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app.py

@@ -3,62 +3,31 @@ import pandas as pd
 import requests
 
 # Set the title of the Streamlit app
-st.title("Sales Revenue Prediction")
+st.title("Airbnb Rental Price Prediction")
 
 # Section for online prediction
 st.subheader("Online Prediction")
 
 # Collect user input for property features
-Product_Id = st.text_input("Product Id")
-Product_Weight = st.number_input("Product Weight", min_value=0.0)
-Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])
-Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0)
-Product_Type = st.selectbox("Product Type", ["Fruits and Vegetables", "Snack Foods","Frozen Foods","Dairy",
-                                             "Household","Baking Goods","Canned","Health and Hygiene",
-                                             "Meat","Soft Drinks","Breads","Hard Drinks","Others",
-                                             "Starchy Foods","Breakfast","Seafood"])
-Product_MRP = st.number_input("Product MRP", min_value=0.0)
-Store_Id = st.text_input("Store Id")
-Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=0)
-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", ["Supermarket Type2", "Supermarket Type1", "Departmental Store","Food Mart"])
-
+room_type = st.selectbox("Room Type", ["Entire home/apt", "Private room", "Shared room"])
+accommodates = st.number_input("Accommodates (Number of guests)", min_value=1, value=2)
+bathrooms = st.number_input("Bathrooms", min_value=1, step=1, value=2)
+cancellation_policy = st.selectbox("Cancellation Policy (kind of cancellation policy)", ["strict", "flexible", "moderate"])
+cleaning_fee = st.selectbox("Cleaning Fee Charged?", ["True", "False"])
+instant_bookable = st.selectbox("Instantly Bookable?", ["False", "True"])
+review_scores_rating = st.number_input("Review Score Rating", min_value=0.0, max_value=100.0, step=1.0, value=90.0)
+bedrooms = st.number_input("Bedrooms", min_value=0, step=1, value=1)
+beds = st.number_input("Beds", min_value=0, step=1, value=1)
 
 # Convert user input into a DataFrame
-input_data = pd.DataFrame([{'Product_Id': Product_Id,
-        '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_Location_City_Type': Store_Location_City_Type,
-        'Store_Type': Store_Type}])
-
-# Make prediction when the "Predict" button is clicked
-if st.button("Predict"):
-    response = requests.post("https://pragmat-SalesRevenuePredictionBackend.hf.space/v1/revenue", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
-    if response.status_code == 200:
-        prediction = response.json()['Predicted Sales Revenue (in dollars)']
-        st.success(f"Predicted Sales Revenue (in dollars): {prediction}")
-    else:
-        st.error("Error making prediction.")
-
-# # Section for batch prediction
-# st.subheader("Batch Prediction")
-
-# # Allow users to upload a CSV file for batch prediction
-# uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
-
-# # Make batch prediction when the "Predict Batch" button is clicked
-# if uploaded_file is not None:
-#     if st.button("Predict Batch"):
-#         response = requests.post("https://pragmat-SalesRevenuePredictionBackend.hf.space/v1/revenuebatch", files={"file": uploaded_file})  # Send file to Flask API
-#         if response.status_code == 200:
-#             predictions = response.json()
-#             st.success("Batch predictions completed!")
-#             st.write(predictions)  # Display the predictions
-#         else:
-#             st.error("Error making batch prediction.")
+input_data = pd.DataFrame([{
+    'room_type': room_type,
+    'accommodates': accommodates,
+    'bathrooms': bathrooms,
+    'cancellation_policy': cancellation_policy,
+    'cleaning_fee': cleaning_fee,
+    'instant_bookable': 'f' if instant_bookable=="False" else "t",  # Convert to 't' or 'f'
+    'review_scores_rating': review_scores_rating,
+    'bedrooms': bedrooms,
+    'beds': beds
+}])