app.py

import os
import streamlit as st
from groq import Groq
from PyPDF2 import PdfReader
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import whisper
from gtts import gTTS
from tempfile import NamedTemporaryFile
import json
import gdown

# Initialize Groq client
client = Groq(api_key="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")

# Load embedding model
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")

# Load Whisper model
whisper_model = whisper.load_model("base")

# Initialize FAISS
embedding_dimension = 384  # Dimension of embeddings from the model
index = faiss.IndexFlatL2(embedding_dimension)
metadata = []

# List of Google Drive PDF links
google_drive_links = [
    "https://drive.google.com/file/d/1_9vZ5jw6Lpoh7jDnqqIiyq082d3uT2dp/view?usp=sharing"
]

# Streamlit App Configuration
st.set_page_config(page_title="Voice/Text Chatbot with RAG PDF Query", page_icon="🔊", layout="wide")

# Title
st.markdown("<h1 style='text-align: center; color: #006400;'> ProManage AI </h1>", unsafe_allow_html=True)
st.markdown("---")

# Sidebar for PDF Upload
st.sidebar.header("Upload Your PDF File")
uploaded_file = st.sidebar.file_uploader("Upload a PDF file", type="pdf")

# Function to extract file ID from Google Drive link
def extract_file_id(drive_link):
    return drive_link.split("/d/")[1].split("/view")[0]

# Function to download PDF from Google Drive
def download_pdf_from_google_drive(file_id, output_path):
    download_url = f"https://drive.google.com/uc?id={file_id}"
    gdown.download(download_url, output_path, quiet=False)

# Function for text extraction from PDF
def extract_text_from_pdf(file):
    reader = PdfReader(file)
    text = ""
    for page in reader.pages:
        text += page.extract_text()
    return text

# Function for text-to-speech
def text_to_speech(response_text):
    tts = gTTS(text=response_text, lang="en")
    audio_file = NamedTemporaryFile(delete=False, suffix=".mp3")
    tts.save(audio_file.name)
    return audio_file.name

# Save embeddings and metadata
def save_database(faiss_index, metadata, file_path="vector_database.json"):
    all_embeddings = []
    for i in range(faiss_index.ntotal):
        all_embeddings.append(faiss_index.reconstruct(i).tolist())
    data = {
        "embeddings": all_embeddings,
        "metadata": metadata
    }
    with open(file_path, "w") as f:
        json.dump(data, f)
    st.success(f"Vector database saved to {file_path}!")

# Process Google Drive PDFs
st.sidebar.header("Processing Google Drive PDFs")
with st.spinner("Downloading and processing Google Drive PDFs..."):
    for link in google_drive_links:
        file_id = extract_file_id(link)
        output_pdf_path = f"downloaded_{file_id}.pdf"

        # Download PDF
        if not os.path.exists(output_pdf_path):  # Avoid re-downloading
            download_pdf_from_google_drive(file_id, output_pdf_path)

        # Extract text and process
        pdf_text = extract_text_from_pdf(output_pdf_path)
        if pdf_text.strip():
            # Split text into chunks and create embeddings
            chunk_size = 500
            chunks = [pdf_text[i:i + chunk_size] for i in range(0, len(pdf_text), chunk_size)]
            embeddings = embedding_model.encode(chunks, convert_to_numpy=True)
            index.add(embeddings)

            # Store metadata
            metadata.extend([{"chunk": chunk, "source": f"Google Drive: {output_pdf_path}"} for chunk in chunks])

# PDF Text Processing
if uploaded_file:
    pdf_text = extract_text_from_pdf(uploaded_file)
    if pdf_text.strip():
        st.success("PDF text successfully extracted!")
        with st.expander("View Extracted Text", expanded=False):
            st.write(pdf_text[:3000] + "..." if len(pdf_text) > 3000 else pdf_text)

        # Split text into chunks and create embeddings
        chunk_size = 500
        chunks = [pdf_text[i:i + chunk_size] for i in range(0, len(pdf_text), chunk_size)]
        embeddings = embedding_model.encode(chunks, convert_to_numpy=True)
        index.add(embeddings)

        # Store metadata
        metadata.extend([{"chunk": chunk, "source": uploaded_file.name} for chunk in chunks])
        save_database(index, metadata)

        st.success(f"Processed {len(chunks)} chunks and stored embeddings in FAISS!")

# Main Chatbot Interface
st.header("🤖 Gen-AI Powered Chatbot")

# Input Method Selection
input_method = st.radio("Select Input Method:", options=["Text", "Audio"])

if input_method == "Text":
    st.subheader("💬 Text Query Input")
    text_query = st.text_input("Enter your query:")
    if st.button("Submit Text Query"):
        if text_query:
            try:
                # Search FAISS for nearest chunks
                query_embedding = embedding_model.encode([text_query], convert_to_numpy=True)
                distances, indices = index.search(query_embedding, k=5)
                relevant_chunks = [metadata[idx]["chunk"] for idx in indices[0]]

                # Generate response using Groq API
                prompt = f"Use these references to answer the query:\n\n{relevant_chunks}\n\nQuery: {text_query}"
                chat_completion = client.chat.completions.create(
                    messages=[{"role": "user", "content": prompt}],
                    model="llama-3.3-70b-versatile",
                )
                response = chat_completion.choices[0].message.content

                # Display text response
                st.write(f"**Chatbot Response:** {response}")

                # Generate and play audio response
                response_audio_path = text_to_speech(response)
                st.audio(response_audio_path, format="audio/mp3", start_time=0)

            except Exception as e:
                st.error(f"Error processing your query: {e}")

elif input_method == "Audio":
    st.subheader("🎤 Audio Query Input")
    uploaded_audio = st.file_uploader("Upload your audio file", type=["m4a", "mp3", "wav"])

    if uploaded_audio:
        try:
            audio_data = uploaded_audio.read()
            audio_file = NamedTemporaryFile(delete=False, suffix=".m4a")
            audio_file.write(audio_data)
            audio_file_path = audio_file.name

            st.success("Audio file uploaded successfully!")

            # Transcribe the audio using Whisper model
            transcription = whisper_model.transcribe(audio_file_path)["text"]
            st.write(f"**You said:** {transcription}")

            # Search FAISS for nearest chunks
            query_embedding = embedding_model.encode([transcription], convert_to_numpy=True)
            distances, indices = index.search(query_embedding, k=5)
            relevant_chunks = [metadata[idx]["chunk"] for idx in indices[0]]

            # Generate response using Groq API
            prompt = f"Use these references to answer the query:\n\n{relevant_chunks}\n\nQuery: {transcription}"
            chat_completion = client.chat.completions.create(
                messages=[{"role": "user", "content": prompt}],
                    model="llama-3.3-70b-versatile",
                )
            response = chat_completion.choices[0].message.content

            # Display text response
            st.write(f"**Chatbot Response:** {response}")

            # Generate and play audio response
            response_audio_path = text_to_speech(response)
            st.audio(response_audio_path, format="audio/mp3", start_time=0)

        except Exception as e:
            st.error(f"Error processing your query: {e}")

# Footer
st.markdown("<p style='text-align: center;'> Muhammad Zaeem Ilyas-PMP®| PMO NESPAK </p>", unsafe_allow_html=True)