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app.py
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@@ -44,43 +44,43 @@ def predict_sales_batch(csv_file):
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except Exception as e:
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return f"Error calling backend: {str(e)}"
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# Create Gradio UI with tabs for single and batch
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with gr.Blocks() as demo:
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demo.launch(share=True) # Added share=True to avoid localhost error in restricted environments like Colab
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except Exception as e:
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return f"Error calling backend: {str(e)}"
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# # Create Gradio UI with tabs for single and batch
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# with gr.Blocks() as demo:
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# gr.Markdown("# Super Kart Product Sales Prediction App")
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# with gr.Tab("Single Prediction"):
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# product_weight = gr.Number(label="Product Weight", value=10.0, minimum=0.0, step=0.1)
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# product_sugar_content = gr.Dropdown(label="Product Sugar Content", choices=["No Sugar", "Low Sugar", "Regular"], value="Low Sugar")
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# product_allocated_area = gr.Number(label="Product Allocated Area (sq ft)", value=500.0, minimum=0.0, step=1.0)
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# product_type = gr.Dropdown(label="Product Type", choices=[
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# "Dairy", "Soft Drinks", "Meat", "Fruits and Vegetables", "Snack Foods", "Household",
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# "Frozen Foods", "Baking Goods", "Canned", "Health and Hygiene", "Hard Drinks",
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# "Breads", "Starchy Foods", "Breakfast", "Seafood", "Others"
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# ], value="Dairy")
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# product_mrp = gr.Number(label="Product MRP (price)", value=100.0, minimum=0.0, step=1.0)
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# store_establishment_year = gr.Number(label="Store Establishment Year", value=2000, minimum=1900, maximum=2025, step=1)
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# store_size = gr.Dropdown(label="Store Size", choices=["Small", "Medium", "High"], value="Medium")
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# store_location_city_type = gr.Dropdown(label="Store Location City Type", choices=["Tier 3", "Tier 2", "Tier 1"], value="Tier 1")
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# store_type = gr.Dropdown(label="Store Type", choices=[
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# "Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"
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# ], value="Supermarket Type1")
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# output_single = gr.Textbox(label="Prediction Result")
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# predict_button = gr.Button("Predict")
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# predict_button.click(
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# fn=predict_sales_single,
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# inputs=[product_weight, product_sugar_content, product_allocated_area, product_type, product_mrp,
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# store_establishment_year, store_size, store_location_city_type, store_type],
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# outputs=output_single
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# )
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# with gr.Tab("Batch Prediction"):
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# csv_file = gr.File(label="Upload CSV for Batch Prediction", file_types=[".csv"])
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# output_batch = gr.JSON(label="Batch Prediction Results") # Better for displaying dict output
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# batch_button = gr.Button("Predict Batch")
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# batch_button.click(
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# fn=predict_sales_batch,
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# inputs=csv_file,
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# outputs=output_batch
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# )
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demo.launch(share=True) # Added share=True to avoid localhost error in restricted environments like Colab
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