app.py

import pandas as pd
import streamlit as st
import pickle

# Load Model
try:
    with open("classification pickle (1).pkl", "rb") as f:
        model = pickle.load(f)
    st.success("✅ Model loaded successfully!")
except FileNotFoundError:
    st.error("❌ Model file not found! Please upload `classification pickle.pkl`.")
    model = None

# Title
st.markdown("<h1 style='text-align: center; color: green;'>Rice Class Predictor</h1>", unsafe_allow_html=True)

# User Inputs
with st.expander("🔹 **Enter Property Details**", expanded=True):
    Area = st.number_input("Area", min_value=2522.0, max_value=10210.0, value=6660.0)
    MajorAxisLength = st.number_input("Major Axis Length", min_value=74.13, max_value=183.21, value=153.88)
    MinorAxisLength = st.number_input("Minor Axis Length", min_value=34.40, max_value=82.55, value=55.72)
    Eccentricity = st.number_input("Eccentricity", min_value=0.67, max_value=0.97, value=0.92)
    ConvexArea = st.number_input("Convex Area", min_value=2579.0, max_value=11008.0, value=6843.0)
    EquivDiameter = st.number_input("Equivalent Diameter", min_value=56.66, max_value=114.01, value=92.08)
    Extent = st.number_input("Extent", min_value=0.38, max_value=0.89, value=0.60)
    Perimeter = st.number_input("Perimeter", min_value=197.01, max_value=508.51, value=353.08)
    Roundness = st.number_input("Roundness", min_value=0.17, max_value=0.90, value=0.70)
    AspectRation = st.number_input("Aspect Ration", min_value=1.35, max_value=3.91, value=2.60)
st.write("**Jasmine-1, Gonen-1**")  
# Prediction Button
if st.button("🔍 Predict Class"):
    if model is not None:
        input_data = pd.DataFrame([[Area, MajorAxisLength, MinorAxisLength, Eccentricity,
                                    ConvexArea, EquivDiameter, Extent, Perimeter,
                                    Roundness, AspectRation]],
                                  columns=["Area", "MajorAxisLength", "MinorAxisLength",
                                           "Eccentricity", "ConvexArea", "EquivDiameter",
                                           "Extent", "Perimeter", "Roundness", "AspectRation"])

        try:
            predicted_class = model.predict(input_data)[0]
            st.markdown(f"<div style='text-align: center; font-size: 20px; font-weight: bold; color: blue;'>Predicted Class: {predicted_class:.2f}</div>", unsafe_allow_html=True)
        except ValueError as e:
            st.error(f"❌ Error during prediction: {e}")