Create app.py

app.py

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+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
+
+# ✅ 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
+    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
+    model = ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key=GOOGLE_API_KEY)
+    qa_chain = RetrievalQA.from_chain_type(llm=model, retriever=retriever)
+    response = qa_chain.invoke({"query": question})["result"]
+    tts = gTTS(text=response, lang="en")
+    temp_audio_path = os.path.join(temp_dir, "response.mp3")
+    tts.save(temp_audio_path)
+    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)
+    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")
+    process_button.click(process_documents, inputs=file_dropdown, outputs=response_output)
+    submit_button.click(query_document, inputs=user_input, outputs=[response_output, audio_output])
+
+demo.launch()
+