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Update app.py
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app.py
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@@ -55,39 +55,16 @@ def gradio_interface(land_size, biodiversity, budget, infrastructure):
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prediction = predict_service(land_size, biodiversity, budget, infrastructure)
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return f"The predicted service is: {prediction}"
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def show_price_guide():
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return """
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| Type | Target Audience | Price Range |
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|-----------|------------------------------------------------------|---------------------|
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| Economy | Budget travelers, local tourists, students. | 5,000 to 50,000 Rs. |
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| Mid-Range | Middle-class tourists, small groups, families. | 50,000 to 2,00,000 Rs. |
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| Luxury | High-end tourists, international visitors, corporate groups. | 2,00,000 to 10,00,000 Rs. |
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"""
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iface = gr.Interface(
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fn=gradio_interface,
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inputs=[
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gr.Number(label="Land Size (acres)"),
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gr.Dropdown(label="Biodiversity", choices=df['Biodiversity'].unique().tolist()),
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gr.Number(label="Budget (INR)"),
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gr.Dropdown(label="Existing Infrastructure", choices=df['Existing Infrastructure'].unique().tolist()),
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gr.Button("Show Budget Guide")
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],
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outputs=[
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gr.Text(label="Predicted Service"),
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gr.Markdown(label="Budget Guide", visible=False) # Initially hidden
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],
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title="Service Prediction Model",
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description="Select land size, biodiversity, budget, and existing infrastructure to predict the service.",
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theme="default" # Set the theme color here
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)
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# Update the interface to show the price guide when the button is clicked
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iface.update(
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components=[None, None, None, None, None, show_price_guide()], # Matching the inputs plus the function for guide
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outputs=[None, "visible"] # Only update the visibility of the Markdown component
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)
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iface.launch()
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prediction = predict_service(land_size, biodiversity, budget, infrastructure)
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return f"The predicted service is: {prediction}"
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iface = gr.Interface(fn=gradio_interface,
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inputs=[
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gr.Number(label="Land Size (acres)"),
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gr.Dropdown(label="Biodiversity", choices=df['Biodiversity'].unique().tolist()),
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gr.Number(label="Budget (INR)"),
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gr.Dropdown(label="Existing Infrastructure", choices=df['Existing Infrastructure'].unique().tolist())
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],
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outputs=gr.Text(label="Predicted Service"),
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title="Service Prediction Model",
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description="Select land size, biodiversity, budget, and existing infrastructure to predict the service.")
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iface.launch()
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