Update app.py

app.py

@@ -1,40 +1,36 @@
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import streamlit as st
-import joblib
-from sklearn.pipeline import Pipeline
-
-# Load Model
-model = joblib.load("rice_model.pkl")
-
-# Streamlit UI
-st.title("Rice Classification App")
-st.write("Upload a CSV file containing rice grain properties to predict the type of rice.")
-
-# File Upload
-uploaded_file = st.file_uploader("Upload CSV", type=["csv"])
-if uploaded_file is not None:
-    data = pd.read_csv(uploaded_file)
-    st.write("### Uploaded Data:")
-    st.dataframe(data)
-    
-    if 'id' in data.columns:
-        data.drop(columns=['id'], inplace=True)
-    
-    if 'Class' in data.columns:
-        data.drop(columns=['Class'], inplace=True)  # Remove target column if present
-    
-    # Make Predictions
-    predictions = model.predict(data)
-    data['Predicted Class'] = predictions
-    
-    st.write("### Predictions:")
-    st.dataframe(data)
-    
-    # Download Predictions
-    st.download_button(label="Download Predictions", data=data.to_csv(index=False), file_name="predictions.csv", mime="text/csv")
+   import streamlit as st
+   import numpy as np
+   import joblib
+   import pandas as pd
+
+   # Load the trained model
+   model = joblib.load("rice_model.pkl")
+
+   # Streamlit UI
+   st.title("Rice Classification Model")
+   st.write("Enter the grain properties to predict the type of rice.")
+
+   # User input fields
+   area = st.number_input("Area", min_value=1000, max_value=10000, value=5000)
+   major_axis_length = st.number_input("Major Axis Length", min_value=50.0, max_value=200.0, value=100.0)
+   minor_axis_length = st.number_input("Minor Axis Length", min_value=30.0, max_value=100.0, value=50.0)
+   eccentricity = st.number_input("Eccentricity", min_value=0.5, max_value=1.0, value=0.75)
+   convex_area = st.number_input("Convex Area", min_value=1000, max_value=10000, value=5000)
+   equiv_diameter = st.number_input("Equivalent Diameter", min_value=30.0, max_value=120.0, value=75.0)
+   extent = st.number_input("Extent", min_value=0.4, max_value=1.0, value=0.7)
+   perimeter = st.number_input("Perimeter", min_value=100.0, max_value=500.0, value=250.0)
+   roundness = st.number_input("Roundness", min_value=0.5, max_value=1.0, value=0.75)
+   aspect_ratio = st.number_input("Aspect Ratio", min_value=1.0, max_value=3.0, value=1.5)
+
+   # Predict button
+   if st.button("Predict Rice Type"):
+       input_features = np.array([[area, major_axis_length, minor_axis_length, eccentricity, convex_area, 
+                                   equiv_diameter, extent, perimeter, roundness, aspect_ratio]])
+       prediction = model.predict(input_features)[0]
+       rice_type = "Type 1" if prediction == 1 else "Type 0"
+       st.success(f"Predicted Rice Type: {rice_type}")
+
+   # Run using: streamlit run app.py