app.py

import streamlit as st
from transformers import pipeline
import random

# Load the zero-shot classification model
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

# Predefined movie list by genre
movie_recommendations = {
    "Action": ["Mad Max: Fury Road", "John Wick", "Die Hard"],
    "Adventure": ["Indiana Jones", "The Revenant", "Jurassic Park"],
    "Comedy": ["Superbad", "Step Brothers", "The Hangover"],
    "Drama": ["The Godfather", "Forrest Gump", "The Pursuit of Happyness"],
    "Horror": ["The Conjuring", "Get Out", "A Nightmare on Elm Street"],
    "Sci-Fi": ["Interstellar", "Blade Runner 2049", "The Matrix"],
    "Fantasy": ["Harry Potter", "The Lord of the Rings", "Pan’s Labyrinth"]
}

# Streamlit UI
st.title("🎬 AI Movie Recommender")
st.write("Tell me how you're feeling, and I'll recommend a movie!")

user_input = st.text_input("How are you feeling today?", "I feel adventurous")

if st.button("Get Recommendation"):  
    # Classify mood to genres
    genres = list(movie_recommendations.keys())
    result = classifier(user_input, genres)
    best_genre = result["labels"][0]  # Top predicted genre
    
    # Select a random movie from the predicted genre
    recommended_movie = random.choice(movie_recommendations[best_genre])
    
    st.success(f"Based on your mood, I recommend: **{recommended_movie}** ({best_genre} genre) 🎥")

st.write("Powered by Hugging Face 🤗 and Streamlit 🚀")