Update screens/predict.py

screens/predict.py

@@ -1,85 +1,130 @@
 import streamlit as st
 import json
 from autogluon.multimodal import MultiModalPredictor
-from autogluon.tabular import TabularPredictor
 import pandas as pd
+from geopy.geocoders import GoogleV3
 import os
 import tempfile
 
 def predict_page():
-    # Add a title with an icon
-    st.title("💵 Property Price Estimator")
-
-    # User role selection (buyer or seller)
-    user_role = st.selectbox("Select Your Role", options=("Buyer", "Seller"), index=0)
-
-    # Load geocoder and model based on user role
-    @st.cache(allow_output_mutation=True)
-
-
-    if user_role == "Seller":
-        model_path = "models/mm-nlp-image-transformer/"
-    else:
-        model_path = "models/tabular/"
-
-    @st.cache(allow_output_mutation=True)
-    def load_mm_text_model():
-        if user_role == "Seller":
-            return MultiModalPredictor.load(model_path, verbosity=0)
-        elif user_role == "Buyer":
-            return TabularPredictor.load(model_path, verbosity=0)
-
-    mm_text_predictor = load_mm_text_model()
-
-    # Common user input fields
-    st.header("Location Details")
-    city_map = json.load(open("city-map.json"))
-    city = st.selectbox("Choose city 🏙️", options=list(city_map.values()))
-    city_district_map = json.load(open("city-district-map.json"))
-    district = st.selectbox("Choose district 🏘️", options=list(city_district_map[city].values()))
-    location = st.text_input("Enter precise location 📍")
-
-    st.header("Property Specifications")
-    area = st.number_input("Area (m2) 📏", min_value=1.0)
-    bedrooms = st.number_input("Number of bedrooms 🛏️", min_value=1, value=1)
-    bathrooms = st.number_input("Number of bathrooms 🚽", min_value=1, value=1)
-    floors = st.number_input("Number of floors 🏢", min_value=1, value=1)
-    front_width = st.number_input("Front width (m) 📏", min_value=0.0, value=0.0, step=0.1)
-    road_width = st.number_input("Road width (m) 🚗", min_value=0.0, value=0.0, step=0.1)
-
-    st.header("Additional Details")
-    timestamp = st.date_input("Date posted 📅")
-    cert_status = st.selectbox("Certification status 📜", options=["Không có", "hợp đồng", "sổ đỏ / sổ hồng"])
-    direction = st.selectbox("Direction 🧭", options=["Không có", "Tây - Nam", "Đông - Nam", "Đông - Bắc", "Tây - Bắc", "Nam", "Tây", "Bắc", "Đông"])
-    balcony_direction = st.selectbox("Balcony direction 🌞", options=["Không có", "Tây - Nam", "Đông - Nam", "Đông - Bắc", "Tây - Bắc", "Nam", "Tây", "Bắc", "Đông"])
-
-    # Description and image upload (conditional for sellers)
-    if user_role == "Seller":
-        description = st.text_area("Description ✍️")
-        title = description.split(".", maxsplit=1)[0]
-
-        uploaded_image = st.file_uploader("Upload an image 📷")
-        image_tmp = None
-        if uploaded_image:
-            image_tmp = tempfile.NamedTemporaryFile(suffix=uploaded_image.name)
-            image_tmp.write(uploaded_image.read())
-            st.image(uploaded_image, caption='Uploaded Image', use_column_width=True)
-    else:
-        description = ""
-        title = ""
-
-    # Calculate latitude and longitude
-
+    if "price_text" not in st.session_state:
+        st.session_state.price_text = 0
+    
+    @st.cache_resource
+    def load_mm_text_no_price_model():
+        return MultiModalPredictor.load("models/mm-text-no-price/", verbosity=0)
+    
+    
+    mm_text_no_price_predictor = load_mm_text_no_price_model()
+    
+    
+    @st.cache_resource
+    def load_city_map():
+        return json.load(open("city-map.json"))
+    
+    
+    city_map = load_city_map()
+    
+    
+    @st.cache_resource
+    def load_city_district_map():
+        return json.load(open("city-district-map.json"))
+    
+    
+    city_district_map = load_city_district_map()
+    
+    CERT_STATUS = pd.CategoricalDtype(
+        categories=["Không có", "hợp đồng", "sổ đỏ / sổ hồng"], ordered=False
+    )
+    DIRECTION = pd.CategoricalDtype(
+        categories=[
+            "Không có",
+            "Tây - Nam",
+            "Đông - Nam",
+            "Đông - Bắc",
+            "Tây - Bắc",
+            "Nam",
+            "Tây",
+            "Bắc",
+            "Đông",
+        ],
+        ordered=False,
+    )
+    CITY = pd.CategoricalDtype(categories=city_map.keys(), ordered=False)
+    DISTRICT = pd.CategoricalDtype(
+        categories=sum([list(map(int, v.keys())) for v in city_district_map.values()], []),
+        ordered=False,
+    )
+    
+    location_options = st.columns([1, 1, 2, 1, 1])
+    with location_options[0]:
+        city = st.selectbox(
+            "Choose city", options=city_map.items(), format_func=lambda x: x[1]
+        )
+    with location_options[1]:
+        district = st.selectbox(
+            "Choose district",
+            options=city_district_map[city[0]].items(),
+            format_func=lambda x: x[1],
+        )
+    with location_options[2]:
+        location = st.text_input("Enter precise location")
+    
+    location = (location + ", " if location else "") + city[1] + ", " + district[1]
+    geocode_result = geocoder.geocode(query=location, region="vn", language="vi")
     latitude = float("nan")
     longitude = float("nan")
-
-    # Display location map
-    st.map([(latitude, longitude)], zoom=15)
-
-    # Create a button with an icon to get estimated price
-    if st.button("Get Estimated Price with Text 💰"):
-        if user_role == "Seller":
-            input_data = {
+    
+    with location_options[3]:
+        latitude = st.number_input(
+            "Enter latitude", value=latitude, step=1e-8, format="%.7f"
+        )
+    with location_options[4]:
+        longitude = st.number_input(
+            "Enter longitude", value=longitude, step=1e-8, format="%.7f"
+        )
+    
+    numerical_options = st.columns(6)
+    with numerical_options[0]:
+        area = st.number_input("Area (m2)", min_value=1.0)
+    with numerical_options[1]:
+        bedrooms = st.number_input("Number of bedrooms", min_value=1, value=1)
+    with numerical_options[2]:
+        bathrooms = st.number_input("Number of bathrooms", min_value=1, value=1)
+    with numerical_options[3]:
+        floors = st.number_input("Number of floors", min_value=1, value=1)
+    with numerical_options[4]:
+        front_width = st.number_input(
+            "Front width, leave 0 for N/A", min_value=0.0, value=0.0, step=0.1
+        )
+    with numerical_options[5]:
+        road_width = st.number_input(
+            "Road width, leave 0 for N/A", min_value=0.0, value=0.0, step=0.1
+        )
+    
+    cat_time_columns = st.columns(4)
+    with cat_time_columns[0]:
+        timestamp = st.date_input("Date posted", format="DD/MM/YYYY")
+    with cat_time_columns[1]:
+        cert_status = st.selectbox("Certification status", options=CERT_STATUS.categories)
+    with cat_time_columns[2]:
+        direction = st.selectbox("Direction", options=DIRECTION.categories)
+    with cat_time_columns[3]:
+        balcony_direction = st.selectbox("Balcony direction", options=DIRECTION.categories)
+    
+    description = st.text_area("Description")
+    title = description.split(".", maxsplit=1)[0]
+    
+    uploaded_image = st.file_uploader("Upload an image")
+    image_tmp = None
+    if uploaded_image:
+        image_tmp = tempfile.NamedTemporaryFile(suffix=uploaded_image.name)
+        image_tmp.write(uploaded_image.read())
+        print(image_tmp.name)
+    
+    df = pd.DataFrame(
+        [
+            {
                 "Title": title,
                 "Area": area,
                 "Location": location,
@@ -93,36 +138,42 @@ def predict_page():
                 "Description": description,
                 "Image URL": image_tmp.name if image_tmp else None,
                 "Road width": road_width or float("nan"),
-                "City_code": city,
-                "DistrictId": district,
+                "City_code": city[0],
+                "DistrictId": int(district[0]),
                 "Lattitude": latitude,
                 "Longitude": longitude,
                 "Balcony_Direction": balcony_direction,
             }
-        elif user_role == "Buyer":
-            input_data = {
-                "Area": area,
-                "Location": location,
-                "Time stamp": timestamp,
-                "Certification status": cert_status,
-                "Direction": direction,
-                "Bedrooms": bedrooms,
-                "Bathrooms": bathrooms,
-                "Front width": front_width or float("nan"),
-                "Floor": floors,
-                "Image URL": image_tmp.name if image_tmp else None,
-                "Road width": road_width or float("nan"),
-                "City_code": city,
-                "DistrictId": district,
-                "Lattitude": latitude,
-                "Longitude": longitude,
-                "Balcony_Direction": balcony_direction,
-            }
-
-        input_df = pd.DataFrame([input_data])
-        predicted_price = mm_text_predictor.predict(input_df, as_pandas=False).item()
-
-        # Display the estimated price with an icon
-        st.subheader("Estimated Price 💲")
-        st.write(f"{predicted_price * 1e6:,.0f} VND")
-
+        ]
+    ).astype(
+        {
+            "Title": "str",
+            "Area": "float",
+            "Location": "str",
+            "Time stamp": "datetime64[ns]",
+            "Certification status": CERT_STATUS,
+            "Direction": DIRECTION,
+            "Bedrooms": "int",
+            "Bathrooms": "int",
+            "Front width": "float",
+            "Floor": "int",
+            "Description": "str",
+            "Image URL": "str",
+            "Road width": "float",
+            "City_code": CITY,
+            "DistrictId": DISTRICT,
+            "Lattitude": "float",
+            "Longitude": "float",
+            "Balcony_Direction": DIRECTION,
+        }
+    )
+    
+    if st.button("Get estimated price with text"):
+        st.session_state.price_text = mm_text_no_price_predictor.predict(
+            df, as_pandas=False
+        ).item()
+    st.text(
+        "Estimated price: {0:,} VND".format(int(st.session_state.price_text * 1e6))
+        if st.session_state.price_text
+        else "No price estimated."
+    )