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app.py

@@ -11,9 +11,15 @@ st.subheader("Online Prediction")
 # Collect user input for product details
 product_type = st.selectbox("Product Type",["Frozen Foods","Dairy","Canned","Baking Goods","Snack Foods","Meat","Fruits and Vegetables","Breads","Breakfast","Starchy Foods","Seafood"])
 product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar","Regular","No Sugar"])
-product_mrp = st.number_input("MRP (in INR)", min_value=10.0, step=10.0, value=150.0)
-product_weight = st.number_input("Product Weight (in gms)", min_value=1.0, step=0.2, value=4.0)
-product_allocated_area=st.number_input("Product Allocated area in %", min_value=1.0, step=1.0, value=3.0)
+
+# Min, Max value taken from the statistics details captured in the EDA
+# Mean value is taken as the default value
+# Step is defined based on the range of numbers
+
+product_mrp = st.number_input("MRP (in $)", min_value=31.0, max_value=267.0, step=1.0, value=147.0)
+product_weight = st.number_input("Product Weight (in Ounce)", min_value=4.0, max_value=22, step=0.2, value=12.0)
+product_allocated_area=st.number_input("Product Allocated area in %", min_value=0.4, max_value=30; step=0.1, value=0.7)
+
 store_size = st.selectbox("Store Size", ["Small","Medium","High"])
 store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1","Tier 2","Tier 3"])
 store_type=st.selectbox("Store Type", ["Food Mart","Supermarket Type1","Supermarket Type2","Departmental Store"])
@@ -50,11 +56,8 @@ uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["
 # Make batch prediction when the "Predict Batch" button is clicked
 if uploaded_file is not None:
   if st.button("Predict Batch"):
-        #files = {"file": (uploaded_file.name, uploaded_file.getvalue(), "text/csv")}
         st.write(uploaded_file.name)
         st.write(uploaded_file.getvalue())
-        #files = {"file": (uploaded_file.name, uploaded_file.getvalue(), "csv")}
-        #response = requests.post("https://deepacsr-RentalPricePredictionBackend.hf.space/v1/ProductBatchSales",files=files) # Send file to Flask API
         response = requests.post("https://deepacsr-ProductPricePredictionBackend.hf.space//v1/batchsales",files={"file": uploaded_file})
         if response.status_code == 200:
             predictions = response.json()