Create app.py

app.py

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+# ================================
+# IMPORTS
+# ================================
+from youtube_transcript_api import YouTubeTranscriptApi
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import gradio as gr
+from groq import Groq
+import re
+import os
+
+# ================================
+# CONFIG
+# ================================
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")  # 🔐 Use HF secrets
+client = Groq(api_key=GROQ_API_KEY)
+
+embed_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# Global store
+vector_store = None
+stored_chunks = []
+
+# ================================
+# UTIL: EXTRACT VIDEO ID
+# ================================
+def extract_video_id(url):
+    match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11})", url)
+    return match.group(1) if match else None
+
+# ================================
+# STEP 1: GET TRANSCRIPT
+# ================================
+def get_transcript(url):
+    video_id = extract_video_id(url)
+    if not video_id:
+        return "❌ Invalid YouTube URL"
+
+    try:
+        api = YouTubeTranscriptApi()
+        transcript = api.fetch(video_id)
+
+        full_text = " ".join([t.text for t in transcript])
+        return full_text
+
+    except Exception as e:
+        return f"❌ Transcript Error: {str(e)}"
+
+# ================================
+# STEP 2: CHUNKING
+# ================================
+def chunk_text(text, chunk_size=300):
+    words = text.split()
+    chunks = []
+
+    for i in range(0, len(words), chunk_size):
+        chunk = " ".join(words[i:i + chunk_size])
+        chunks.append(chunk)
+
+    return chunks
+
+# ================================
+# STEP 3: VECTOR STORE
+# ================================
+def create_vector_store(chunks):
+    global vector_store, stored_chunks
+
+    embeddings = embed_model.encode(chunks)
+
+    dim = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dim)
+    index.add(np.array(embeddings))
+
+    vector_store = index
+    stored_chunks = chunks
+
+# ================================
+# STEP 4: RETRIEVAL
+# ================================
+def retrieve(query, top_k=3):
+    query_embedding = embed_model.encode([query])
+    distances, indices = vector_store.search(np.array(query_embedding), top_k)
+
+    results = [stored_chunks[i] for i in indices[0]]
+    return "\n".join(results)
+
+# ================================
+# STEP 5: LLM
+# ================================
+def generate_answer(query, context):
+    prompt = f"""
+You are a helpful assistant.
+
+Use ONLY the context below to answer the question.
+
+Context:
+{context}
+
+Question:
+{query}
+
+Answer:
+"""
+
+    response = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=[{"role": "user", "content": prompt}],
+        temperature=0.3
+    )
+
+    return response.choices[0].message.content
+
+# ================================
+# HANDLERS
+# ================================
+def handle_process(url):
+    transcript = get_transcript(url)
+
+    if transcript.startswith("❌"):
+        return transcript, "", []
+
+    chunks = chunk_text(transcript)
+    create_vector_store(chunks)
+
+    preview = transcript[:500]
+
+    return "✅ Video processed successfully!", preview, []
+
+def handle_chat(query, chat_history):
+    if vector_store is None:
+        return "", chat_history + [(query, "❌ Process a video first")]
+
+    context = retrieve(query)
+    answer = generate_answer(query, context)
+
+    chat_history.append((query, answer))
+    return "", chat_history
+
+# ================================
+# UI
+# ================================
+with gr.Blocks(theme=gr.themes.Soft()) as app:
+
+    gr.Markdown("# 🎥 YouTube Video Assistant")
+    gr.Markdown("Paste a YouTube link → process → chat with the video")
+
+    with gr.Row():
+        url_input = gr.Textbox(label="🔗 YouTube URL", scale=4)
+        process_btn = gr.Button("🚀 Process", scale=1)
+
+    status_output = gr.Markdown("")
+
+    transcript_preview = gr.Textbox(
+        label="📄 Transcript Preview",
+        lines=5,
+        interactive=False
+    )
+
+    gr.Markdown("---")
+
+    chatbot = gr.Chatbot(label="💬 Chat with Video")
+
+    with gr.Row():
+        query_input = gr.Textbox(
+            placeholder="Ask something about the video...",
+            scale=4
+        )
+        send_btn = gr.Button("Send", scale=1)
+
+    process_btn.click(
+        handle_process,
+        inputs=url_input,
+        outputs=[status_output, transcript_preview, chatbot]
+    )
+
+    send_btn.click(
+        handle_chat,
+        inputs=[query_input, chatbot],
+        outputs=[query_input, chatbot]
+    )
+
+app.launch()