app.py

import os
import json
import requests
import re
import tempfile
import torch
import soundfile as sf

from bs4 import BeautifulSoup
from groq import Groq
import gradio as gr
from dotenv import load_dotenv
from youtube_transcript_api import YouTubeTranscriptApi

from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan

load_dotenv()

# API KEYS
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
BRIGHT_API_KEY = os.getenv("BRIGHT_API_KEY")
BRIGHT_ZONE = os.getenv("BRIGHT_ZONE")

client = Groq(api_key=GROQ_API_KEY)

CHAT_FILE = "chat_history.json"
PREF_FILE = "preferences.json"


# SAFE JSON FUNCTIONS

def load_json(file, default):
    if os.path.exists(file):
        try:
            with open(file, "r") as f:
                return json.load(f)
        except:
            return default
    return default


def save_json(file, data):
    with open(file, "w") as f:
        json.dump(data, f, indent=4)


conversation_history = load_json(CHAT_FILE, [])
user_preferences = load_json(PREF_FILE, {"style": "Default"})



# LOAD TTS MODEL (Version 4)


print("Loading SpeechT5 model...")

processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
tts_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

speaker_embeddings = torch.randn(1, 512)

print("TTS Model Loaded")



# VERSION 1 — WEBSITE SCRAPER


def brightdata_request(target_url):

    response = requests.post(
        "https://api.brightdata.com/request",
        headers={
            "Content-Type": "application/json",
            "Authorization": f"Bearer {BRIGHT_API_KEY}"
        },
        json={
            "zone": BRIGHT_ZONE,
            "url": target_url,
            "format": "raw"
        }
    )

    return response.text


def scrape_goodreads(url):

    html = brightdata_request(url)

    soup = BeautifulSoup(html, "html.parser")

    books = []

    rows = soup.find_all("tr")

    for row in rows:

        title_tag = row.find("a", class_="bookTitle")
        author_tag = row.find("a", class_="authorName")
        rating_tag = row.find("span", class_="minirating")

        if title_tag and author_tag and rating_tag:

            books.append({
                "title": title_tag.get_text(strip=True),
                "author": author_tag.get_text(strip=True),
                "rating": rating_tag.get_text(strip=True)
            })

    return books[:10]


def qa_bot(url, question):

    books = scrape_goodreads(url)

    if not books:
        return "No book data found."

    context = "\n".join(
        [f"{i+1}. {b['title']} by {b['author']} - {b['rating']}"
         for i, b in enumerate(books)]
    )

    system_prompt = f"""
You are a helpful assistant.
Answer ONLY using the following scraped Goodreads data.

{context}
"""

    response = client.chat.completions.create(
        model="llama-3.1-8b-instant",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": question}
        ]
    )

    return response.choices[0].message.content



# VERSION 2 — YOUTUBE QA


def extract_video_id(url):

    pattern = r"(?:v=|youtu\.be/)([a-zA-Z0-9_-]{11})"

    match = re.search(pattern, url)

    return match.group(1) if match else None


def get_youtube_transcript(url):

    video_id = extract_video_id(url)

    if not video_id:
        return "Invalid YouTube URL."

    try:
        transcript = YouTubeTranscriptApi.get_transcript(video_id)

        full_text = " ".join([entry["text"] for entry in transcript])

        return full_text

    except:
        return "No transcript available for this video."


def youtube_qa(video_url, question):

    transcript = get_youtube_transcript(video_url)

    if transcript.startswith("No") or transcript.startswith("Invalid"):
        return transcript

    system_prompt = f"""
You are a helpful assistant.
Answer ONLY using this transcript.

Transcript:
{transcript[:6000]}
"""

    response = client.chat.completions.create(
        model="llama-3.1-8b-instant",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": question}
        ]
    )

    return response.choices[0].message.content



# VERSION 3 — MEMORY CHAT


def chat_with_memory(user_message, preference_text):

    global conversation_history, user_preferences

    if preference_text and preference_text.strip():

        user_preferences["style"] = preference_text

        save_json(PREF_FILE, user_preferences)

    system_prompt = f"""
You are a helpful AI assistant.

User Preferences:
{user_preferences.get("style","Default")}

Follow the preferred style in all responses.
Maintain conversational memory.
"""

    messages = [{"role": "system", "content": system_prompt}]

    messages.extend(conversation_history)

    messages.append({"role": "user", "content": user_message})

    response = client.chat.completions.create(
        model="llama-3.1-8b-instant",
        messages=messages
    )

    assistant_reply = response.choices[0].message.content

    conversation_history.append({"role": "user", "content": user_message})

    conversation_history.append({"role": "assistant", "content": assistant_reply})

    save_json(CHAT_FILE, conversation_history)

    return assistant_reply


def clear_memory():

    global conversation_history

    conversation_history = []

    save_json(CHAT_FILE, [])

    return []



# VERSION 4 — VOICE AI


def transcribe_audio(audio_path):

    with open(audio_path, "rb") as audio_file:

        transcription = client.audio.transcriptions.create(
            file=audio_file,
            model="whisper-large-v3"
        )

    return transcription.text


def text_to_speech(text):

    inputs = processor(text=text, return_tensors="pt")

    speech = tts_model.generate_speech(
        inputs["input_ids"],
        speaker_embeddings,
        vocoder=vocoder
    )

    temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")

    sf.write(temp_audio.name, speech.numpy(), samplerate=16000)

    return temp_audio.name


def process_text(user_message, preference_text, chat_display):

    if not user_message.strip():
        return "", chat_display, None

    assistant_reply = chat_with_memory(user_message, preference_text)

    chat_display.append({"role": "user", "content": user_message})
    chat_display.append({"role": "assistant", "content": assistant_reply})

    audio_output = text_to_speech(assistant_reply)

    return "", chat_display, audio_output


def process_voice(audio_file, preference_text, chat_display):

    if audio_file is None:
        return chat_display, None

    user_text = transcribe_audio(audio_file)

    assistant_reply = chat_with_memory(user_text, preference_text)

    chat_display.append({"role": "user", "content": user_text})
    chat_display.append({"role": "assistant", "content": assistant_reply})

    audio_output = text_to_speech(assistant_reply)

    return chat_display, audio_output



# GRADIO UI


with gr.Blocks() as demo:

    gr.Markdown("# 🚀 Full AI Assistant (V1 + V2 + V3 + V4)")

    # TAB 1
    with gr.Tab("🌐 Website Scraper Q&A"):

        url_input = gr.Textbox(
            label="Enter Goodreads URL",
            value="https://www.goodreads.com/list/show/1.Best_Books_Ever"
        )

        question_input = gr.Textbox(label="Ask your question")

        output1 = gr.Textbox(label="Answer")

        btn1 = gr.Button("Submit")

        btn1.click(
            qa_bot,
            inputs=[url_input, question_input],
            outputs=output1
        )

    # TAB 2
    with gr.Tab("🎥 YouTube Transcript Q&A"):

        video_input = gr.Textbox(label="Enter YouTube URL")

        yt_question = gr.Textbox(label="Ask your question")

        output2 = gr.Textbox(label="Answer")

        btn2 = gr.Button("Submit")

        btn2.click(
            youtube_qa,
            inputs=[video_input, yt_question],
            outputs=output2
        )

    # TAB 3
    with gr.Tab("🧠 Memory Chatbot"):

        chatbot = gr.Chatbot(label="Conversation", value=conversation_history)

        preference_input = gr.Textbox(
            label="User Preferences (Optional)"
        )

        user_message = gr.Textbox(label="Your Message")

        send_btn = gr.Button("Send")

        clear_btn = gr.Button("Clear Memory")

        def chat_interface(user_message, preference_text, chat_display):

            if not user_message.strip():
                return "", chat_display

            assistant_reply = chat_with_memory(user_message, preference_text)

            chat_display.append({"role": "user", "content": user_message})

            chat_display.append({"role": "assistant", "content": assistant_reply})

            return "", chat_display

        send_btn.click(
            chat_interface,
            inputs=[user_message, preference_input, chatbot],
            outputs=[user_message, chatbot]
        )

        clear_btn.click(
            clear_memory,
            outputs=chatbot
        )

    # TAB 4
    with gr.Tab("🎙️ Voice AI Assistant"):

        chatbot_v4 = gr.Chatbot(label="Conversation", value=conversation_history)

        preference_input_v4 = gr.Textbox(
            label="User Preferences (Optional)"
        )

        user_message_v4 = gr.Textbox(label="Type your message")

        audio_input = gr.Audio(
            sources=["microphone"],
            type="filepath",
            label="Speak your question"
        )

        audio_output = gr.Audio(label="Assistant Voice Response")

        send_btn_v4 = gr.Button("Send Text")

        voice_btn = gr.Button("Send Voice")

        clear_btn_v4 = gr.Button("Clear Memory")

        send_btn_v4.click(
            process_text,
            inputs=[user_message_v4, preference_input_v4, chatbot_v4],
            outputs=[user_message_v4, chatbot_v4, audio_output]
        )

        voice_btn.click(
            process_voice,
            inputs=[audio_input, preference_input_v4, chatbot_v4],
            outputs=[chatbot_v4, audio_output]
        )

        clear_btn_v4.click(
            clear_memory,
            outputs=chatbot_v4
        )


if __name__ == "__main__":

    demo.launch()