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app.py

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+import gradio as gr
+import pandas as pd
+import ast
+import torch
+import numpy as np
+from huggingface_hub import hf_hub_download
+from sentence_transformers import SentenceTransformer, util
+
+# 🔹 Load model
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+# 🔹 Download book embeddings from Hugging Face Hub
+repo_id = "AventIQ-AI/all-MiniLM-L6-v2-book-recommendation-system"
+filename = "book_embeddings.csv"
+csv_path = hf_hub_download(repo_id=repo_id, filename=filename)
+
+# 🔹 Load embeddings
+df_embeddings = pd.read_csv(csv_path)
+df_embeddings["embedding"] = df_embeddings["embedding"].apply(ast.literal_eval)
+book_embeddings = torch.tensor(df_embeddings["embedding"].tolist())
+
+# 🔹 Function to get book recommendations
+def get_book_recommendations(query, top_k=5):
+    query_embedding = model.encode(query, convert_to_tensor=True)
+
+    similarities = util.pytorch_cos_sim(query_embedding, book_embeddings).squeeze(0)
+    top_k_values, top_k_indices = torch.topk(similarities, k=top_k)
+
+    recommended_titles = df_embeddings.iloc[top_k_indices.cpu().numpy()]["title"].tolist()
+    recommended_scores = top_k_values.cpu().numpy().tolist()
+
+    return [f"📚 {title} - Score: {score:.4f}" for title, score in zip(recommended_titles, recommended_scores)]
+
+# 🔹 Define Gradio UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("## 📖 AI-Powered Book Recommendation System")
+    gr.Markdown("🔍 **Find your next favorite book!** Enter a description or a genre, and the AI will suggest books.")
+
+    with gr.Row():
+        query_input = gr.Textbox(label="Enter Book Description / Genre", placeholder="E.g. A thrilling mystery novel...")
+        recommend_button = gr.Button("Get Recommendations 🎯")
+
+    output = gr.Textbox(label="Recommended Books", lines=5)
+
+    examples = [
+        ["A horror novel with ghosts and dark nights"],
+        ["A sci-fi adventure with aliens and space travel"],
+        ["A romance story set in Paris"],
+        ["A detective novel solving crimes in the city"],
+        ["An inspiring self-help book for personal growth"]
+    ]
+
+    gr.Examples(examples, inputs=[query_input])
+
+    recommend_button.click(fn=get_book_recommendations, inputs=[query_input], outputs=[output])
+
+# 🔹 Launch the Gradio app
+demo.launch()

requirements.txt

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+torch
+transformers
+gradio
+sentencepiece
+torchvision
+huggingface_hub
+pillow
+numpy