Create app.py

app.py

@@ -0,0 +1,65 @@
+import streamlit as st
+import pandas as pd
+import joblib
+
+# Page config
+st.set_page_config(page_title="Blueberry Yield Predictor", layout="wide")
+
+# Load model and scaler
+@st.cache_resource
+def load_model():
+    model = joblib.load('model.joblib')
+    scaler = joblib.load('scaler.joblib')
+    return model, scaler
+
+model, scaler = load_model()
+
+# App title
+st.title('🫐 Blueberry Yield Predictor')
+st.markdown('Enter field parameters to predict blueberry yield')
+
+# Create two columns for inputs
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader('Field Parameters')
+    clonesize = st.slider('Clone Size', 10.0, 40.0, 25.0)
+    honeybee = st.slider('Honeybee', 0.0, 1.0, 0.5)
+    bumbles = st.slider('Bumbles', 0.0, 1.0, 0.25)
+    andrena = st.slider('Andrena', 0.0, 1.0, 0.5)
+
+with col2:
+    st.subheader('Environmental Parameters')
+    osmia = st.slider('Osmia', 0.0, 1.0, 0.5)
+    MaxOfUpperTRange = st.slider('Max Temperature (°F)', 60.0, 100.0, 80.0)
+    MinOfUpperTRange = st.slider('Min Temperature (°F)', 35.0, 60.0, 50.0)
+    RainingDays = st.slider('Raining Days', 1.0, 34.0, 20.0)
+
+# Predict button
+if st.button('Predict Yield'):
+    # Create input data
+    input_data = pd.DataFrame({
+        'clonesize': [clonesize],
+        'honeybee': [honeybee],
+        'bumbles': [bumbles],
+        'andrena': [andrena],
+        'osmia': [osmia],
+        'MaxOfUpperTRange': [MaxOfUpperTRange],
+        'MinOfUpperTRange': [MinOfUpperTRange],
+        'AverageOfUpperTRange': [(MaxOfUpperTRange + MinOfUpperTRange) / 2],
+        'MaxOfLowerTRange': [60.0],
+        'MinOfLowerTRange': [30.0],
+        'AverageOfLowerTRange': [45.0],
+        'RainingDays': [RainingDays],
+        'AverageRainingDays': [RainingDays/100],
+        'fruitset': [0.5],
+        'fruitmass': [0.45],
+        'seeds': [35.0]
+    })
+    
+    # Scale and predict
+    input_scaled = scaler.transform(input_data)
+    prediction = model.predict(input_scaled)[0]
+    
+    # Display prediction
+    st.success(f'Predicted Yield: {prediction:.2f}')