
+from PIL import Image
+import re
+import time
+import json
+import pandas as pd
+from pathlib import Path
+from typing import List, Dict, Optional, Union
+from tabulate import tabulate
+import whisper
 
-duckduckgo_tool = DuckDuckGoSearchResults()
-wiki_tool = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
-
-
-# Initialize enhanced tools
-enhanced_search_tool = LangTool.from_function(
-    name="enhanced_web_search",
-    func=EnhancedSearchTool().run,
-    description="Enhanced web search with intelligent query processing, multiple search strategies, and result filtering. Provides comprehensive and relevant search results."
-)
-
-enhanced_wiki_tool = LangTool.from_function(
-    name="enhanced_wikipedia",
-    func=EnhancedWikipediaTool().run,
-    description="Enhanced Wikipedia search with entity extraction, multi-term search, and relevant content filtering. Provides detailed encyclopedic information."
-)
-
-excel_tool = StructuredTool.from_function(
-    name="excel_to_text",
-    func=excel_to_markdown,
-    description="Enhanced Excel analysis with metadata, statistics, and structured data preview. Inputs: 'excel_path' (str), 'sheet_name' (str, optional).",
-)
-
-image_tool = StructuredTool.from_function(
-    name="image_file_info",
-    func=image_file_info,
-    description="Enhanced image file analysis with detailed metadata and properties."
-)
-
-audio_tool = LangTool.from_function(
-    name="audio_file_info",
-    func=audio_file_info,
-    description="Enhanced audio processing with transcription, language detection, and timestamped segments."
-)
-
-code_tool = LangTool.from_function(
-    name="code_file_read",
-    func=code_file_read,
-    description="Enhanced code file analysis with language-specific insights and structure analysis."
-)
-
-youtube_tool = LangTool.from_function(
-    name="extract_youtube_info",
-    func=extract_youtube_info,
-    description="Extracts transcription from the youtube link"
-)
-
-# Enhanced tool registry
-AVAILABLE_TOOLS = {
-    "excel": excel_tool,
-    "search": wiki_tool,
-    "wikipedia": duckduckgo_tool,
-    "image": image_tool,
-    "audio": audio_tool,
-    "code": code_tool,
-    "youtube": youtube_tool
-}
-
-# ----------- Intelligent Tool Selection -----------
-def analyze_question(state: AgentState) -> AgentState:
-    """Enhanced question analysis with better tool recommendation"""
-    analysis_prompt = f"""
-    Analyze this question and determine the best tools and approach:
-    Question: {state["question"]}
-    
-    Available enhanced tools:
-    1. excel - Enhanced Excel/CSV analysis with statistics and metadata
-    2. search - Enhanced web search with intelligent query processing and result filtering
-    3. wikipedia - Enhanced Wikipedia search with entity extraction and content filtering
-    4. image - Enhanced image analysis with what the image contains
-    5. audio - Enhanced audio processing with transcription
-    6. code - Enhanced code analysis with language-specific insights
-    7. youtube - Extracts transcription from the youtube link
-    
-    Consider:
-    - Question type (factual, analytical, current events, technical)
-    - Required information sources (files, web, encyclopedic)
-    - Time sensitivity (current vs historical information)
-    - Complexity level
-    
-    Respond with:
-    1. Question type: <type>
-    2. Primary tools needed: <tools>
-    3. Search strategy: <strategy>
-    4. Expected answer format: <format>
-    
-    Format: TYPE: <type> | TOOLS: <tools> | STRATEGY: <strategy> | FORMAT: <format>
-    """
-    
-    try:
-        response = llm.invoke(analysis_prompt).content
-        state["conversation_history"].append({"role": "analysis", "content": response})
-        state["current_step"] = AgentStep.SELECT_TOOLS.value
-    except Exception as e:
-        state["error_count"] += 1
-        state["conversation_history"].append({"role": "error", "content": f"Analysis failed: {e}"})
-        state["current_step"] = AgentStep.ERROR_RECOVERY.value
-    
-    return state
-
-def select_tools(state: AgentState) -> AgentState:
-    """Enhanced tool selection with smarter logic"""
-    question = state["question"].lower()
-    selected_tools = []
-
-    # File-based tool selection
-    if any(keyword in question for keyword in ["excel", "csv", "spreadsheet", ".xlsx", ".xls"]):
-        selected_tools.append("excel")
-    if any(keyword in question for keyword in [".png", ".jpg", ".jpeg", ".bmp", ".gif", "image"]):
-        selected_tools.append("image")
-    if any(keyword in question for keyword in [".mp3", ".wav", ".ogg", "audio", "transcribe"]):
-        selected_tools.append("audio")
-    if any(keyword in question for keyword in [".py", ".ipynb", "code", "script", "function"]):
-        selected_tools.append("code")
-    if any(keyword in question for keyword in ["youtube"]):
-        selected_tools.append("youtube")
-
-    print(f"File-based tools selected: {selected_tools}")
-
-    tools_prompt = f"""
-    You are a smart assistant that selects relevant tools based on the user's natural language question.
-
-    Available tools:
-    - "search" → Use for real-time, recent, or broad web information.
-    - "wikipedia" → Use for factual or encyclopedic knowledge.
-    - "excel" → Use for spreadsheet-related questions (.xlsx, .csv).
-    - "image" → Use for image files (.png, .jpg, etc.) or image-based tasks.
-    - "audio" → Use for sound files (.mp3, .wav, etc.) or transcription.
-    - "code" → Use for programming-related questions or when files like .py are mentioned.
-    - "youtube" → Use for questions involving YouTube videos.
-
-    Return the result as a **Python list of strings**, no explanation. Use only the relevant tools.
-    If not relevant tool is found, return an empty list such as [].
-
-    ### Examples:
-
-    Q: "Show me recent news about elections in 2025"
-    A: ["search"]
-
-    Q: "Summarize this Wikipedia article about Einstein"
-    A: ["wikipedia"]
-
-    Q: "Analyze this .csv file"
-    A: ["excel"]
-
-    Q: "Transcribe this .wav audio file"
-    A: ["audio"]
-
-    Q: "Generate Python code from this prompt"
-    A: ["code"]
-
-    Q: "Who was the president of USA in 1945?"
-    A: ["wikipedia"]
-
-    Q: "Give me current weather updates"
-    A: ["search"]
-
-    Q: "Look up the history of space exploration"
-    A: ["search", "wikipedia"]
-
-    Q: "What is 2 + 2?"
-    A: []
-
-    ### Now answer:
+import numpy as np
+import os
 
-    Q: {state["question"]}
-    A:
-    """
+# ----------- Enhanced Search Functionality -----------
+class EnhancedSearchTool:
+    """Enhanced web search with intelligent query processing and result filtering"""
     
-    llm_tools = ast.literal_eval(llm.invoke(tools_prompt).content.strip())
-    if not isinstance(llm_tools, list):
-        llm_tools = []
-    print(f"LLM suggested tools: {llm_tools}")
-    selected_tools.extend(llm_tools)
-    selected_tools = list(set(selected_tools))  # Remove duplicates
-
-    print(f"Final selected tools after LLM suggestion: {selected_tools}")
-
-
-    # # Information-based tool selection
-    # current_indicators = ["recent", "current", "news", "today", "2025", "now"]
-    # encyclopedia_indicators = ["wiki", "wikipedia"]
+    def __init__(self, max_results: int = 10):
+        self.base_tool = DuckDuckGoSearchResults(num_results=max_results)
+        self.max_results = max_results
+        
+    def _extract_key_terms(self, question: str) -> List[str]:
+        """Extract key search terms from the question using LLM"""
+        try:
+            extract_prompt = f"""
+            Extract the most important search terms from this question for web search:
+            Question: {question}
+            
+            Return ONLY a comma-separated list of key terms, no explanations.
+            Focus on: proper nouns, specific concepts, technical terms, dates, numbers.
+            Avoid: common words like 'what', 'how', 'when', 'the', 'is', 'are'.
+            
+            Example: "What is the population of Tokyo in 2023?" -> "Tokyo population 2023"
+            """
+            
+            response = llm.invoke(extract_prompt).content.strip()
+            return [term.strip() for term in response.split(',')]
+        except Exception:
+            # Fallback to simple keyword extraction
+            return self._simple_keyword_extraction(question)
+    
+    def _simple_keyword_extraction(self, question: str) -> List[str]:
+        """Fallback keyword extraction using regex"""
+        # Remove common question words
+        stop_words = {'what', 'how', 'when', 'where', 'why', 'who', 'which', 'the', 'is', 'are', 'was', 'were', 'do', 'does', 'did', 'can', 'could', 'should', 'would'}
+        words = re.findall(r'\b[A-Za-z]+\b', question.lower())
+        return [word for word in words if word not in stop_words and len(word) > 2]
+    
+    def _generate_search_queries(self, question: str) -> List[str]:
+        """Generate multiple search queries for comprehensive results"""
+        key_terms = self._extract_key_terms(question)
+        
+        queries = []
+        
+        # Original question (cleaned)
+        cleaned_question = re.sub(r'[^\w\s]', ' ', question).strip()
+        queries.append(cleaned_question)
+        
+        # Key terms combined
+        if key_terms:
+            queries.append(' '.join(key_terms[:5]))  # Top 5 terms
+        
+        # Specific query patterns based on question type
+        if any(word in question.lower() for word in ['latest', 'recent', 'current', 'new']):
+            queries.append(f"{' '.join(key_terms[:3])} 2024 2025")
+        
+        if any(word in question.lower() for word in ['statistics', 'data', 'number', 'count']):
+            queries.append(f"{' '.join(key_terms[:3])} statistics data")
+        
+        if any(word in question.lower() for word in ['definition', 'what is', 'meaning']):
+            queries.append(f"{' '.join(key_terms[:2])} definition meaning")
+        
+        return list(dict.fromkeys(queries))  # Remove duplicates while preserving order
     
-    # if any(indicator in question for indicator in current_indicators):
-    #     selected_tools.append("search")
-    # elif any(indicator in question for indicator in encyclopedia_indicators):
-    #     selected_tools.append("wikipedia")
-    # elif any(keyword in question for keyword in ["search", "find", "look up", "information about"]):
-    #     # Use both for comprehensive coverage
-    #     selected_tools.extend(["search", "wikipedia"])
-
-    # # Default fallback
-    # if not selected_tools:
-    #     if any(word in question for word in ["who", "what", "when", "where"]):
-    #         selected_tools.append("wikipedia")
-    #         selected_tools.append("search")
-
-    # # Remove duplicates while preserving order
-    # selected_tools = list(dict.fromkeys(selected_tools))
+    def _filter_and_rank_results(self, results: List[Dict], question: str) -> List[Dict]:
+        """Filter and rank search results based on relevance"""
+        if not results:
+            return results
+        
+        key_terms = self._extract_key_terms(question)
+        key_terms_lower = [term.lower() for term in key_terms]
+        
+        scored_results = []
+        for result in results:
+            score = 0
+            text_content = (result.get('snippet', '') + ' ' + result.get('title', '')).lower()
+            
+            # Score based on key term matches
+            for term in key_terms_lower:
+                if term in text_content:
+                    score += text_content.count(term)
+            
+            # Bonus for recent dates
+            if any(year in text_content for year in ['2024', '2025', '2023']):
+                score += 2
+            
+            # Penalty for very short snippets
+            if len(result.get('snippet', '')) < 50:
+                score -= 1
+            
+            scored_results.append((score, result))
+        
+        # Sort by score and return top results
+        scored_results.sort(key=lambda x: x[0], reverse=True)
+        return [result for score, result in scored_results[:self.max_results]]
     
-    state["selected_tools"] = selected_tools
-    state["current_step"] = AgentStep.EXECUTE_TOOLS.value
-    return state
-
-def execute_tools(state: AgentState) -> AgentState:
-    """Enhanced tool execution with better error handling"""
-    results = {}
-
-    # Enhanced file detection
-    file_path = None
-    downloaded_file_marker = "A file was downloaded for this task and saved locally at:"
-    if downloaded_file_marker in state["question"]:
-        lines = state["question"].splitlines()
-        for i, line in enumerate(lines):
-            if downloaded_file_marker in line:
-                if i + 1 < len(lines):
-                    file_path_candidate = lines[i + 1].strip()
-                    if Path(file_path_candidate).exists():
-                        file_path = file_path_candidate
-                        print(f"Detected file path: {file_path}")
-                break
-
-    for tool_name in state["selected_tools"]:
+    def run(self, question: str) -> str:
+        """Enhanced search execution with multiple queries and result filtering"""
         try:
-            print(f"Executing tool: {tool_name}")
+            search_queries = self._generate_search_queries(question)
+            all_results = []
+            
+            for query in search_queries[:3]:  # Limit to 3 queries to avoid rate limits
+                try:
+                    results = self.base_tool.run(query)
+                    if isinstance(results, str):
+                        # Parse string results if needed
+                        try:
+                            results = json.loads(results) if results.startswith('[') else [{'snippet': results, 'title': 'Search Result'}]
+                        except:
+                            results = [{'snippet': results, 'title': 'Search Result'}]
+                    
+                    if isinstance(results, list):
+                        all_results.extend(results)
+                    
+                    time.sleep(0.5)  # Rate limiting
+                except Exception as e:
+                    print(f"Search query failed: {query} - {e}")
+                    continue
+            
+            if not all_results:
+                return "No search results found."
             
-            # File-based tools
-            if tool_name in ["excel", "image", "audio", "code"] and file_path:
-                if tool_name == "excel":
-                    result = AVAILABLE_TOOLS["excel"].run({"excel_path": file_path, "sheet_name": None})
-                elif tool_name == "image":
-                    result = AVAILABLE_TOOLS["image"].run({"image_path": file_path, "question": state["question"]})
-                elif tool_name == "youtube":
-                    print(f"Running YouTube tool with file path: {file_path}")
-                    result = AVAILABLE_TOOLS["youtube"].run(state["question"])
-                else:
-                    result = AVAILABLE_TOOLS[tool_name].run(file_path)
-            # Information-based tools
-            else:
-                # Extract clean query for search tools
-                clean_query = state["question"]
-                if downloaded_file_marker in clean_query:
-                    clean_query = clean_query.split(downloaded_file_marker)[0].strip()
+            # Filter and rank results
+            filtered_results = self._filter_and_rank_results(all_results, question)
+            
+            # Format results
+            formatted_results = []
+            for i, result in enumerate(filtered_results[:5], 1):
+                title = result.get('title', 'No title')
+                snippet = result.get('snippet', 'No description')
+                link = result.get('link', '')
                 
-                result = AVAILABLE_TOOLS[tool_name].run(clean_query)
-
-            results[tool_name] = result
-
-            print(f"Tool {tool_name} completed successfully.")
-            print(f"Output for {tool_name}: {result}")
+                formatted_results.append(f"{i}. {title}\n   {snippet}\n   Source: {link}\n")
+            
+            return "ENHANCED SEARCH RESULTS:\n" + "\n".join(formatted_results)
             
         except Exception as e:
-            error_msg = f"Error using {tool_name}: {str(e)}"
-            results[tool_name] = error_msg
-            state["error_count"] += 1
-            print(error_msg)
-
-    state["tool_results"] = results
-    state["current_step"] = AgentStep.SYNTHESIZE_ANSWER.value
-    return state
-
-def synthesize_answer(state: AgentState) -> AgentState:
-    """Enhanced answer synthesis with better formatting"""
-
-    tool_results_str = "\n".join([f"=== {tool.upper()} RESULTS ===\n{result}\n" for tool, result in state["tool_results"].items()])
-
-    cot_prompt = f"""You are a precise assistant tasked with analyzing the user's question{" using the available tool outputs" if state["tool_results"] else ""}.
-
-    Question:
-    {state["question"]}
+            return f"Enhanced search error: {str(e)}"
 
-    {f"Available tool outputs: {tool_results_str}" if state["tool_results"] else ""}
-
-    Instructions:
-    - Think step-by-step to determine the best strategy to answer the question.
-    - Use only the given information; do not hallucinate or infer from external knowledge.
-    - If decoding, logical deduction, counting, or interpretation is required, show each step clearly.
-    - If any part of the tool output is unclear or incomplete, mention it and its impact.
-    - Do not guess. If the information is insufficient, say so clearly.
-    - Finish with a clearly marked line: `---END OF ANALYSIS---`
-
-    Your step-by-step analysis:"""
-
-    cot_response = llm.invoke(cot_prompt).content
-
-    final_answer_prompt = f"""You are a precise assistant tasked with deriving the **final answer** from the step-by-step analysis below.
-
-    Question:
-    {state["question"]}
-
-    Step-by-step analysis:
-    {cot_response}
-
-    Instructions:
-    - Read the analysis thoroughly before responding.
-    - Output ONLY the final answer. Do NOT include any reasoning or explanation.
-    - Remove any punctuation at the corners of the answer unless it is explicitly mentioned in the question.
-    - The answer must be concise and factual.
-    - If the analysis concluded that a definitive answer cannot be determined, respond with: `NA` (exactly).
-
-    Final answer:"""
-
-        
-    try:
-        response = llm.invoke(final_answer_prompt).content
-        state["final_answer"] = response
-        state["current_step"] = AgentStep.COMPLETE.value
-    except Exception as e:
-        state["error_count"] += 1
-        state["final_answer"] = f"Error synthesizing answer: {e}"
-        state["current_step"] = AgentStep.ERROR_RECOVERY.value
+# ----------- Enhanced Wikipedia Tool -----------
+class EnhancedWikipediaTool:
+    """Enhanced Wikipedia search with intelligent query processing and content extraction"""
     
-    return state
-
-def error_recovery(state: AgentState) -> AgentState:
-    """Enhanced error recovery with multiple fallback strategies"""
-    if state["error_count"] >= state["max_errors"]:
-        state["final_answer"] = "I encountered multiple errors and cannot complete this task reliably."
-        state["current_step"] = AgentStep.COMPLETE.value
-    else:
-        # Enhanced fallback: try with simplified approach
+    def __init__(self):
+        self.base_wrapper = WikipediaAPIWrapper(
+            top_k_results=3,
+            doc_content_chars_max=3000,
+            load_all_available_meta=True
+        )
+        self.base_tool = WikipediaQueryRun(api_wrapper=self.base_wrapper)
+    
+    def _extract_entities(self, question: str) -> List[str]:
+        """Extract named entities for Wikipedia search"""
         try:
-            fallback_prompt = f"""
-            Answer this question directly using your knowledge:
-            {state["original_question"]}
+            entity_prompt = f"""
+            Extract named entities (people, places, organizations, concepts) from this question for Wikipedia search:
+            Question: {question}
+            
+            Return ONLY a comma-separated list of the most important entities.
+            Focus on: proper nouns, specific names, places, organizations, historical events, scientific concepts.
             
-            Provide a helpful response even if you cannot access external tools.
-            Be clear about any limitations in your answer.
+            Example: "Tell me about Einstein's theory of relativity" -> "Albert Einstein, theory of relativity, relativity"
             """
-            response = llm.invoke(fallback_prompt).content
-            state["final_answer"] = f"Using available knowledge (some tools unavailable): {response}"
-            state["current_step"] = AgentStep.COMPLETE.value
-        except Exception as e:
-            state["final_answer"] = f"All approaches failed. Error: {e}"
-            state["current_step"] = AgentStep.COMPLETE.value
-    
-    return state
-
-# ----------- Enhanced LangGraph Workflow -----------
-def route_next_step(state: AgentState) -> str:
-    """Route to next step based on current state"""
-    step_routing = {
-        "start": AgentStep.ANALYZE_QUESTION.value,
-        AgentStep.ANALYZE_QUESTION.value: AgentStep.SELECT_TOOLS.value,
-        AgentStep.SELECT_TOOLS.value: AgentStep.EXECUTE_TOOLS.value,
-        AgentStep.EXECUTE_TOOLS.value: AgentStep.SYNTHESIZE_ANSWER.value,
-        AgentStep.SYNTHESIZE_ANSWER.value: AgentStep.COMPLETE.value,
-        AgentStep.ERROR_RECOVERY.value: AgentStep.COMPLETE.value,
-        AgentStep.COMPLETE.value: END,
-    }
+            
+            response = llm.invoke(entity_prompt).content.strip()
+            entities = [entity.strip() for entity in response.split(',')]
+            return [e for e in entities if len(e) > 2]
+        except Exception:
+            # Fallback: extract capitalized words and phrases
+            return self._extract_capitalized_terms(question)
+    
+    def _extract_capitalized_terms(self, question: str) -> List[str]:
+        """Fallback: extract capitalized terms as potential entities"""
+        # Find capitalized words and phrases
+        capitalized_words = re.findall(r'\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b', question)
+        # Also look for quoted terms
+        quoted_terms = re.findall(r'"([^"]+)"', question)
+        quoted_terms.extend(re.findall(r"'([^']+)'", question))
+        
+        return capitalized_words + quoted_terms
     
-    return step_routing.get(state["current_step"], END)
-
-# Create enhanced workflow
-workflow = StateGraph(AgentState)
-
-# Add nodes
-workflow.add_node("analyze_question", RunnableLambda(analyze_question))
-workflow.add_node("select_tools", RunnableLambda(select_tools))
-workflow.add_node("execute_tools", RunnableLambda(execute_tools))
-workflow.add_node("synthesize_answer", RunnableLambda(synthesize_answer))
-workflow.add_node("error_recovery", RunnableLambda(error_recovery))
-
-# Set entry point
-workflow.set_entry_point("analyze_question")
-
-# Add conditional edges
-workflow.add_conditional_edges(
-    "analyze_question",
-    lambda state: "select_tools" if state["current_step"] == AgentStep.SELECT_TOOLS.value else "error_recovery"
-)
-workflow.add_edge("select_tools", "execute_tools")
-workflow.add_conditional_edges(
-    "execute_tools",
-    lambda state: "synthesize_answer" if state["current_step"] == AgentStep.SYNTHESIZE_ANSWER.value else "error_recovery"
-)
-workflow.add_conditional_edges(
-    "synthesize_answer",
-    lambda state: END if state["current_step"] == AgentStep.COMPLETE.value else "error_recovery"
-)
-workflow.add_edge("error_recovery", END)
-
-# Compile the enhanced graph
-graph = workflow.compile()
-
-# ----------- Agent Class -----------
-class GaiaAgent:
-    """GAIA Agent with tools and intelligent processing"""
+    def _search_multiple_terms(self, entities: List[str]) -> Dict[str, str]:
+        """Search Wikipedia for multiple entities and return best results"""
+        results = {}
+        
+        for entity in entities[:3]:  # Limit to avoid too many API calls
+            try:
+                result = self.base_tool.run(entity)
+                if result and "Page:" in result and len(result) > 100:
+                    results[entity] = result
+                time.sleep(0.5)  # Rate limiting
+            except Exception as e:
+                print(f"Wikipedia search failed for '{entity}': {e}")
+                continue
+        
+        return results
     
-    def __init__(self):
-        self.graph = graph
-        self.tool_usage_stats = {}
-        print("Enhanced GAIA Agent initialized with:")
-        print("✓ Intelligent multi-query web search")
-        print("✓ Entity-aware Wikipedia search")
-        print("✓ Enhanced file processing tools")
-        print("✓ Advanced error recovery")
-        print("✓ Comprehensive result synthesis")
-
-    def get_tool_stats(self) -> Dict[str, int]:
-        """Get usage statistics for tools"""
-        return self.tool_usage_stats.copy()
-
-    def __call__(self, task_id: str, question: str) -> str:
-        print(f"\n{'='*60}")
-        print(f"[{task_id}] ENHANCED PROCESSING: {question}")
+    def _extract_relevant_sections(self, content: str, question: str) -> str:
+        """Extract the most relevant sections from Wikipedia content"""
+        if not content or len(content) < 200:
+            return content
+        
+        # Split content into sections (usually separated by double newlines)
+        sections = re.split(r'\n\s*\n', content)
+        
+        # Score sections based on relevance to question
+        key_terms = self._extract_entities(question)
+        key_terms_lower = [term.lower() for term in key_terms]
+        
+        scored_sections = []
+        for section in sections:
+            if len(section.strip()) < 50:
+                continue
+                
+            score = 0
+            section_lower = section.lower()
+            
+            # Score based on key term matches
+            for term in key_terms_lower:
+                score += section_lower.count(term)
+            
+            # Bonus for sections with dates, numbers, or specific facts
+            if re.search(r'\b(19|20)\d{2}\b', section):  # Years
+                score += 1
+            if re.search(r'\b\d+([.,]\d+)?\s*(million|billion|thousand|percent|%)\b', section):
+                score += 1
+            
+            scored_sections.append((score, section))
+        
+        # Sort by relevance and take top sections
+        scored_sections.sort(key=lambda x: x[0], reverse=True)
+        top_sections = [section for score, section in scored_sections[:3] if score > 0]
         
-        # Initialize state
-        processed_question = process_file(task_id, question)
-        initial_state = initialize_state(processed_question)
+        if not top_sections:
+            # If no highly relevant sections, take first few sections
+            top_sections = sections[:2]
         
+        return '\n\n'.join(top_sections)
+    
+    def run(self, question: str) -> str:
+        """Enhanced Wikipedia search with entity extraction and content filtering"""
         try:
-            # Execute the enhanced workflow
-            result = self.graph.invoke(initial_state)
+            entities = self._extract_entities(question)
             
-            # Extract results
-            answer = result.get("final_answer", "No answer generated")
-            selected_tools = result.get("selected_tools", [])
-            conversation_history = result.get("conversation_history", [])
-            tool_results = result.get("tool_results", {})
-            error_count = result.get("error_count", 0)
+            if not entities:
+                # Fallback to direct search with cleaned question
+                cleaned_question = re.sub(r'[^\w\s]', ' ', question).strip()
+                try:
+                    result = self.base_tool.run(cleaned_question)
+                    return self._extract_relevant_sections(result, question) if result else "No Wikipedia results found."
+                except Exception as e:
+                    return f"Wikipedia search error: {str(e)}"
             
-            # Update tool usage statistics
-            for tool in selected_tools:
-                self.tool_usage_stats[tool] = self.tool_usage_stats.get(tool, 0) + 1
+            # Search for multiple entities
+            search_results = self._search_multiple_terms(entities)
             
-            # Enhanced logging
-            print(f"[{task_id}] Selected tools: {selected_tools}")
-            print(f"[{task_id}] Tools executed: {list(tool_results.keys())}")
-            print(f"[{task_id}] Processing steps: {len(conversation_history)}")
-            print(f"[{task_id}] Errors encountered: {error_count}")
+            if not search_results:
+                return "No relevant Wikipedia articles found."
             
-            # Log tool result sizes for debugging
-            for tool, result in tool_results.items():
-                result_size = len(str(result)) if result else 0
-                print(f"[{task_id}] {tool} result size: {result_size} chars")
+            # Combine and format results
+            formatted_results = []
+            for entity, content in search_results.items():
+                relevant_content = self._extract_relevant_sections(content, question)
+                if relevant_content:
+                    formatted_results.append(f"=== {entity} ===\n{relevant_content}")
             
-            print(f"[{task_id}] FINAL ANSWER: {answer}")
-            print(f"{'='*60}")
+            if not formatted_results:
+                return "No relevant information found in Wikipedia articles."
             
-            return answer
+            return "ENHANCED WIKIPEDIA RESULTS:\n\n" + "\n\n".join(formatted_results)
             
         except Exception as e:
-            error_msg = f"Critical error in enhanced agent execution: {str(e)}"
-            print(f"[{task_id}] {error_msg}")
-            
-            # Try fallback direct LLM response
-            try:
-                fallback_response = llm.invoke(f"Please answer this question: {question}").content
-                return f"Fallback response: {fallback_response}"
-            except:
-                return error_msg
+            return f"Enhanced Wikipedia error: {str(e)}"
 
-# ----------- Enhanced File Processing -----------
-def detect_file_type(file_path: str) -> Optional[str]:
-    """Enhanced file type detection with more formats"""
-    ext = Path(file_path).suffix.lower()
-    
-    file_type_mapping = {
-        # Spreadsheets
-        '.xlsx': 'excel', '.xls': 'excel', '.csv': 'excel',
-        # Images
-        '.png': 'image', '.jpg': 'image', '.jpeg': 'image', 
-        '.bmp': 'image', '.gif': 'image', '.tiff': 'image', '.webp': 'image',
-        # Audio
-        '.mp3': 'audio', '.wav': 'audio', '.ogg': 'audio', 
-        '.flac': 'audio', '.m4a': 'audio', '.aac': 'audio',
-        # Code
-        '.py': 'code', '.ipynb': 'code', '.js': 'code', '.html': 'code',
-        '.css': 'code', '.java': 'code', '.cpp': 'code', '.c': 'code',
-        '.sql': 'code', '.r': 'code', '.json': 'code', '.xml': 'code',
-        # Documents
-        '.txt': 'text', '.md': 'text', '.pdf': 'document',
-        '.doc': 'document', '.docx': 'document'
-    }
-    
-    return file_type_mapping.get(ext)
+# ----------- Enhanced File Processing Tools -----------
+def excel_to_markdown(inputs: dict) -> str:
+    """Enhanced Excel tool with better error handling and data analysis"""
+    try:
+        excel_path = inputs["excel_path"]
+        sheet_name = inputs.get("sheet_name", None)
+        file_path = Path(excel_path).expanduser().resolve()
+        if not file_path.is_file():
+            return f"Error: Excel file not found at {file_path}"
+
+        sheet: Union[str, int] = (
+            int(sheet_name) if sheet_name and sheet_name.isdigit() else sheet_name or 0
+        )
+        df = pd.read_excel(file_path, sheet_name=sheet)
+        
+        # Enhanced metadata
+        metadata = f"EXCEL FILE ANALYSIS:\n"
+        metadata += f"File: {file_path.name}\n"
+        metadata += f"Dimensions: {len(df)} rows × {len(df.columns)} columns\n"
+        metadata += f"Columns: {', '.join(df.columns.tolist())}\n"
+        
+        # Data type information
+        metadata += f"Data types: {dict(df.dtypes)}\n"
+        
+        # Basic statistics for numeric columns
+        numeric_cols = df.select_dtypes(include=['number']).columns
+        if len(numeric_cols) > 0:
+            metadata += f"Numeric columns: {list(numeric_cols)}\n"
+            for col in numeric_cols[:3]:  # Limit to first 3 numeric columns
+                metadata += f"  {col}: mean={df[col].mean():.2f}, min={df[col].min()}, max={df[col].max()}\n"
+        
+        metadata += "\nSAMPLE DATA (first 10 rows):\n"
+        
+        if hasattr(df, "to_markdown"):
+            sample_data = df.head(10).to_markdown(index=False)
+        else:
+            sample_data = tabulate(df.head(10), headers="keys", tablefmt="github", showindex=False)
+        
+        return metadata + sample_data + f"\n\n(Showing first 10 rows of {len(df)} total rows)"
+        
+    except Exception as e:
+        return f"Error reading Excel file: {str(e)}"
 
-def process_file(task_id: str, question_text: str) -> str:
-    """Enhanced file processing with better error handling and metadata"""
-    file_url = f"{FILE_PATH}{task_id}"
-    
+def image_file_info(image_path: str, question: str) -> str:
+    """Enhanced image file analysis using Gemini API"""
     try:
-        print(f"[{task_id}] Attempting to download file from: {file_url}")
-        response = requests.get(file_url, timeout=30)
-        response.raise_for_status()
-        print(f"[{task_id}] File download successful. Status: {response.status_code}")
-        
-    except requests.exceptions.RequestException as exc:
-        print(f"[{task_id}] File download failed: {str(exc)}")
-        return question_text  # Return original question if no file
-    
-    # Enhanced filename extraction
-    content_disposition = response.headers.get("content-disposition", "")
-    filename = task_id  # Default fallback
-    
-    # Try to extract filename from Content-Disposition header
-    filename_match = re.search(r'filename[*]?=(?:"([^"]+)"|([^;]+))', content_disposition)
-    if filename_match:
-        filename = filename_match.group(1) or filename_match.group(2)
-        filename = filename.strip()
-    
-    # Create enhanced temp directory structure
-    temp_storage_dir = Path(tempfile.gettempdir()) / "gaia_enhanced_files" / task_id
-    temp_storage_dir.mkdir(parents=True, exist_ok=True)
-    
-    file_path = temp_storage_dir / filename
-    file_path.write_bytes(response.content)
-    
-    # Get file metadata
-    file_size = len(response.content)
-    file_type = detect_file_type(filename)
-    
-    print(f"[{task_id}] File saved: {filename} ({file_size:,} bytes, type: {file_type})")
-    
-    # Enhanced question augmentation
-    enhanced_question = f"{question_text}\n\n"
-    enhanced_question += f"{'='*50}\n"
-    enhanced_question += f"FILE INFORMATION:\n"
-    enhanced_question += f"A file was downloaded for this task and saved locally at:\n"
-    enhanced_question += f"{str(file_path)}\n"
-    enhanced_question += f"File details:\n"
-    enhanced_question += f"- Name: {filename}\n"
-    enhanced_question += f"- Size: {file_size:,} bytes ({file_size/1024:.1f} KB)\n"
-    enhanced_question += f"- Type: {file_type or 'unknown'}\n"
-    enhanced_question += f"{'='*50}\n\n"
-    
-    return enhanced_question
+        from google import genai
+        from google.genai.types import  Part
 
-# ----------- Usage Examples and Testing -----------
-def run_enhanced_tests():
-    """Run comprehensive tests of the enhanced agent"""
-    agent = GaiaAgent()
-    
-    test_cases = [
-        {
-            "id": "test_search_1",
-            "question": "What are the latest developments in artificial intelligence in 2024?",
-            "expected_tools": ["search"]
-        },
-        {
-            "id": "test_wiki_1", 
-            "question": "Tell me about Albert Einstein's contributions to physics",
-            "expected_tools": ["wikipedia"]
-        },
-        {
-            "id": "test_combined_1",
-            "question": "What is machine learning and what are recent breakthroughs?",
-            "expected_tools": ["wikipedia", "search"]
-        },
-        {
-            "id": "test_excel_1",
-            "question": "Analyze the data in the Excel file sales_data.xlsx",
-            "expected_tools": ["excel"]
-        }
-    ]
-    
-    print("\n" + "="*80)
-    print("RUNNING ENHANCED AGENT TESTS")
-    print("="*80)
+        client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
+
+        # Read content from a local file
+        with open(image_path, "rb") as f:
+            img_bytes = f.read()
+
+        response = client.models.generate_content(
+            model="gemini-2.5-flash-preview-05-20",
+            contents=[
+                question,
+                Part.from_bytes(data=img_bytes, mime_type="image/jpeg")
+            ],
+        )
+        return response.text
     
-    for test_case in test_cases:
-        print(f"\nTest Case: {test_case['id']}")
-        print(f"Question: {test_case['question']}")
-        print(f"Expected tools: {test_case['expected_tools']}")
+    except Exception as e:
+        return f"Error during image analysis: {e}"
+
+def audio_file_info(audio_path: str) -> str:
+    """Returns only the transcription of an audio file."""
+    try:
+        model = whisper.load_model("tiny")  # Fast + accurate balance
+        result = model.transcribe(audio_path, fp16=False)
+        return result['text']
+    except Exception as e:
+        return f"Error transcribing audio: {str(e)}"
+
+def code_file_read(code_path: str) -> str:
+    """Enhanced code file analysis"""
+    try:
+        with open(code_path, "r", encoding="utf-8") as f:
+            content = f.read()
         
-        try:
-            result = agent(test_case['id'], test_case['question'])
-            print(f"Result length: {len(result)} characters")
-            print(f"Result preview: {result[:200]}...")
-        except Exception as e:
-            print(f"Test failed: {e}")
+        file_path = Path(code_path)
         
-        print("-" * 60)
-    
-    # Print tool usage statistics
-    print(f"\nTool Usage Statistics:")
-    for tool, count in agent.get_tool_stats().items():
-        print(f"  {tool}: {count} times")
-
-# Usage example
-if __name__ == "__main__":
-    # Create enhanced agent
-    agent = GaiaAgent()
-    
-    # Example usage
-    sample_questions = [
-        "What is the current population of Tokyo and how has it changed recently?",
-        "Explain quantum computing and its recent developments",
-        "Tell me about the history of machine learning and current AI trends",
-    ]
-    
-    print("\n" + "="*80)
-    print("ENHANCED GAIA AGENT DEMONSTRATION")
-    print("="*80)
+        info = f"CODE FILE ANALYSIS:\n"
+        info += f"File: {file_path.name}\n"
+        info += f"Extension: {file_path.suffix}\n"
+        info += f"Size: {len(content)} characters, {len(content.splitlines())} lines\n"
+        
+        # Language-specific analysis
+        if file_path.suffix == '.py':
+            # Python-specific analysis
+            import_lines = [line for line in content.splitlines() if line.strip().startswith(('import ', 'from '))]
+            if import_lines:
+                info += f"Imports ({len(import_lines)}): {', '.join(import_lines[:5])}\n"
+            
+            # Count functions and classes
+            func_count = len(re.findall(r'^def\s+\w+', content, re.MULTILINE))
+            class_count = len(re.findall(r'^class\s+\w+', content, re.MULTILINE))
+            info += f"Functions: {func_count}, Classes: {class_count}\n"
+        
+        info += f"\nCODE CONTENT:\n{content}"
+        return info
+        
+    except Exception as e:
+        return f"Error reading code file: {e}"
     
-    for i, question in enumerate(sample_questions):
-        print(f"\nExample {i+1}: {question}")
-        result = agent(f"demo_{i}", question)
-        print(f"Answer: {result[:300]}...")
-        print("-" * 60)
     
-    # Uncomment to run comprehensive tests
-    # run_enhanced_tests()
+import yt_dlp
+from pathlib import Path
+
+def extract_youtube_info(question: str) -> str:
+    """
+    Download a YouTube video or audio using yt-dlp without merging.
+
+    Parameters:
+    - url: str — YouTube URL
+    - audio_only: bool — if True, downloads audio only; else best single video+audio stream
+
+    Returns:
+    - str: path to downloaded file or error message
+    """
+    pattern = r"(https?://(?:www\.)?(?:youtube\.com/watch\?v=[\w\-]+|youtu\.be/[\w\-]+))"
+    match = re.search(pattern, question)
+    youtube_url =  match.group(1) if match else None
+    print(f"Extracting YouTube URL: {youtube_url}")
+    
+    match = re.search(r"(?:v=|\/)([a-zA-Z0-9_-]{11})", youtube_url)
+    video_id = match.group(1) if match else "dummy_id"
+    file_path = Path(video_id)
+
+    output_dir = Path(file_path).parent
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    ydl_opts = {
+        'format': 'best[ext=mp4]/best',  # best mp4 combined stream or fallback to best available
+        'outtmpl': str(file_path),
+        'quiet': True,
+        'no_warnings': True,
+    }
+
+    try:
+        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+            ydl.download([youtube_url])
+        return audio_file_info(str(file_path))
+    except Exception as e:
+        return f"Error: {e}"