diff --git a/app.py b/app.py
new file mode 100644
index 0000000..d523071
--- /dev/null
+++ b/app.py
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+import streamlit as st
+import pandas as pd
+import numpy as np
+from surprise import Dataset, Reader, SVD
+import requests
+import pickle
+
+# Set page config
+st.set_page_config(page_title='STREAMFLIX', page_icon="🎬", layout='wide')
+
+# Load your data
+@st.cache_data
+def load_data():
+    df = pd.read_csv('movies_data/movies.csv')
+    ratings = pd.read_csv('movies_data/ratings.csv')
+    return df, ratings
+
+# Train your model
+@st.cache_resource
+def train_model(ratings):
+    reader = Reader(rating_scale=(1, 5))
+    data = Dataset.load_from_df(ratings[['userId_x', 'movieId', 'rating']], reader)
+    model = SVD()
+    model.fit(data.build_full_trainset())
+    return model
+
+# Get recommendations
+def get_recommendations(model, df, user_ratings, n=5, genre=None):
+    new_user_id = df['userId_x'].max() + 1
+    movies_to_predict = df[~df['movieId'].isin([x[0] for x in user_ratings])]['movieId'].unique()
+    
+    predictions = []
+    for movie_id in movies_to_predict:
+        predicted_rating = model.predict(new_user_id, movie_id).est
+        predictions.append((movie_id, predicted_rating))
+    
+    recommendations = sorted(predictions, key=lambda x: x[1], reverse=True)
+    
+    if genre:
+        genre_recommendations = [
+            (movie_id, rating) for movie_id, rating in recommendations
+            if genre.lower() in df[df['movieId'] == movie_id]['genres'].iloc[0].lower()
+        ]
+        return genre_recommendations[:n]
+    else:
+        return recommendations[:n]
+
+# Fetch movie poster
+@st.cache_data
+def fetch_poster(movie_id):
+    url = f"https://api.themoviedb.org/3/movie/{movie_id}?api_key=your_api_key"
+    response = requests.get(url)
+    data = response.json()
+    return "https://image.tmdb.org/t/p/w500/" + data.get('poster_path', '')
+
+# Main app
+def main():
+    st.title("🎬 Streamflix: Hybrid Movie Recommendation System")
+
+    # Load data
+    df, ratings = load_data()
+    model = train_model(ratings)
+
+    # Sidebar
+    st.sidebar.title('Navigation')
+    page = st.sidebar.radio('Go to', ['Home', 'Get Recommendations', 'Search Movie'])
+
+    if page == 'Home':
+        st.header('🔥 Top Trending Movies')
+        top_movies = df.sort_values('popularity', ascending=False).head(10)
+        
+        for _, movie in top_movies.iterrows():
+            col1, col2 = st.columns([1, 3])
+            with col1:
+                poster_url = fetch_poster(movie['id'])
+                st.image(poster_url, width=150)
+            with col2:
+                st.subheader(movie['title'])
+                st.write(f"Genres: {movie['genres']}")
+                st.write(f"Average Rating: {movie['vote_average']:.1f}/10")
+                if st.button(f"Rate {movie['title']}", key=f"rate_{movie['id']}"):
+                    rating = st.slider('Your rating', 0.5, 5.0, 3.0, 0.5, key=f"slider_{movie['id']}")
+                    st.write(f"You rated {movie['title']} {rating} stars!")
+            st.write(''---'')
+
+    elif page == 'Get Recommendations':
+        st.header('🎯 Get Personalized Recommendations')
+        user_id = st.number_input('Please enter your user ID', min_value=1, step=1)
+        genres = st.multiselect('Select genres', df['genres'].explode().unique())
+        
+        if st.button('Get Recommendations'):
+            recommendations = get_recommendations(user_id, model, df, ratings)
+            if genres:
+                recommendations = recommendations[recommendations['genres'].apply(lambda x: any(genre in x for genre in genres))]
+            
+            st.subheader('Your Recommended Movies:')
+            for _, movie in recommendations.iterrows():
+                col1, col2 = st.columns([1, 3])
+                with col1:
+                    poster_url = fetch_poster(movie['id'])
+                    st.image(poster_url, width=150)
+                with col2:
+                    st.write(f'**{movie['title']}**')
+                    st.write(f'Genres: {movie['genres']}')
+                    st.write(f'Average Rating: {movie['vote_average']:.1f}/10')
+                    if st.button(f'Watch Trailer for {movie['title']}', key=f'trailer_{movie['id']}'):
+                        # You would need to implement a function to fetch and display the trailer
+                        st.video('https://www.youtube.com/watch?v=dQw4w9WgXcQ')  # Placeholder
+                st.write('---')
+
+    elif page == 'Search Movies':
+        st.header('🔍 Search Movies')
+        search_term = st.text_input('Enter a movie title')
+        if search_term:
+            results = df[df['title'].str.contains(search_term, case=False)]
+            for _, movie in results.iterrows():
+                col1, col2 = st.columns([1, 3])
+                with col1:
+                    poster_url = fetch_poster(movie['id'])
+                    st.image(poster_url, width=150)
+                with col2:
+                    st.subheader(movie['title'])
+                    st.write(f'Genres: {movie['genres']}')
+                    st.write(f'Average Rating: {movie['vote_average']:.1f}/10')
+                    st.write(f'Overview: {movie['overview'][:200]}...')
+                st.write('---')
+
+if __name__ == '__main__':
+    main()
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