app.py

import streamlit as st
import pdfplumber
import docx
import os
import re
import numpy as np
import google.generativeai as palm
from sklearn.metrics.pairwise import cosine_similarity
import logging
import time
import uuid
import json
import firebase_admin
from firebase_admin import credentials, firestore
from dotenv import load_dotenv
import chromadb

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[logging.StreamHandler()]
)
logger = logging.getLogger(__name__)

# Load environment variables
load_dotenv()

# Configuration class
class Config:
    CHUNK_WORDS = 300
    EMBEDDING_MODEL = "models/text-embedding-004"
    TOP_N = 5
    SYSTEM_PROMPT = (
        "You are a helpful assistant. Answer the question using the provided context below. "
        "Answer based on your knowledge if the context given is not enough."
    )
    GENERATION_MODEL = "models/gemini-1.5-flash"

# Initialize Firebase
def init_firebase():
    """Initialize Firebase with proper credential handling"""
    if not firebase_admin._apps:
        try:
            firebase_cred = os.getenv("FIREBASE_CRED")
            if not firebase_cred:
                logger.error("Firebase credentials not found in environment variables")
                st.error("Firebase configuration is missing. Please check your .env file.")
                st.stop()
                
            cred_dict = json.loads(firebase_cred)
            cred = credentials.Certificate(cred_dict)
            firebase_admin.initialize_app(cred)
            logger.info("Firebase initialized successfully")
            
        except json.JSONDecodeError:
            logger.error("Invalid Firebase credentials format")
            st.error("Firebase credentials are invalid. Please check your .env file.")
            st.stop()
        except Exception as e:
            logger.error("Firebase initialization failed", exc_info=True)
            st.error("Failed to initialize Firebase. Please contact support.")
            st.stop()

# Initialize ChromaDB
def init_chroma():
    """Initialize ChromaDB with proper persistence handling"""
    try:
        persist_directory = "chroma_db"
        os.makedirs(persist_directory, exist_ok=True)
        
        client = chromadb.PersistentClient(path=persist_directory)
        collection = client.get_or_create_collection(
            name="document_embeddings",
            metadata={"hnsw:space": "cosine"}
        )
        logger.info("ChromaDB initialized successfully")
        return client, collection
    except Exception as e:
        logger.error("ChromaDB initialization failed", exc_info=True)
        st.error("Failed to initialize ChromaDB. Please check your configuration.")
        st.stop()

# Initialize services
init_firebase()
fs_client = firestore.client()
chroma_client, embedding_collection = init_chroma()

# Configure Palm API
API_KEY = os.getenv("GOOGLE_API_KEY")
if not API_KEY:
    st.error("Google API key is not configured.")
    st.stop()
palm.configure(api_key=API_KEY)

# Utility functions
@st.cache_data(show_spinner=True)
def generate_embedding_cached(text: str) -> list:
    """Generate embeddings with caching"""
    logger.info(f"Generating embedding for text: {text[:50]}...")
    try:
        response = palm.embed_content(
            model=Config.EMBEDDING_MODEL,
            content=text,
            task_type="retrieval_document"
        )
        if "embedding" not in response or not response["embedding"]:
            logger.error("No embedding returned from API")
            return [0.0] * 768
        
        embedding = np.array(response["embedding"])
        if embedding.ndim == 2:
            embedding = embedding.flatten()
        return embedding.tolist()
    except Exception as e:
        logger.error(f"Embedding generation failed: {e}")
        return [0.0] * 768

def extract_text_from_file(uploaded_file) -> str:
    """Extract text from various file formats"""
    file_name = uploaded_file.name.lower()
    
    if file_name.endswith(".txt"):
        return uploaded_file.read().decode("utf-8")
    elif file_name.endswith(".pdf"):
        with pdfplumber.open(uploaded_file) as pdf:
            return "\n".join([page.extract_text() for page in pdf.pages if page.extract_text()])
    elif file_name.endswith(".docx"):
        doc = docx.Document(uploaded_file)
        return "\n".join([para.text for para in doc.paragraphs])
    else:
        raise ValueError("Unsupported file type. Please upload a .txt, .pdf, or .docx file.")

def chunk_text(text: str) -> list[str]:
    """Split text into manageable chunks"""
    max_words = Config.CHUNK_WORDS
    paragraphs = [p.strip() for p in text.split("\n\n") if p.strip()]
    chunks = []
    current_chunk = ""
    current_word_count = 0
    
    for paragraph in paragraphs:
        para_word_count = len(paragraph.split())
        
        if para_word_count > max_words:
            if current_chunk:
                chunks.append(current_chunk.strip())
                current_chunk = ""
                current_word_count = 0
                
            sentences = re.split(r'(?<=[.!?])\s+', paragraph)
            temp_chunk = ""
            temp_word_count = 0
            
            for sentence in sentences:
                sentence_word_count = len(sentence.split())
                if temp_word_count + sentence_word_count > max_words:
                    if temp_chunk:
                        chunks.append(temp_chunk.strip())
                    temp_chunk = sentence + " "
                    temp_word_count = sentence_word_count
                else:
                    temp_chunk += sentence + " "
                    temp_word_count += sentence_word_count
            
            if temp_chunk:
                chunks.append(temp_chunk.strip())
        else:
            if current_word_count + para_word_count > max_words:
                if current_chunk:
                    chunks.append(current_chunk.strip())
                current_chunk = paragraph + "\n\n"
                current_word_count = para_word_count
            else:
                current_chunk += paragraph + "\n\n"
                current_word_count += para_word_count
                
    if current_chunk:
        chunks.append(current_chunk.strip())
    return chunks

def process_document(uploaded_file) -> None:
    """Process document and store in ChromaDB"""
    try:
        # Clear existing session state
        keys_to_clear = ["document_text", "document_chunks", "document_embeddings"]
        for key in keys_to_clear:
            st.session_state.pop(key, None)
            
        # Extract and validate text
        file_text = extract_text_from_file(uploaded_file)
        if not file_text.strip():
            st.error("The uploaded file contains no valid text.")
            return
            
        # Process text into chunks
        chunks = chunk_text(file_text)
        if not chunks:
            st.error("Failed to split text into chunks.")
            return
            
        # Generate embeddings
        embeddings = []
        chunk_ids = []

        progress_bar = st.progress(0)  # ✅ Correctly initialize progress bar
        
        for i, chunk in enumerate(chunks):
            chunk_id = str(uuid.uuid4())
            embedding = generate_embedding_cached(chunk)
            
            if not any(embedding):  # Ensure embedding is valid
                continue
                
            embeddings.append(embedding)
            chunk_ids.append(chunk_id)
            progress_bar.progress((i + 1) / len(chunks))  # ✅ Update progress bar
        
        if not embeddings:
            st.error("Failed to generate valid embeddings for the document.")
            return

        # Ensure `embedding_collection` is properly initialized
        if embedding_collection is None:
            st.error("ChromaDB collection is not initialized.")
            return
        
        # Save to ChromaDB
        embedding_collection.add(
            ids=chunk_ids,
            documents=chunks[:len(embeddings)],
            embeddings=embeddings,
            metadatas=[{"chunk_index": idx} for idx in range(len(embeddings))]
        )
        
        # Update session state
        st.session_state.update({
            "document_text": file_text,
            "document_chunks": chunks[:len(embeddings)],
            "document_embeddings": embeddings,
            "chunk_ids": chunk_ids
        })
        
        if not st.session_state.get("doc_processed", False):
            st.success("Document processing complete! You can now start chatting.")
            st.session_state.doc_processed = True
            
    except Exception as e:
        logger.error(f"Document processing failed: {e}")
        st.error(f"An error occurred while processing the document: {e}")

def search_query(query: str) -> list[tuple[str, float]]:
    """Search for relevant document chunks"""
    try:
        query_embedding = generate_embedding_cached(query)
        
        results = embedding_collection.query(
            query_embeddings=[query_embedding],
            n_results=Config.TOP_N
        )
        
        results_data = []
        for i, metadata in enumerate(results["metadatas"]):
            chunk_index = metadata["chunk_index"]
            similarity_score = results["distances"][i]
            results_data.append((st.session_state["document_chunks"][chunk_index], similarity_score))
        
        return results_data
    except Exception as e:
        logger.error(f"Search query failed: {e}")
        return []

def generate_answer(user_query: str, context: str) -> str:
    """Generate answer using Palm API"""
    prompt = (
        f"System: {Config.SYSTEM_PROMPT}\n\n"
        f"Context:\n{context}\n\n"
        f"User: {user_query}\nAssistant:"
    )
    try:
        model = palm.GenerativeModel(Config.GENERATION_MODEL)
        response = model.generate_content(prompt)
        return response.text if hasattr(response, "text") else response
    except Exception as e:
        logger.error(f"Answer generation failed: {e}")
        return "I'm sorry, I encountered an error generating a response."

# Firebase functions
def save_conversation_to_firestore(session_id, user_question, assistant_answer, feedback=None):
    """Save conversation to Firestore"""
    conv_ref = fs_client.collection("sessions").document(session_id).collection("conversations")
    data = {
        "user_question": user_question,
        "assistant_answer": assistant_answer,
        "feedback": feedback,
        "timestamp": firestore.SERVER_TIMESTAMP
    }
    doc_ref = conv_ref.add(data)
    return doc_ref[1].id

def update_feedback_in_firestore(session_id, conversation_id, feedback):
    """Update feedback in Firestore"""
    conv_doc = fs_client.collection("sessions").document(session_id).collection("conversations").document(conversation_id)
    conv_doc.update({"feedback": feedback})

def handle_feedback(feedback_val):
    """Handle user feedback"""
    update_feedback_in_firestore(
        st.session_state.session_id,
        st.session_state.latest_conversation_id,
        feedback_val
    )
    st.session_state.conversations[-1]["feedback"] = feedback_val

# Chat interface
def chat_app():
    """Main chat interface"""
    if "conversations" not in st.session_state:
        st.session_state.conversations = []
    if "session_id" not in st.session_state:
        st.session_state.session_id = str(uuid.uuid4())

    # Display conversation history
    for conv in st.session_state.conversations:
        with st.chat_message("user"):
            st.write(conv["user_question"])
        with st.chat_message("assistant"):
            st.write(conv["assistant_answer"])
            if conv.get("feedback"):
                st.markdown(f"**Feedback:** {conv['feedback']}")

    # Handle new user input
    user_input = st.chat_input("Type your message here")
    if user_input:
        with st.chat_message("user"):
            st.write(user_input)
            
        results = search_query(user_input)
        context = "\n\n".join([chunk for chunk, score in results]) if results else ""
        answer = generate_answer(user_input, context)
        
        with st.chat_message("assistant"):
            st.write(answer)
            
        conversation_id = save_conversation_to_firestore(
            st.session_state.session_id,
            user_question=user_input,
            assistant_answer=answer
        )
        st.session_state.latest_conversation_id = conversation_id
        st.session_state.conversations.append({
            "user_question": user_input,
            "assistant_answer": answer,
        })
        
        # Add feedback buttons
        if "feedback" not in st.session_state.conversations[-1]:
            col1, col2, col3, col4, col5, col6, col7, col8, col9, col10 = st.columns(10)
            col1.button("👍", key=f"feedback_like_{len(st.session_state.conversations)}", 
                       on_click=handle_feedback, args=("positive",))
            col2.button("👎", key=f"feedback_dislike_{len(st.session_state.conversations)}", 
                       on_click=handle_feedback, args=("negative",))

def main():
    """Main application"""
    st.title("Chat with your files")
    
    # Sidebar for file upload
    st.sidebar.header("Upload Document")
    uploaded_file = st.sidebar.file_uploader("Upload (.txt, .pdf, .docx)", type=["txt", "pdf", "docx"])
    
    if uploaded_file and not st.session_state.get("doc_processed", False):
        process_document(uploaded_file)
        
    if "document_text" in st.session_state:
        chat_app()
    else:
        st.info("Please upload and process a document from the sidebar to start chatting.")
        
    # Footer
    st.markdown(
        """
        <div style="position: fixed; right: 10px; bottom: 10px; font-size: 12px; z-index: 9999; text-align: right;">
        Made by Danny.<br>
        Your questions, our response as well as your feedback will be saved for evaluation purposes.
        </div>
        """,
        unsafe_allow_html=True
    )

if __name__ == "__main__":
    main()