app.py

import os
import re
import gradio as gr
import numpy as np
import faiss
from youtube_transcript_api import YouTubeTranscriptApi
from sentence_transformers import Transformer, SentenceTransformer
from langchain_text_splitters import RecursiveCharacterTextSplitter
from groq import Groq

# ===============================
# CONFIGURATION
# ===============================

# Load Groq API Key from Hugging Face Secrets
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
groq_client = Groq(api_key=GROQ_API_KEY) if GROQ_API_KEY else None

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

# Global variables to store the "brain" of the current video
vector_store = None
chunks_store = []

# ===============================
# CORE FUNCTIONS
# ===============================

def extract_video_id(url):
    """Extracts the 11-character YouTube video ID."""
    # Handles standard URLs, shorts, and shared links
    regex = r"(?:v=|\/|be\/|embed\/|shorts\/)([0-9A-Za-z_-]{11})"
    match = re.search(regex, url)
    return match.group(1) if match else None

def get_transcript(url):
    """Fetches transcript from YouTube."""
    video_id = extract_video_id(url)
    if not video_id:
        return "ERROR: Invalid YouTube URL."
    
    try:
        # Correct Method Call using the imported class
        transcript_list = YouTubeTranscriptApi.get_transcript(video_id)
        text = " ".join([i['text'] for i in transcript_list])
        return text
    except Exception as e:
        return f"ERROR: Could not retrieve transcript. (Details: {str(e)})"

def build_vector_index(text):
    """Chunks text and stores it in a FAISS vector database."""
    global vector_store, chunks_store
    
    # 1. Chunking
    splitter = RecursiveCharacterTextSplitter(chunk_size=600, chunk_overlap=60)
    chunks_store = splitter.split_text(text)
    
    # 2. Embedding
    embeddings = embedding_model.encode(chunks_store)
    
    # 3. Indexing with FAISS
    dimension = embeddings.shape[1]
    index = faiss.IndexFlatL2(dimension)
    index.add(np.array(embeddings).astype('float32'))
    vector_store = index

def get_ai_response(user_query):
    """Retrieves context and asks Groq Llama 3."""
    if vector_store is None or not chunks_store:
        return "Please load a video first."
    
    # Search for relevant chunks
    query_embedding = embedding_model.encode([user_query])
    D, I = vector_store.search(np.array(query_embedding).astype('float32'), k=3)
    context = "\n".join([chunks_store[i] for i in I[0] if i != -1])

    prompt = f"""Use the following video transcript context to answer the question. 
    If the answer isn't in the context, say you don't know based on the video.
    
    Context: {context}
    Question: {user_query}
    Answer:"""

    try:
        completion = groq_client.chat.completions.create(
            model="llama-3.3-70b-versatile",
            messages=[{"role": "user", "content": prompt}]
        )
        return completion.choices[0].message.content
    except Exception as e:
        return f"AI Error: {str(e)}"

# ===============================
# UI LOGIC
# ===============================

def process_video_step(url):
    transcript = get_transcript(url)
    if transcript.startswith("ERROR"):
        return transcript, "❌ Failed"
    
    build_vector_index(transcript)
    return transcript[:1000] + "...", "✅ Video Indexed! Go to Chat tab."

def chat_step(message, history):
    if not GROQ_API_KEY:
        history.append((message, "Error: Groq API Key missing in Secrets."))
        return history, ""
    
    answer = get_ai_response(message)
    history.append((message, answer))
    return history, ""

# ===============================
# GRADIO INTERFACE
# ===============================

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 📺 YouTube AI Expert (RAG)")
    
    with gr.Tabs():
        with gr.Tab("1. Setup Video"):
            url_input = gr.Textbox(label="YouTube URL", placeholder="https://www.youtube.com/watch?v=...")
            process_btn = gr.Button("Process Video", variant="primary")
            status = gr.Textbox(label="Status")
            preview = gr.Textbox(label="Transcript Preview (First 1000 chars)", lines=5)
            
            process_btn.click(process_video_step, inputs=url_input, outputs=[preview, status])
            
        with gr.Tab("2. Chat with Video"):
            chatbot = gr.Chatbot(height=400)
            msg = gr.Textbox(label="Ask anything about the video...")
            clear = gr.ClearButton([msg, chatbot])
            
            msg.submit(chat_step, [msg, chatbot], [chatbot, msg])

if __name__ == "__main__":
    demo.launch()