Minor changes

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 import pandas as pd
 import joblib
 
-
 models = ["Linear Regression", "XGBoost", "Random Forests Regressor"]
 
 @st.cache_resource
@@ -27,7 +26,7 @@ def load_model(chosen_model: str) -> tuple:
         chosen_model (str): The name of the model you wish to import for inference.
         
     Returns:
-        The ML regressor model and the scaler
+        tuple: The ML regressor model and the scaler
     """
     model = None
     if chosen_model == models[0]:
@@ -39,7 +38,7 @@ def load_model(chosen_model: str) -> tuple:
     
     return joblib.load(model), joblib.load("./models/scaler.pkl")
 
-def get_input_data(df: pd.core.frame.DataFrame) -> pd.core.frame.DataFrame:
+def get_input_data(df: pd.core.frame.DataFrame):
     """
     Takes the input of user that will be used to predict the crop yield.
     
@@ -47,7 +46,7 @@ def get_input_data(df: pd.core.frame.DataFrame) -> pd.core.frame.DataFrame:
         df (pd.core.frame.DataFrame): The dataframe to be used as a basis as to what the user can choose.
     
     Returns:
-        pd.core.frame.DataFrame: A dataframe that contains a single instance based on the user input. 
+        A dataframe that contains a single instance based on the user input, and a tuple of the item and area.
     """
     
     item_mapping = {category: code for code, category in enumerate(df['Item'].unique())}
@@ -58,7 +57,7 @@ def get_input_data(df: pd.core.frame.DataFrame) -> pd.core.frame.DataFrame:
     year = st.number_input("When do you wish to plant?", min_value=2025, max_value=2030, value=2025, placeholder="Choose a year")
     avg_rainfall = st.slider("What is the estimated average rainfall for that year?", min_value=df['average_rain_fall_mm_per_year'].min(), max_value=df['average_rain_fall_mm_per_year'].max())
     pesticides = st.slider("How much pesticides do you intend to use? (in tonnes)", max_value=int(df['pesticides_tonnes'].max()))
-    avg_temp = st.slider("How hot or cold is the area around you?", min_value=df['avg_temp'].min(), max_value=df['avg_temp'].max())
+    avg_temp = st.slider("How hot or cold is the area around you? (Average Temperature)", min_value=df['avg_temp'].min(), max_value=df['avg_temp'].max())
     
     item_num = item_mapping[item]
     area_num = area_mapping[area]
@@ -70,7 +69,7 @@ def get_input_data(df: pd.core.frame.DataFrame) -> pd.core.frame.DataFrame:
     "average_rain_fall_mm_per_year": [avg_rainfall],
     "pesticides_tonnes": [pesticides],
     "avg_temp": [avg_temp]
-    })
+    }), (item, area)
 
 def main():
     st.title("Crop Yield Predictor")
@@ -81,8 +80,9 @@ def main():
     
     st.dataframe(df, height=300, width=900)
     
-    # Get input data
-    input_data = get_input_data(df)
+    st.divider()
+    
+    input_data, (item, area) = get_input_data(df)
     
     selected_model = st.selectbox("Which model do you want to use?",
                                   tuple(models),
@@ -98,14 +98,23 @@ def main():
             st.image("./plots/rf_plot.png", caption="Random Forests Regressor Plot")
             
     if st.button("Predict yield!"):
-        st.write("inputted data:")
-        st.dataframe(input_data, height=50, width=900)
+        
+        col1, col2 = st.columns(2)
+        col1.metric("Area", area, border=True)
+        col2.metric("Item", item, border=True)
+        
+        col3, col4, col5 = st.columns(3)
+        col3.metric("Average Rainfall", input_data['average_rain_fall_mm_per_year'], border=True)
+        col4.metric("Pestiscide Usage (Tonne)", input_data['pesticides_tonnes'], border=True)
+        col5.metric("Average Temperature (Celcius)", input_data['avg_temp'], border=True)
+        
         model, scaler = load_model(selected_model)
         
         input_scaled = scaler.transform(input_data)
         
         pred = model.predict(input_scaled)
         
-        st.write(pred)
+        st.header(f"Predicted Crop Yield: **{int(pred[0])}**")
+        
 if __name__ == "__main__":
     main()