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+FROM python:3.11.13
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import requests
+
+def predict_sales(product_weight, product_sugar_content, product_allocated_area, product_type, product_mrp, 
+                  store_establishment_year, store_size, store_location_city_type, store_type):
+    # Create input dictionary
+    sample = {
+        'Product_Weight': product_weight,
+        'Product_Sugar_Content': product_sugar_content,
+        'Product_Allocated_Area': product_allocated_area,
+        'Product_Type': product_type,
+        'Product_MRP': product_mrp,
+        'Store_Establishment_Year': store_establishment_year,
+        'Store_Size': store_size,
+        'Store_Location_City_Type': store_location_city_type,
+        'Store_Type': store_type
+    }
+
+    # Convert to DataFrame
+    features_df = pd.DataFrame([sample])
+
+    # Apply one-hot encoding for nominal columns (matching backend)
+    features_df = pd.get_dummies(features_df, columns=['Product_Type', 'Store_Type'], drop_first=True)
+
+    # Apply ordinal encoding (based on backend mappings)
+    sugar_mapping = {'No Sugar': 0, 'Low Sugar': 1, 'Regular': 2}
+    size_mapping = {'Small': 0, 'Medium': 1, 'High': 2}
+    city_mapping = {'Tier 3': 0, 'Tier 2': 1, 'Tier 1': 2}
+
+    features_df['Product_Sugar_Content'] = features_df['Product_Sugar_Content'].map(sugar_mapping)
+    features_df['Store_Size'] = features_df['Store_Size'].map(size_mapping)
+    features_df['Store_Location_City_Type'] = features_df['Store_Location_City_Type'].map(city_mapping)
+
+    # Call the backend API
+    backend_url = "https://Hugo014-TotalSalesPredictionBackend.hf.space/v1/sales"
+    try:
+        response = requests.post(backend_url, json=sample)
+        if response.status_code == 200:
+            result = response.json()
+            predicted_sales = result['Predicted Sales Total (in dollars)']
+            return f"The predicted sales total for the product is ${predicted_sales:.2f}."
+        else:
+            return f"Backend error: {response.status_code} - {response.text}"
+    except Exception as e:
+        return f"Error calling backend: {str(e)}"
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict_sales,
+    inputs=[
+        gr.Number(label="Product Weight", value=10.0, minimum=0.0, step=0.1),
+        gr.Dropdown(label="Product Sugar Content", choices=["No Sugar", "Low Sugar", "Regular"], value="Low Sugar"),
+        gr.Number(label="Product Allocated Area (sq ft)", value=500.0, minimum=0.0, step=1.0),
+        gr.Dropdown(label="Product Type", choices=[
+            "Dairy", "Soft Drinks", "Meat", "Fruits and Vegetables", "Snack Foods", "Household",
+            "Frozen Foods", "Baking Goods", "Canned", "Health and Hygiene", "Hard Drinks",
+            "Breads", "Starchy Foods", "Breakfast", "Seafood", "Others"
+        ], value="Dairy"),
+        gr.Number(label="Product MRP (price)", value=100.0, minimum=0.0, step=1.0),
+        gr.Number(label="Store Establishment Year", value=2000, minimum=1900, maximum=2025, step=1),
+        gr.Dropdown(label="Store Size", choices=["Small", "Medium", "High"], value="Medium"),
+        gr.Dropdown(label="Store Location City Type", choices=["Tier 3", "Tier 2", "Tier 1"], value="Tier 1"),
+        gr.Dropdown(label="Store Type", choices=[
+            "Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"
+        ], value="Supermarket Type1")
+    ],
+    outputs=gr.Textbox(label="Prediction Result"),
+    title="Super Kart Product Sales Prediction App",
+    description="This tool predicts the total sales for a product based on store and product details."
+)
+
+demo.launch()

requirements.txt

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+pandas==2.2.2
+requests==2.28.1
+streamlit==1.48.1
+gradio==5.42.0