Upload app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# pipeline 1: zero-shot to identify movie genres
+genre_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
+# pipeline 2: text2text-generation for movie descriptions
+desc_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
+
+# fine-tuned model (pipeline 3): local movie recommender model
+rec_pipeline = pipeline("text-classification", model="model")
+
+candidate_movies = [
+    "Inception", "The Matrix", "Interstellar", "Titanic", "The Dark Knight",
+    "The Godfather", "Pulp Fiction", "The Shawshank Redemption", "Forrest Gump", "Avengers"
+]
+
+movie_genres = ["sci-fi", "drama", "romance", "action", "crime", "thriller", "adventure"]
+
+def recommend_movies(input_movies):
+    if not input_movies.strip():
+        return "⚠️ Please enter at least one movie.", []
+
+    genres = genre_classifier(input_movies, candidate_labels=movie_genres, multi_label=True)
+    top_genres = genres["labels"][:2]
+
+    # Use fine-tuned model to recommend a movie label (simplified)
+    try:
+        rec_results = rec_pipeline(f"{input_movies} | genres: {', '.join(top_genres)}")
+    except Exception as e:
+        return f"❌ Recommendation model error: {e}", []
+
+    top_rec = rec_results[:5]
+    gallery = []
+    for item in top_rec:
+        title = item["label"].replace("LABEL_", "").strip()
+        score = item["score"]
+        try:
+            desc = desc_pipeline(f"Describe the movie {title} in one sentence.", max_length=40)[0]["generated_text"]
+        except:
+            desc = "No description available."
+        img_url = f"https://via.placeholder.com/200x300?text={title.replace(' ', '+')}"
+        label = f"🎞️ **{title}**\n\n{desc}\n\nConfidence: {score:.2f}"
+        gallery.append((img_url, label))
+
+    summary = f"🎬 Based on your input and detected genres ({', '.join(top_genres)}), we recommend:"
+    return summary, gallery
+
+with gr.Blocks(title="🎥 Movie Recommender") as demo:
+    gr.Markdown("# 🎬 Personalized Movie Recommendation\n_Using Hugging Face pipelines + fine-tuned model_")
+    with gr.Row():
+        input_box = gr.Textbox(label="Enter up to 3 movies you like", placeholder="e.g. Inception, Titanic")
+        btn = gr.Button("🎯 Recommend")
+    output_text = gr.Markdown()
+    output_gallery = gr.Gallery(columns=2)
+    btn.click(fn=recommend_movies, inputs=input_box, outputs=[output_text, output_gallery])
+
+demo.launch()