app.py

import os
import json
import requests
import tempfile
from google.oauth2 import service_account
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload
import openai
from dotenv import load_dotenv, dotenv_values
import io
import logging
from typing import List, Dict, Optional

# LangChain imports
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.docstore.document import Document
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.schema import BaseRetriever
import pickle
import hashlib

from openai import OpenAI
openai.api_key = os.getenv('OPENAI_API_KEY')
openai = OpenAI(api_key=openai.api_key)

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class EnhancedGPTDriveIntegration:
    def __init__(self):
        # Build credentials info from individual environment variables
        credentials_info = {
            "type": "service_account",
            "project_id": os.getenv('GOOGLE_PROJECT_ID'),
            "private_key_id": os.getenv('GOOGLE_PRIVATE_KEY_ID'),
            "private_key": os.getenv('GOOGLE_PRIVATE_KEY').replace('\\n', '\n'),
            "client_email": os.getenv('GOOGLE_CLIENT_EMAIL'),
            "client_id": os.getenv('GOOGLE_CLIENT_ID'),
            "auth_uri": "https://accounts.google.com/o/oauth2/auth",
            "token_uri": "https://oauth2.googleapis.com/token",
            "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
            "client_x509_cert_url": os.getenv('GOOGLE_CLIENT_CERT_URL'),
            "universe_domain": "googleapis.com"
        }
        
        # Check if all required fields are present
        required_fields = ['project_id', 'private_key', 'client_email']
        missing_fields = [field for field in required_fields if not credentials_info[field]]
        
        if missing_fields:
            raise ValueError(f"Missing required environment variables: {missing_fields}")
        
        # Initialize Google Drive API
        self.credentials = service_account.Credentials.from_service_account_info(
            credentials_info,
            scopes=['https://www.googleapis.com/auth/drive.readonly']
        )
        
        self.drive_service = build('drive', 'v3', credentials=self.credentials)
        
        # Initialize OpenAI and LangChain components
        openai.api_key = os.getenv('OPENAI_API_KEY')
        self.embeddings = OpenAIEmbeddings(openai_api_key=os.getenv('OPENAI_API_KEY'))
        self.llm = OpenAI(temperature=0.7, openai_api_key=os.getenv('OPENAI_API_KEY'))
        
        # Text splitter for better chunking
        self.text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=200,
            length_function=len,
            separators=["\n\n", "\n", " ", ""]
        )
        
        # Initialize vector store
        self.vector_store = None
        self.conversation_memory = ConversationBufferMemory(
            memory_key="chat_history",
            return_messages=True
        )
        
        # Cache for processed files
        self.processed_files = {}
        self.cache_file = "processed_files_cache.pkl"
        self.load_cache()
    
    def load_cache(self):
        """Load processed files cache"""
        try:
            if os.path.exists(self.cache_file):
                with open(self.cache_file, 'rb') as f:
                    self.processed_files = pickle.load(f)
                logger.info(f"Loaded cache with {len(self.processed_files)} files")
        except Exception as e:
            logger.error(f"Error loading cache: {e}")
            self.processed_files = {}
    
    def save_cache(self):
        """Save processed files cache"""
        try:
            with open(self.cache_file, 'wb') as f:
                pickle.dump(self.processed_files, f)
            logger.info("Cache saved successfully")
        except Exception as e:
            logger.error(f"Error saving cache: {e}")
    
    def get_file_hash(self, file_id: str, file_size: str) -> str:
        """Generate hash for file to check if it's been processed"""
        return hashlib.md5(f"{file_id}_{file_size}".encode()).hexdigest()
    
    def search_files(self, query: str, file_types: Optional[List[str]] = None) -> List[Dict]:
        """Search for files in Google Drive with improved query handling"""
        # Build more sophisticated search query
        search_terms = query.lower().split()
        search_queries = []
        
        # Search in file names and content
        for term in search_terms:
            search_queries.append(f"name contains '{term}' or fullText contains '{term}'")
        
        search_query = " and ".join([f"({sq})" for sq in search_queries])
        
        if file_types:
            type_queries = []
            for file_type in file_types:
                if file_type.lower() == 'pdf':
                    type_queries.append("mimeType='application/pdf'")
                elif file_type.lower() in ['doc', 'docx']:
                    type_queries.append("mimeType contains 'document'")
                elif file_type.lower() in ['xls', 'xlsx']:
                    type_queries.append("mimeType contains 'spreadsheet'")
                elif file_type.lower() == 'txt':
                    type_queries.append("mimeType='text/plain'")
            
            if type_queries:
                search_query += f" and ({' or '.join(type_queries)})"
        
        try:
            results = self.drive_service.files().list(
                q=search_query,
                fields="files(id, name, mimeType, size, modifiedTime)",
                pageSize=20  # Increased to get more results
            ).execute()
            
            files = results.get('files', [])
            logger.info(f"Found {len(files)} files matching query: {query}")
            return files
            
        except Exception as e:
            logger.error(f"Error searching files: {e}")
            return []
    
    def get_file_content(self, file_id: str, mime_type: str) -> str:
        """Download and extract text content from file with better error handling"""
        try:
            if 'text' in mime_type or 'document' in mime_type:
                if 'document' in mime_type:
                    request = self.drive_service.files().export_media(
                        fileId=file_id, mimeType='text/plain'
                    )
                else:
                    request = self.drive_service.files().get_media(fileId=file_id)
                
                file_content = io.BytesIO()
                downloader = MediaIoBaseDownload(file_content, request)
                done = False
                while done is False:
                    status, done = downloader.next_chunk()
                
                return file_content.getvalue().decode('utf-8', errors='ignore')
            
            elif 'spreadsheet' in mime_type:
                request = self.drive_service.files().export_media(
                    fileId=file_id, mimeType='text/csv'
                )
                file_content = io.BytesIO()
                downloader = MediaIoBaseDownload(file_content, request)
                done = False
                while done is False:
                    status, done = downloader.next_chunk()
                
                return file_content.getvalue().decode('utf-8', errors='ignore')
            
            elif mime_type == 'application/pdf':
                request = self.drive_service.files().get_media(fileId=file_id)
                file_content = io.BytesIO()
                downloader = MediaIoBaseDownload(file_content, request)
                done = False
                while done is False:
                    status, done = downloader.next_chunk()
                
                file_content.seek(0)
                
                try:
                    import PyPDF2
                    pdf_reader = PyPDF2.PdfReader(file_content)
                    text = ""
                    for page in pdf_reader.pages:
                        text += page.extract_text() + "\n"
                    return text
                except ImportError:
                    logger.warning("PyPDF2 not available, trying alternative PDF extraction")
                    # Try alternative PDF extraction
                    try:
                        import pdfplumber
                        with pdfplumber.open(file_content) as pdf:
                            text = ""
                            for page in pdf.pages:
                                text += page.extract_text() + "\n"
                        return text
                    except ImportError:
                        return "PDF text extraction requires PyPDF2 or pdfplumber library"
                except Exception as e:
                    return f"Error extracting PDF text: {str(e)}"
            
            else:
                return "File type not supported for text extraction"
                
        except Exception as e:
            logger.error(f"Error reading file {file_id}: {e}")
            return f"Error reading file: {str(e)}"
    
    def process_documents_to_vector_store(self, files: List[Dict]) -> None:
        """Process documents and create/update vector store"""
        documents = []
        new_files_processed = 0
        
        for file in files:
            file_hash = self.get_file_hash(file['id'], file.get('size', '0'))
            
            # Check if file is already processed and hasn't changed
            if file_hash in self.processed_files:
                # Load cached documents
                cached_docs = self.processed_files[file_hash]
                documents.extend(cached_docs)
                continue
            
            # Process new or changed file
            content = self.get_file_content(file['id'], file['mimeType'])
            
            if content and not content.startswith('Error'):
                # Split content into chunks
                chunks = self.text_splitter.split_text(content)
                
                # Create Document objects with metadata
                file_documents = []
                for i, chunk in enumerate(chunks):
                    doc = Document(
                        page_content=chunk,
                        metadata={
                            'source': file['name'],
                            'file_id': file['id'],
                            'chunk_id': i,
                            'mime_type': file['mimeType'],
                            'total_chunks': len(chunks)
                        }
                    )
                    file_documents.append(doc)
                
                documents.extend(file_documents)
                
                # Cache the processed documents
                self.processed_files[file_hash] = file_documents
                new_files_processed += 1
                
                logger.info(f"Processed file: {file['name']} ({len(chunks)} chunks)")
        
        if new_files_processed > 0:
            self.save_cache()
            logger.info(f"Processed {new_files_processed} new files")
        
        # Create or update vector store
        if documents:
            if self.vector_store is None:
                self.vector_store = FAISS.from_documents(documents, self.embeddings)
                logger.info(f"Created new vector store with {len(documents)} documents")
            else:
                # Add new documents to existing vector store
                new_docs = [doc for file_docs in self.processed_files.values() 
                           for doc in file_docs if doc not in documents]
                if new_docs:
                    self.vector_store.add_documents(new_docs)
                    logger.info(f"Added {len(new_docs)} new documents to vector store")
    
    def create_conversational_chain(self) -> ConversationalRetrievalChain:
        """Create a conversational retrieval chain"""
        if self.vector_store is None:
            raise ValueError("Vector store not initialized. Process documents first.")
        
        # Create custom prompt template
        prompt_template = """You are Study Buddy, an AI assistant specialized in helping students study anatomy effectively. 
        Use the following context from the student's study materials to answer their question.

        Context: {context}

        Question: {question}

        Instructions:
        1. Answer the question directly and comprehensively using the provided context
        2. If the context doesn't contain enough information, say so clearly
        3. Provide study tips or exam strategies when relevant
        4. Use clear, educational language appropriate for students
        5. Always end your response with "Is there anything else I can help you with?"

        Answer:"""
        
        PROMPT = PromptTemplate(
            template=prompt_template,
            input_variables=["context", "question"]
        )
        
        # Create retrieval chain
        qa_chain = ConversationalRetrievalChain.from_llm(
            llm=self.llm,
            retriever=self.vector_store.as_retriever(
                search_type="similarity",
                search_kwargs={"k": 6}  # Retrieve top 6 relevant chunks
            ),
            memory=self.conversation_memory,
            combine_docs_chain_kwargs={"prompt": PROMPT},
            return_source_documents=True,
            verbose=True
        )
        
        return qa_chain
    
    def process_query(self, user_query: str, search_terms: Optional[List[str]] = None) -> Dict:
        """Enhanced query processing with LangChain"""
        try:
            # Extract search terms from query if not provided
            if not search_terms:
                search_terms = user_query.lower().split()[:5]  # Take first 5 words
            
            # Search for relevant files
            all_files = []
            for term in search_terms:
                files = self.search_files(term)
                all_files.extend(files)
            
            # Remove duplicates while preserving order
            unique_files = []
            seen_ids = set()
            for file in all_files:
                if file['id'] not in seen_ids:
                    unique_files.append(file)
                    seen_ids.add(file['id'])
            
            if not unique_files:
                return {
                    'answer': "No relevant files found in your Google Drive for this query. Please check if you have uploaded study materials related to your question.",
                    'sources': [],
                    'confidence': 'low'
                }
            
            # Process documents and create vector store
            self.process_documents_to_vector_store(unique_files[:10])  # Process top 10 files
            
            if self.vector_store is None:
                return {
                    'answer': "Unable to process the documents. Please check if the files contain readable text content.",
                    'sources': [],
                    'confidence': 'low'
                }
            
            # Create conversational chain and get answer
            qa_chain = self.create_conversational_chain()
            
            # Query the chain
            result = qa_chain({"question": user_query})
            
            # Extract source documents
            source_docs = result.get('source_documents', [])
            sources = list(set([doc.metadata['source'] for doc in source_docs]))
            
            # Calculate confidence based on source document relevance
            confidence = 'high' if len(source_docs) >= 3 else 'medium' if len(source_docs) >= 1 else 'low'
            
            return {
                'answer': result['answer'],
                'sources': sources,
                'confidence': confidence,
                'total_files_searched': len(unique_files),
                'chunks_retrieved': len(source_docs)
            }
            
        except Exception as e:
            logger.error(f"Error processing query: {e}")
            return {
                'answer': f"An error occurred while processing your query: {str(e)}. Please try again or rephrase your question.",
                'sources': [],
                'confidence': 'low'
            }
    
    def clear_memory(self):
        """Clear conversation memory"""
        self.conversation_memory.clear()
        logger.info("Conversation memory cleared")
    
    def get_vector_store_stats(self) -> Dict:
        """Get statistics about the vector store"""
        if self.vector_store is None:
            return {"total_documents": 0, "total_files": 0}
        
        try:
            total_docs = len(self.vector_store.docstore._dict)
            total_files = len(set([doc.metadata.get('source', 'Unknown') 
                                 for doc in self.vector_store.docstore._dict.values()]))
            
            return {
                "total_documents": total_docs,
                "total_files": total_files,
                "cache_size": len(self.processed_files)
            }
        except:
            return {"total_documents": "Unknown", "total_files": "Unknown"}

# Initialize the enhanced system
enhanced_gpt_drive = EnhancedGPTDriveIntegration()

def process_user_query(query: str, search_terms_input: str) -> tuple:
    """Process user query and return formatted response"""
    if not query.strip():
        return "Please enter a question.", "", ""
    
    # Parse search terms if provided
    search_terms = None
    if search_terms_input.strip():
        search_terms = [term.strip() for term in search_terms_input.split(',')]
    
    # Process the query
    result = enhanced_gpt_drive.process_query(query, search_terms)
    
    # Format the response
    answer = result['answer']
    sources = result['sources']
    
    # Create detailed sources text
    sources_text = ""
    if sources:
        sources_text = "**Sources used:**\n" + "\n".join([f"• {source}" for source in sources])
        sources_text += f"\n\n**Search Details:**\n"
        sources_text += f"• Files searched: {result.get('total_files_searched', 0)}\n"
        sources_text += f"• Relevant chunks found: {result.get('chunks_retrieved', 0)}\n"
        sources_text += f"• Confidence: {result.get('confidence', 'unknown').title()}"
    
    # Stats for display
    stats = enhanced_gpt_drive.get_vector_store_stats()
    stats_text = f"**Knowledge Base:** {stats['total_documents']} chunks from {stats['total_files']} files"
    
    return answer, sources_text, stats_text

def clear_conversation():
    """Clear conversation memory"""
    enhanced_gpt_drive.clear_memory()
    return "Conversation history cleared. You can start a fresh conversation now."

def get_system_status():
    """Get system status information"""
    stats = enhanced_gpt_drive.get_vector_store_stats()
    
    status_lines = [
        "✅ Google Drive API: Connected",
        "✅ OpenAI API: Connected", 
        "✅ LangChain: Initialized",
        f"📚 Knowledge Base: {stats['total_documents']} document chunks",
        f"📁 Processed Files: {stats['total_files']} files",
        f"💾 Cache Size: {stats['cache_size']} entries"
    ]
    
    return "\n".join(status_lines)

# Create enhanced Gradio interface
import gradio as gr

with gr.Blocks(title="Enhanced Study Buddy", theme=gr.themes.Soft()) as app:
    gr.Markdown("# 🧠 Enhanced Anatomy Study Buddy with LangChain")
    gr.Markdown("Study more effectively with advanced AI-powered document analysis and conversational memory!")
    
    with gr.Row():
        with gr.Column(scale=3):
            # Main query interface
            with gr.Group():
                gr.Markdown("### 💬 Ask a Question")
                query_input = gr.Textbox(
                    label="Your Question",
                    placeholder="Ask me anything about your anatomy study materials...",
                    lines=3
                )
                
                search_terms_input = gr.Textbox(
                    label="🔍 Search Terms (Optional)",
                    placeholder="Enter comma-separated terms to focus the search",
                    lines=1
                )
                
                with gr.Row():
                    submit_btn = gr.Button("🚀 Search & Ask", variant="primary", size="lg")
                    clear_btn = gr.Button("🧹 Clear Memory", variant="secondary")
            
            # Results section
            with gr.Group():
                gr.Markdown("### 🎯 Answer")
                answer_output = gr.Textbox(
                    label="AI Response",
                    lines=12,
                    interactive=False
                )
                
                sources_output = gr.Textbox(
                    label="📚 Sources & Details",
                    lines=6,
                    interactive=False
                )
        
        with gr.Column(scale=1):
            # System info
            with gr.Group():
                gr.Markdown("### 📊 System Status")
                status_btn = gr.Button("🔄 Refresh Status", size="sm")
                status_output = gr.Textbox(
                    label="System Information",
                    lines=8,
                    interactive=False
                )
                
                stats_output = gr.Textbox(
                    label="Knowledge Base",
                    lines=2,
                    interactive=False
                )
    
    # Event handlers
    submit_btn.click(
        fn=process_user_query,
        inputs=[query_input, search_terms_input],
        outputs=[answer_output, sources_output, stats_output]
    )
    
    clear_btn.click(
        fn=clear_conversation,
        outputs=answer_output
    )
    
    status_btn.click(
        fn=get_system_status,
        outputs=status_output
    )
    
    # Enhanced examples
    with gr.Row():
        gr.Examples(
            examples=[
                ["What is morbid anatomy and how does it relate to pathology?", "morbid, anatomy, pathology"],
                ["Explain the neural transmission process between neurons", "neuron, transmission, synaptic"],
                ["Describe the complete anatomy of the external ear", "external ear, anatomy, auditory"],
                ["What are the different types of therapeutic massage?", "massage, therapy, treatment"],
                ["Define trauma and its classification in medical terms", "trauma, medical, classification"],
                ["Explain upper limb prosthetics and their applications", "prosthetics, upper limb, rehabilitation"],
                ["How does the nervous system control muscle movement?", "nervous system, muscle, motor control"],
                ["What are the key anatomical landmarks for injection sites?", "injection sites, anatomical landmarks"]
            ],
            inputs=[query_input, search_terms_input]
        )
    
    # Initial status load
    app.load(
        fn=get_system_status,
        outputs=status_output
    )

# Launch the enhanced app
if __name__ == "__main__":
    app.launch(
        share=True,
        debug=True,
        server_name="0.0.0.0",
        server_port=7860
    )