Create app.py

app.py

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+import gradio as gr
+import os
+import pickle
+import numpy as np
+from surprise import SVDpp
+from sklearn.metrics.pairwise import cosine_similarity
+
+# --- Download Model from Kaggle ---
+def download_kaggle_model():
+    kaggle_username = os.environ.get("KAGGLE_USERNAME")
+    kaggle_key = os.environ.get("KAGGLE_KEY")
+    if not kaggle_username or not kaggle_key:
+        raise ValueError("Set KAGGLE_USERNAME and KAGGLE_KEY as HF secrets!")
+
+    os.system("mkdir -p ~/.kaggle")
+    with open("/root/.kaggle/kaggle.json", "w") as f:
+        f.write(f'{{"username":"{kaggle_username}","key":"{kaggle_key}"}}')
+    os.chmod("/root/.kaggle/kaggle.json", 0o600)
+
+    # Download your Kaggle model
+    print("📥 Downloading model from Kaggle...")
+    os.system("kaggle datasets download -d <your-username>/<your-model-dataset> -p ./model --unzip")
+
+# --- Load Models ---
+def load_models():
+    with open("./model/svdpp_model.pkl", "rb") as f:
+        svdpp_model = pickle.load(f)
+
+    with open("./model/content_features.pkl", "rb") as f:
+        content_features = pickle.load(f)
+
+    with open("./model/mappings.pkl", "rb") as f:
+        mappings = pickle.load(f)
+
+    return svdpp_model, content_features, mappings
+
+# --- Hybrid Prediction ---
+def hybrid_predict(user_id, book_id, alpha=0.7):
+    try:
+        uid = user_encoder.transform([user_id])[0]
+        iid = item_encoder.transform([book_id])[0]
+    except:
+        return "Unknown user_id or book_id"
+
+    svd_pred = svdpp_model.predict(uid, iid).est
+
+    user_liked = np.where(svdpp_model.trainset.ur[uid])[0]
+    if len(user_liked) == 0:
+        content_score = 0
+    else:
+        similarities = cosine_similarity(content_features[iid], content_features[user_liked])
+        content_score = np.mean(similarities)
+
+    hybrid_score = alpha * svd_pred + (1 - alpha) * content_score * 5
+    return round(hybrid_score, 2)
+
+# --- Gradio Interface ---
+def recommend(user_id, book_id, alpha=0.7):
+    return f"Predicted Rating: {hybrid_predict(user_id, book_id, alpha)}"
+
+# Download model from Kaggle and load
+download_kaggle_model()
+svdpp_model, content_features, mappings = load_models()
+user_encoder = mappings["user_encoder"]
+item_encoder = mappings["item_encoder"]
+
+# Start Gradio app
+demo = gr.Interface(
+    fn=recommend,
+    inputs=[
+        gr.Textbox(label="User ID"),
+        gr.Textbox(label="Book ID"),
+        gr.Slider(0, 1, value=0.7, step=0.1, label="Hybrid Weight (alpha)")
+    ],
+    outputs="text",
+    title="📚 Hybrid Book Recommender",
+    description="Enter a user_id and book_id to get a predicted rating using a Hybrid SVD++ and Content-based model."
+)
+
+demo.launch()