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
import concurrent.futures


def load_document(file_name, file_path):
    """Loads a document based on its file type."""
    try:
        if file_name.endswith(".pdf"):
            return PyPDFLoader(file_path).load()
        elif file_name.endswith(".txt"):
            return TextLoader(file_path).load()
        elif file_name.endswith(".docx"):
            return Docx2txtLoader(file_path).load()
        else:
            logging.warning(f"⚠️ Unsupported file type: {file_name}")
            return []
    except Exception as e:
        logging.error(f"❌ Error loading {file_name}: {e}")
        return []

def process_documents(selected_files):
    global vector_store
    docs = []

    # ✅ Use parallel processing to load documents faster
    with concurrent.futures.ThreadPoolExecutor() as executor:
        future_to_file = {executor.submit(load_document, file_name, download_file(file_id_map[file_name], file_name)): file_name for file_name in selected_files}
        for future in concurrent.futures.as_completed(future_to_file):
            docs.extend(future.result())

    # ✅ Calculate total word count  
    total_words = sum(len(doc.page_content.split()) for doc in docs)

    # ✅ Dynamically adjust chunk size for efficiency  
    if total_words < 1000:
        chunk_size, chunk_overlap = 500, 50   # Small  
    elif total_words < 5000:
        chunk_size, chunk_overlap = 1000, 100  # Medium  
    else:
        chunk_size, chunk_overlap = 2000, 200  # Large  

    logging.info(f"📄 Document Size: {total_words} words | Chunk Size: {chunk_size}, Overlap: {chunk_overlap}")

    # ✅ Efficient document splitting  
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
    split_docs = text_splitter.split_documents(docs)

    # ✅ Store efficiently in vector database  
    vector_store = Chroma.from_documents(split_docs, embeddings)

    return "✅ Documents processed successfully!"


# ✅ Query document




# ✅ Ensure temp_file_map exists
temp_file_map = {}

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)

    # ✅ Categorize file size  
    if total_words < 500:
        file_size_category = "small"
        k_value = 3
    elif total_words < 2000:
        file_size_category = "medium"
        k_value = 5
    else:
        file_size_category = "large"
        k_value = 10

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

    # ✅ Adjust response detail based on file size  
    if file_size_category == "small":
        prompt_prefix = "Provide a **concise** response focusing on key points."
    elif file_size_category == "medium":
        prompt_prefix = "Provide a **detailed response** with examples and key insights."
    else:
        prompt_prefix = "Provide a **comprehensive and structured response**, including step-by-step analysis and explanations."

    # ✅ Final prompt  
    detailed_prompt = f"""{prompt_prefix}  
    - Ensure clarity and completeness.  
    - Highlight the most relevant information.  
    **Question:** {question}  
    """

    # ✅ Dynamically select model based on file size  
    if file_size_category in ["small", "medium"]:
        model_name = "gemini-2.0-pro-exp-02-05"
    else:
        model_name = "gemini-2.0-flash"

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

    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()