app.py

import gradio as gr
import torch
import faiss
import numpy as np
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
import pdfplumber
import docx

# Load RAG Model
model_name = "facebook/rag-sequence-nq"
tokenizer = RagTokenizer.from_pretrained(model_name)
retriever = RagRetriever.from_pretrained(model_name, index_name="exact", use_dummy_dataset=True)
model = RagSequenceForGeneration.from_pretrained(model_name, retriever=retriever)

# FAISS Vector Store
dimension = 768  
index = faiss.IndexFlatL2(dimension)  
stored_docs = []  
chat_history = []  

# Extract text from uploaded files
def extract_text(files):
    texts = []
    for file in files:
        file_name = file.name
        file_ext = file_name.split('.')[-1].lower()
        text = ""

        if file_ext == "txt":
            text = file.read().decode("utf-8")

        elif file_ext == "pdf":
            with pdfplumber.open(file) as pdf:
                for page in pdf.pages:
                    text += page.extract_text() + "\n"

        elif file_ext == "docx":
            doc = docx.Document(file)
            for para in doc.paragraphs:
                text += para.text + "\n"

        else:
            return "Unsupported file format! Upload TXT, PDF, or DOCX."
        
        texts.append(text.strip())
        store_in_faiss(text.strip())

    return "\n\n---\n\n".join(texts)  

# Store document in FAISS
def store_in_faiss(document):
    global index, stored_docs
    if not document.strip():
        return

    # Tokenize and get embeddings
    inputs = tokenizer(document, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        embeddings = model.rag.retriever(input_ids=inputs["input_ids"]).cpu().numpy()
    
    index.add(embeddings)
    stored_docs.append(document)

# Retrieve the most relevant document from FAISS
def retrieve_relevant_doc(query):
    if index.ntotal == 0:
        return ""

    # Tokenize query and get embeddings
    inputs = tokenizer(query, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        query_embedding = model.rag.retriever(input_ids=inputs["input_ids"]).cpu().numpy()

    _, top_idx = index.search(query_embedding, k=1)
    return stored_docs[top_idx[0][0]]

# Answer questions using RAG with FAISS and maintain chat history
def chat_with_ai(history, question):
    if not stored_docs:
        return history + [[question, "Please upload a document first."]]

    relevant_doc = retrieve_relevant_doc(question)
    
    chat_context = "\n".join(["User: " + q + "\nAI: " + a for q, a in history])
    full_input = f"Context: {chat_context}\n\nDocument: {relevant_doc}\n\nQuestion: {question}"
    
    inputs = tokenizer(question, relevant_doc, return_tensors="pt", truncation=True)
    with torch.no_grad():
        generated = model.generate(**inputs)
    answer = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]

    history.append([question, answer])  
    return history, answer  

# Gradio UI with Chat Interface, Voice Input & Text-to-Speech
with gr.Blocks(theme=gr.themes.Soft(), css="""
    .gradio-container {background-color: #1E1E1E; color: #FFFFFF;}
    .voice-btn, .speak-btn {background-color: #FFA500; color: black; border-radius: 5px; padding: 5px;}
""") as app:
    gr.Markdown("# 🎙️ AI-Powered Document Chatbot with Voice Input & AI Speech", elem_id="title")

    with gr.Row():
        file_input = gr.File(label="Upload Documents (TXT, PDF, DOCX)", type="file", multiple=True)
        file_output = gr.Textbox(label="Extracted Text (Editable)", lines=10)

    file_input.change(extract_text, inputs=file_input, outputs=file_output)

    editor = gr.Textbox(label="Editor Canvas (Modify Extracted Text)", lines=10)
    file_output.change(lambda x: x, inputs=file_output, outputs=editor)

    chatbot = gr.Chatbot(label="AI Chat Assistant", elem_id="chatbot")
    question_input = gr.Textbox(label="Ask AI a Question", placeholder="Type or use voice...")

    with gr.Row():
        send_btn = gr.Button("Send", elem_id="send-btn")
        voice_btn = gr.Button("🎤 Voice", elem_id="voice-btn")
        speak_btn = gr.Button("🗣️ Speak Answer", elem_id="speak-btn")

    send_btn.click(chat_with_ai, inputs=[chatbot, question_input], outputs=[chatbot, None])

    voice_btn.click(None, _js="""
        () => {
            const recognition = new webkitSpeechRecognition() || new SpeechRecognition();
            recognition.lang = "en-US";
            recognition.start();
            recognition.onresult = function(event) {
                let transcript = event.results[0][0].transcript;
                document.querySelector('textarea').value = transcript;
            };
        }
    """)

    speak_btn.click(None, _js="""
        () => {
            let lastMsg = document.querySelectorAll('.chat-message:last-child .chat-response')[0].innerText;
            let utterance = new SpeechSynthesisUtterance(lastMsg);
            utterance.lang = "en-US";
            utterance.rate = 1.0;
            speechSynthesis.speak(utterance);
        }
    """)

app.launch()