app.py

import gradio as gr
from transformers import pipeline

# Load the movie recommendation model
model_name = "AventIQ-AI/all-MiniLM-L6-v2-movie-recommendation-system"
retriever = pipeline("feature-extraction", model=model_name)

def recommend_movies(movie_description):
    """Generates movie recommendations based on user input."""
    if not movie_description.strip():
        return "⚠️ Please enter a movie description or genre."

    # Dummy output (replace with actual model inference logic)
    recommendations = [
        "Inception (2010)",
        "Interstellar (2014)",
        "The Matrix (1999)",
        "Blade Runner 2049 (2017)",
        "The Dark Knight (2008)"
    ]
    
    return "\n".join(recommendations[:3])  # Return top 3 recommendations

# Example Inputs
example_descriptions = [
    "A mind-bending sci-fi thriller about dreams within dreams.",
    "A group of superheroes saves the world from an alien invasion.",
    "A gripping crime drama featuring a brilliant detective.",
    "A heartwarming animated movie about friendship and adventure."
]

# Create Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 🎬 AI-Powered Movie Recommendation System")
    gr.Markdown("Enter a movie description or genre, and the model will suggest similar movies!")

    with gr.Row():
        input_text = gr.Textbox(label="🎥 Enter a movie description or genre:", 
                                placeholder="Example: A sci-fi adventure through space and time.")

    recommend_button = gr.Button("🔍 Get Recommendations")
    output_text = gr.Textbox(label="🍿 Recommended Movies:")

    gr.Markdown("### 🎭 Example Inputs")
    example_buttons = [gr.Button(example) for example in example_descriptions]

    for btn in example_buttons:
        btn.click(fn=lambda text=btn.value: text, outputs=input_text)

    recommend_button.click(recommend_movies, inputs=input_text, outputs=output_text)

# Launch the Gradio app
demo.launch()