Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from datasets import load_dataset
+
+# Load dataset and models
+dataset = load_dataset("AntZet/home_decoration_objects_images")
+style_clf = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+advice_gen = pipeline("text2text-generation", model="google/flan-t5-base")
+
+def get_recommendation(style_input):
+    # 1. Find matching styles
+    style_examples = [d for d in dataset['train'] if d['style'].lower() == style_input.lower()]
+    
+    if not style_examples:
+        return "Style not found. Try: Industrial, Scandinavian, etc."
+    
+    # 2. Get common objects
+    objects = [obj for ex in style_examples for obj in ex['objects']]
+    top_objects = sorted(set(objects), key=lambda x: -objects.count(x))[:3]
+    
+    # 3. Generate advice
+    prompt = f"""As an Airbnb host, how to decorate in {style_input} style?
+    Key objects: {top_objects}
+    Provide:
+    - 3 essential elements
+    - 2 budget tips
+    - Colors to use"""
+    
+    advice = advice_gen(prompt, max_length=300)[0]['generated_text']
+    
+    # 4. Show example images
+    example_images = [ex['image'] for ex in style_examples[:3]]
+    
+    return advice, example_images
+
+# Simple UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🏡 Airbnb Style Advisor")
+    style_input = gr.Textbox(label="Enter style (e.g. Industrial)")
+    submit_btn = gr.Button("Get Advice")
+    
+    with gr.Row():
+        advice_output = gr.Textbox(label="Recommendations")
+        gallery = gr.Gallery(label="Style Examples")
+    
+    submit_btn.click(
+        fn=get_recommendation,
+        inputs=style_input,
+        outputs=[advice_output, gallery]
+    )
+
+demo.launch()