import streamlit as st
import pandas as pd
import joblib
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load the trained model
@st.cache_resource
def load_model():
    return joblib.load("Agriculture_Yield_Prediction_model_v1_0.joblib")

model = load_model()

# Streamlit UI for Price Prediction
st.title("🌾 Crop Yield Prediction")
st.write("Please Provide your crop details below to estimate yield (t/ha).")

st.subheader("Enter the listing details:")

# Collect user input
# Dropdowns
# --- Farmer-friendly Inputs ---
state = st.selectbox("🌍 State", [
    "West Bengal","Nagaland","Chhattisgarh","Himachal Pradesh","Bihar",
    "Karnataka","Jammu and Kashmir","Andhra Pradesh","Tripura","Haryana",
    "Uttarakhand","Assam","Manipur","Odisha","Punjab","Madhya Pradesh",
    "Tamil Nadu","Mizoram","Uttar Pradesh","Telangana","Puducherry","Sikkim",
    "Maharashtra","Delhi","Gujarat","Arunachal Pradesh","Kerala","Jharkhand",
    "Meghalaya","Goa"
])
crop = st.selectbox("🌱 Crop", [
    "Sesamum","Urad","Moong(Green Gram)","Groundnut","Other Kharif pulses",
    "Small millets","Sunflower","Peas & beans (Pulses)","Arhar/Tur","Bajra",
    "Rapeseed &Mustard","Maize","Horse-gram","Other Rabi pulses","Jowar","Ragi",
    "Gram","Masoor","Dry chillies","Castor seed","Soyabean","Linseed",
    "Cotton(lint)","Turmeric","Wheat","Barley","Coriander","Sannhamp","Rice",
    "Garlic","Ginger","Tobacco","Other Cereals","Safflower","Cowpea(Lobia)",
    "Niger seed","other oilseeds","Mesta","Onion","Moth","Black pepper",
    "Arecanut","Jute","Cardamom","Sugarcane","Cashewnut","Sweet potato",
    "Guar seed","Potato","Other Summer Pulses","Khesari","Banana"
])
season = st.selectbox("🗓️ Season", [
    "Kharif","Rabi","Whole Year","Summer","Autumn","Winter"
])
crop_year = st.number_input("📅 Crop Year", min_value=1990, max_value=2025, value=2024)

area_acres = st.number_input("🌾 Area (in acres)", min_value=0.1, step=0.1)
production_bags = st.number_input("📦 Production (in bags previous year data)", min_value=1, step=1)
bag_weight = st.number_input("⚖️ Bag Weight (kg)", min_value=50, max_value=100, value=75)

rainfall = st.number_input("🌧️ Annual Rainfall (mm)", min_value=0.0, step=1.0)
fertilizer = st.number_input("💊 Fertilizer Used (kg)", min_value=0.0, step=1.0)
pesticide = st.number_input("🧴 Pesticide Used (kg)", min_value=0.0, step=1.0)

area_ha = area_acres / 2.47   # convert acres → hectares
production_tonnes = (production_bags * bag_weight) / 1000  # convert bags → tonnes



input_data = pd.DataFrame({
    "Crop": [crop],
    "Crop_Year": [crop_year],
    "Season": [season],
    "State": [state],
    "Area": [area_ha],
    "Production": [production_tonnes],
    "Annual_Rainfall": [rainfall],
    "Fertilizer": [fertilizer],
    "Pesticide": [pesticide],
})


# --- Prediction ---
if st.button("🚀 Predict Yield"):
    prediction = model.predict(input_data)[0]   # model outputs yield in t/ha
    st.success(f"🌾 Estimated Yield: {prediction:.2f} t/ha")

    prediction_bags_per_acre = (prediction * 2.47 * 1000) / bag_weight
    st.info(f"Equivalent: {prediction_bags_per_acre:.2f} bags per acre")