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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from autogluon.multimodal import MultiModalPredictor | |
| from autogluon.tabular import TabularPredictor | |
| # Define icons | |
| seller_icon = "🏡" | |
| buyer_icon = "🔍" | |
| submit_icon = "📝" | |
| predict_icon = "🔮" | |
| # Initialize df as a global variable | |
| df = None | |
| def predict_price(): | |
| global df # Declare df as a global variable | |
| # Set the title and subheader | |
| st.title("Real Estate Price Prediction") | |
| st.subheader("Choose your role and provide property details") | |
| # User role selection | |
| option = st.selectbox("Who are you?", ['Seller', 'Buyer'], index=0) | |
| if option == "Seller": | |
| st.subheader(f"{seller_icon} Seller Information") | |
| with st.spinner("Loading model..."): | |
| predictor = MultiModalPredictor.load("C:/Users/duong/OneDrive/Desktop/mm-nlp-image-transformer") | |
| st.success("Done") | |
| description = st.text_area("Property Description", help="Describe your property") | |
| title = st.text_input("Property Title", help="Enter a title for your property") | |
| else: | |
| st.subheader(f"{buyer_icon} Buyer Information") | |
| with st.spinner("Loading model..."): | |
| predictor = TabularPredictor.load("C:/Users/duong/OneDrive/Desktop/tabular", require_py_version_match=False) | |
| st.success("Done") | |
| # Property details input | |
| area = st.number_input("Property Area (square meters)", min_value=1) | |
| location = st.text_input("Property Location", help="Enter the location of the property") | |
| city_code = st.text_input("City Code", help="Enter the city code") | |
| district = st.text_input("District", help="Enter the district name") | |
| bedroom = st.slider("Number of Bedrooms", min_value=1, max_value=10, value=5, step=1) | |
| bathroom = st.slider("Number of Bathrooms", min_value=1, max_value=10, value=2, step=1) | |
| # Submit button to create the DataFrame | |
| submitted = st.button(f"{submit_icon} Submit") | |
| # Create a DataFrame from user inputs | |
| if submitted: | |
| if area and location and city_code and district and bedroom and bathroom: | |
| if option == "Seller": | |
| if (not description or not title): | |
| st.error("Please fill in both Description and Title fields for Sellers.") | |
| else: | |
| data = { | |
| "Price": np.nan, | |
| "Area": [area], | |
| "Location": [location], | |
| "Time stamp": np.nan, | |
| "Certification status": np.nan, | |
| "Direction": np.nan, | |
| "Bedrooms": [bedroom], | |
| "Bathrooms": [bathroom], | |
| "Front width": np.nan, | |
| "Floor": np.nan, | |
| "Image URL": np.nan, | |
| "Road width": np.nan, | |
| "City_code": [city_code], | |
| "DistrictId": [district], | |
| "Balcony_Direction": np.nan, | |
| "Longitude": np.nan, | |
| "Lattitude": np.nan, | |
| "Description": [description], | |
| "Title": [title] | |
| } | |
| df = pd.DataFrame(data) | |
| st.write(f"{seller_icon} Input Data:") | |
| st.dataframe(df) | |
| elif option == "Buyer": | |
| data = { | |
| "Price": np.nan, | |
| "Area": [area], | |
| "Location": [location], | |
| "Time stamp": np.nan, | |
| "Certification status": np.nan, | |
| "Direction": np.nan, | |
| "Bedrooms": [bedroom], | |
| "Bathrooms": [bathroom], | |
| "Front width": np.nan, | |
| "Floor": np.nan, | |
| "Image URL": np.nan, | |
| "Road width": np.nan, | |
| "City_code": [city_code], | |
| "DistrictId": [district], | |
| "Balcony_Direction": np.nan, | |
| "Longitude": np.nan, | |
| "Lattitude": np.nan | |
| } | |
| df = pd.DataFrame(data) | |
| st.write(f"{buyer_icon} Input Data:") | |
| st.dataframe(df) | |
| else: | |
| st.error("Please fill in all fields to have a better prediction!") | |
| # Prediction button (enabled only when data has been submitted) | |
| if st.button(f"{predict_icon} Predict"): | |
| with st.spinner("Loading..."): | |
| # Perform predictions and calculations here | |
| predictions = predictor.predict(df.drop(columns="Price")) | |
| st.success(f"Predicted Price: {predictions[0]:,.0f} VND") | |
| scores = predictor.evaluate( | |
| df, | |
| metrics=[ | |
| "mean_squared_error", | |
| "r2", | |
| ], | |
| ) | |
| st.subheader("Model Evaluation Metrics:") | |
| for metric, score in scores.items(): | |
| st.write(f"{metric}: {score:.2f}") | |