Delete tools.py

tools.py

@@ -1,434 +0,0 @@
-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
-
-import numpy as np
-import os
-
-# ----------- Enhanced Search Functionality -----------
-class EnhancedSearchTool:
-    """Enhanced web search with intelligent query processing and result filtering"""
-    
-    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
-    
-    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]]
-    
-    def run(self, question: str) -> str:
-        """Enhanced search execution with multiple queries and result filtering"""
-        try:
-            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."
-            
-            # 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', '')
-                
-                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:
-            return f"Enhanced search error: {str(e)}"
-
-# ----------- Enhanced Wikipedia Tool -----------
-class EnhancedWikipediaTool:
-    """Enhanced Wikipedia search with intelligent query processing and content extraction"""
-    
-    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:
-            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.
-            
-            Example: "Tell me about Einstein's theory of relativity" -> "Albert Einstein, theory of relativity, relativity"
-            """
-            
-            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
-    
-    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 _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]
-        
-        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:
-            entities = self._extract_entities(question)
-            
-            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)}"
-            
-            # Search for multiple entities
-            search_results = self._search_multiple_terms(entities)
-            
-            if not search_results:
-                return "No relevant Wikipedia articles found."
-            
-            # 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}")
-            
-            if not formatted_results:
-                return "No relevant information found in Wikipedia articles."
-            
-            return "ENHANCED WIKIPEDIA RESULTS:\n\n" + "\n\n".join(formatted_results)
-            
-        except Exception as e:
-            return f"Enhanced Wikipedia error: {str(e)}"
-
-# ----------- 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 image_file_info(image_path: str, question: str) -> str:
-    """Enhanced image file analysis using Gemini API"""
-    try:
-        from google import genai
-        from google.genai.types import  Part
-
-        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
-    
-    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()
-        
-        file_path = Path(code_path)
-        
-        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}"
-    
-    
-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}"