Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import faiss
+import numpy as np
+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
+# --------------------------
+@st.cache_resource
+def load_movie_data():
+    # Synthetic movie dataset (replace with wiki_movies loading)
+    movies = [
+        {
+            "title": "The Dark Knight",
+            "plot": "Batman faces the Joker in a battle for Gotham's soul...",
+            "cast": {"Christian Bale": "Bruce Wayne", "Heath Ledger": "Joker"},
+            "director": "Christopher Nolan",
+            "year": 2008
+        },
+        {
+            "title": "Inception",
+            "plot": "A thief who enters the dreams of others...",
+            "cast": {"Leonardo DiCaprio": "Cobb", "Tom Hardy": "Eames"},
+            "director": "Christopher Nolan",
+            "year": 2010
+        }
+    ]
+    df = pd.DataFrame(movies)
+    df['context'] = df.apply(lambda x: f"Title: {x.title}\nPlot: {x.plot}\nCast: {', '.join([f'{k} as {v}' for k,v in x.cast.items()])}\nDirector: {x.director}\nYear: {x.year}", 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
+# --------------------------
+client = 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()