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app.py

@@ -53,7 +53,7 @@ def predict_sales():
 
     # Convert to Python float and round
     predicted_sales = round(float(predicted_sales), 2)
-    
+
     # Return the actual price
     return jsonify({'Predicted Sales Forecast (in dollars)': predicted_sales})
 
@@ -74,17 +74,11 @@ def predict_sales_batch():
 
     # Make predictions for all properties in the DataFrame (get log_prices)
     predicted_sales = model.predict(input_data).tolist()
+    # Use row indices as keys
+    output_dict = {f"Row_{i}": round(float(pred), 2) for i, pred in enumerate(predicted_sales)}
 
-    # # Calculate actual prices
-    # predicted_prices = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
-
-    # Create a dictionary of predictions with property IDs as keys
-    property_ids = input_data['Product_Id'].tolist()  # Assuming 'id' is the property ID column
-    output_dict = dict(zip(property_ids, predicted_sales))  # Use actual prices
-
-    # Return the predictions dictionary as a JSON response
-    return output_dict
+    return jsonify(output_dict)
 
 # Run the Flask application in debug mode if this script is executed directly
 if __name__ == '__main__':
-    rental_price_predictor_api.run(debug=True)
+    superkart_sales_forecast_api.run(debug=True)