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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()
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