app.py

import os
import time
import json
import logging
import threading
import gradio as gr
import google.generativeai as genai
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload
from google.oauth2 import service_account
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader, TextLoader, Docx2txtLoader
from langchain.chains import RetrievalQA
from langchain_google_genai import ChatGoogleGenerativeAI
from PyPDF2 import PdfReader
from gtts import gTTS

temp_file_map = {}  

# ✅ Configure logging
logging.basicConfig(level=logging.INFO)

# ✅ Load API Keys
logging.info("🔑 Loading API keys...")
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY_1")
SERVICE_ACCOUNT_JSON = os.getenv("SERVICE_ACCOUNT_JSON")

if not GOOGLE_API_KEY or not SERVICE_ACCOUNT_JSON:
    logging.error("❌ Missing API Key or Service Account JSON.")
    raise ValueError("❌ Missing API Key or Service Account JSON. Please add them as environment variables.")

os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
SERVICE_ACCOUNT_FILE = json.loads(SERVICE_ACCOUNT_JSON)
SCOPES = ["https://www.googleapis.com/auth/drive"]
FOLDER_ID = "1xqOpwgwUoiJYf9GkeuB4dayme4zJcujf"
creds = service_account.Credentials.from_service_account_info(SERVICE_ACCOUNT_FILE)
drive_service = build("drive", "v3", credentials=creds)

# ✅ Initialize variables
vector_store = None
file_id_map = {}
temp_dir = "./temp_downloads"
os.makedirs(temp_dir, exist_ok=True)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

# ✅ Get list of files from Google Drive
def get_files_from_drive():
    logging.info("📂 Fetching files from Google Drive...")
    query = f"'{FOLDER_ID}' in parents and trashed = false"
    results = drive_service.files().list(q=query, fields="files(id, name)").execute()
    files = results.get("files", [])
    global file_id_map
    file_id_map = {file["name"]: file["id"] for file in files}
    return list(file_id_map.keys()) if files else []

# ✅ Download file from Google Drive
def download_file(file_id, file_name):
    file_path = os.path.join(temp_dir, file_name)
    request = drive_service.files().get_media(fileId=file_id)
    with open(file_path, "wb") as f:
        downloader = MediaIoBaseDownload(f, request)
        done = False
        while not done:
            _, done = downloader.next_chunk()
    return file_path

# ✅ Process documents
def process_documents(selected_files):
    global vector_store
    docs = []
    for file_name in selected_files:
        file_path = download_file(file_id_map[file_name], file_name)
        if file_name.endswith(".pdf"):
            loader = PyPDFLoader(file_path)
        elif file_name.endswith(".txt"):
            loader = TextLoader(file_path)
        elif file_name.endswith(".docx"):
            loader = Docx2txtLoader(file_path)
        else:
            logging.warning(f"⚠️ Unsupported file type: {file_name}")
            continue
        docs.extend(loader.load())
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    split_docs = text_splitter.split_documents(docs)
    vector_store = Chroma.from_documents(split_docs, embeddings)
    return "✅ Documents processed successfully!"

# ✅ Query document


def query_document(question):
    
    if vector_store is None:
        return "❌ No documents processed.", None

    # ✅ Fetch stored documents  
    stored_docs = vector_store.get()["documents"]  

    # ✅ Calculate total word count safely  
    total_words = sum(len(doc.split()) if isinstance(doc, str) else len(doc.page_content.split()) for doc in stored_docs)  

    # ✅ Dynamically adjust k based on document size  
    if total_words < 500:  
        k_value = 3  
    elif total_words < 2000:  
        k_value = 5  
    else:  
        k_value = 10  

    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": k_value})  

    # ✅ Improved prompt for detailed response  
    detailed_prompt = f"""  
    Provide a **detailed and structured answer** to the following question.  
    - Use relevant **examples, key points, and explanations**.  
    - If applicable, provide **step-by-step analysis** or comparisons.  
    - Ensure **clarity and completeness**.  
    **Question:** {question}  
    """  

    # ✅ Dynamically select model based on document size  
    if total_words < 1000:  
        model_name = "gemini-2.0-pro-exp-02-05"  # More detailed responses for small files  
    else:  
        model_name = "gemini-2.0-flash"  # Faster processing for large documents  

    logging.info(f"🧠 Using Model: {model_name} for processing")  

    model = ChatGoogleGenerativeAI(model=model_name, google_api_key=GOOGLE_API_KEY)  
    qa_chain = RetrievalQA.from_chain_type(llm=model, retriever=retriever)  
    response = qa_chain.invoke({"query": detailed_prompt})["result"]  

    # ✅ Convert response to speech  
    tts = gTTS(text=response, lang="en")  
    temp_audio_path = os.path.join(temp_dir, "response.mp3")  
    tts.save(temp_audio_path)  
    temp_file_map["response.mp3"] = time.time()  

    return response, temp_audio_path 


# ✅ Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# 📄 AI-Powered Multi-Document Chatbot with Voice Output")
    
    file_dropdown = gr.Dropdown(choices=get_files_from_drive(), label="📂 Select Files", multiselect=True)
    refresh_button = gr.Button("🔄 Refresh Files")  # 🔄 Add Refresh Button
    process_button = gr.Button("🚀 Process Documents")
    
    user_input = gr.Textbox(label="🔎 Ask a Question")
    submit_button = gr.Button("💬 Get Answer")
    
    response_output = gr.Textbox(label="📝 Response")
    audio_output = gr.Audio(label="🔊 Audio Response")

    # 🔄 Function to Refresh File List
    def refresh_files():
        return gr.update(choices=get_files_from_drive())

    # ✅ Connect Refresh Button
    refresh_button.click(refresh_files, outputs=file_dropdown)

    # ✅ Connect Process Button
    process_button.click(process_documents, inputs=file_dropdown, outputs=response_output)

    # ✅ Connect Query Button
    submit_button.click(query_document, inputs=user_input, outputs=[response_output, audio_output])

demo.launch()