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app.py

@@ -60,37 +60,37 @@ st.title("Product Revenue prediction")
 st.subheader("Online Prediction")
 
 #Collect user input for  features 
-Product_Weight = st.number_input("Product Weight", min_value=4.0, max_value=25.0, step=0.5)
-Product_Sugar_Content = st.selectbox("Product Sugar Content", ["No Sugar", "Low Sugar", "Regular"])
-Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.001, max_value=0.3)
-Product_Type = st.selectbox("Product Type", product_types)
-Product_MRP = st.number_input("Product MRP", min_value=30.0, max_value=300.0)
-Store_Id = st.selectbox("Store Id", store_ids)
-Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1988, max_value=2010, step=1)
-Store_Size = st.selectbox("Store Size", store_sizes)
-Store_Location_City_Type = st.selectbox("Store Location City Type", store_Location_City_Types)
-Store_Type = st.selectbox("Store Type", store_types)
+# Product_Weight = st.number_input("Product Weight", min_value=4.0, max_value=25.0, step=0.5)
+# Product_Sugar_Content = st.selectbox("Product Sugar Content", ["No Sugar", "Low Sugar", "Regular"])
+# Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.001, max_value=0.3)
+# Product_Type = st.selectbox("Product Type", product_types)
+# Product_MRP = st.number_input("Product MRP", min_value=30.0, max_value=300.0)
+# Store_Id = st.selectbox("Store Id", store_ids)
+# Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1988, max_value=2010, step=1)
+# Store_Size = st.selectbox("Store Size", store_sizes)
+# Store_Location_City_Type = st.selectbox("Store Location City Type", store_Location_City_Types)
+# Store_Type = st.selectbox("Store Type", store_types)
 
-payload = {
-        '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_Id': Store_Id,
-        'Store_Establishment_Year': Store_Establishment_Year,
-        'Store_Size': Store_Size,
-        'Store_Location_City_Type': Store_Location_City_Type,
-        'Store_Type': Store_Type
-    }
+# payload = {
+#         '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_Id': Store_Id,
+#         'Store_Establishment_Year': Store_Establishment_Year,
+#         'Store_Size': Store_Size,
+#         'Store_Location_City_Type': Store_Location_City_Type,
+#         'Store_Type': Store_Type
+#     }
 
 
-if st.button("Predict"):
-  response = requests.post(API_ENDPOINT, json=payload)
-  if response.status_code == 200:
-    json_data= response.json()
-    st.write('Predicted Sales revenue ', json_data.get('Prediction'))
-  else:
-    st.write(f"Error making prediction: {response.status_code}")
+# if st.button("Predict"):
+#   response = requests.post(API_ENDPOINT, json=payload)
+#   if response.status_code == 200:
+#     json_data= response.json()
+#     st.write('Predicted Sales revenue ', json_data.get('Prediction'))
+#   else:
+#     st.write(f"Error making prediction: {response.status_code}")
   
 

requirements.txt

@@ -2,5 +2,4 @@ pandas==2.2.2
 numpy==2.0.2
 scikit-learn==1.6.1
 requests==2.28.1
-joblib==1.4.2
 streamlit==1.43.2