app.py

import streamlit as st
import pandas as pd
import faiss
import numpy as np
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from groq import Groq
import os

# --------------------------
# Configuration & Styling
# --------------------------
st.set_page_config(
    page_title="CineMaster AI - Movie Expert",
    page_icon="🎬",
    layout="wide",
    initial_sidebar_state="expanded"
)

st.markdown("""
<style>
    :root {
        --primary: #7017ff;
        --secondary: #ff2d55;
    }
    .header {
        background: linear-gradient(135deg, var(--primary), var(--secondary));
        color: white;
        padding: 2rem;
        border-radius: 15px;
        text-align: center;
        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
    }
    .response-box {
        background: rgba(255,255,255,0.1);
        border-radius: 10px;
        padding: 1.5rem;
        margin: 1rem 0;
        border: 1px solid rgba(255,255,255,0.2);
    }
    .stButton>button {
        background: linear-gradient(45deg, var(--primary), var(--secondary)) !important;
        color: white !important;
        border-radius: 25px;
        padding: 0.8rem 2rem;
        font-weight: 600;
        transition: transform 0.2s;
    }
    .stButton>button:hover {
        transform: scale(1.05);
    }
</style>
""", unsafe_allow_html=True)

# --------------------------
# Movie Dataset & Embeddings
# --------------------------
# Replace load_movie_data() with:
@st.cache_resource
def load_movie_data():
    dataset = load_dataset("wiki_movies", split="train")
    df = pd.DataFrame(dataset)
    df['context'] = df.apply(lambda x: f"Title: {x['title']}\nPlot: {x['plot']}\nCast: {x['cast']}", axis=1)
    return df

@st.cache_resource
def setup_retrieval(df):
    embedder = SentenceTransformer('all-MiniLM-L6-v2')
    embeddings = embedder.encode(df['context'].tolist())
    
    index = faiss.IndexFlatL2(embeddings.shape[1])
    index.add(embeddings)
    return embedder, index

# --------------------------
# Groq API Setup
# --------------------------
def get_groq_client():
    return Groq(
        api_key=os.getenv("GROQ_API_KEY", "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")
    )

def movie_expert(query, context):
    prompt = f"""You are a film expert. Answer using this context:
    
    {context}
    
    Question: {query}
    
    Format response with:
    1. 🎥 Direct Answer
    2. 📖 Detailed Explanation
    3. 🏆 Key Cast Members
    4. 🌟 Trivia (if available)
    """
    
    response = client.chat.completions.create(
        messages=[{"role": "user", "content": prompt}],
        model="llama3-70b-8192",
        temperature=0.3
    )
    return response.choices[0].message.content

# --------------------------
# Main Application
# --------------------------
def main():
    df = load_movie_data()
    embedder, index = setup_retrieval(df)
    
    # Header Section
    st.markdown("""
    <div class="header">
        <h1>🎞️ CineMaster AI</h1>
        <h3>Your Personal Movie Encyclopedia</h3>
    </div>
    """, unsafe_allow_html=True)
    
    # Sidebar
    with st.sidebar:
        st.image("https://cdn-icons-png.flaticon.com/512/2598/2598702.png", width=120)
        st.subheader("Sample Questions")
        examples = [
            "Who played the Joker in The Dark Knight?",
            "What's the plot of Inception?",
            "List Christopher Nolan's movies",
            "Who directed The Dark Knight?",
            "What year was Inception released?"
        ]
        for ex in examples:
            st.code(ex, language="bash")
    
    # Main Interface
    query = st.text_input("🎯 Ask any movie question:", 
                        placeholder="e.g., 'Who played the villain in The Dark Knight?'")
    
    if st.button("🚀 Get Answer"):
        if query:
            with st.spinner("🔍 Searching through 10,000+ movie records..."):
                query_embed = embedder.encode([query])
                _, indices = index.search(query_embed, 2)
                contexts = [df.iloc[i]['context'] for i in indices[0]]
                combined_context = "\n\n".join(contexts)
                
            with st.spinner("🎥 Generating cinematic insights..."):
                answer = movie_expert(query, combined_context)
                
            st.markdown("---")
            with st.container():
                st.markdown("## 🎬 Expert Analysis")
                st.markdown(f'<div class="response-box">{answer}</div>', unsafe_allow_html=True)
                
                st.markdown("## 📚 Source Materials")
                cols = st.columns(2)
                for i, ctx in enumerate(contexts):
                    with cols[i]:
                        with st.expander(f"Source {i+1}", expanded=True):
                            st.write(ctx)
        else:
            st.warning("Please enter a movie-related question")

if __name__ == "__main__":
    main()