app.py

import gradio as gr
import pandas as pd
import lightgbm as lgb
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import os
import torch
from torchvision import models, transforms
from PIL import Image


# ---------------------------
# Crop Recommendation Setup
# ---------------------------
url = "https://raw.githubusercontent.com/sehajpreet22/data/refs/heads/main/cleaned_crop_data_with_pbi_labels.csv"
data = pd.read_csv(url)

X = data.drop('label', axis=1)
y = data['label']
le = LabelEncoder()
y_encoded = le.fit_transform(y)

X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.3, random_state=0)
model = lgb.LGBMClassifier()
model.fit(X_train, y_train)

def predict_crop(ਨਾਈਟ੍ਰੋਜਨ, ਫਾਸਫੋਰਸ, ਪੋਟਾਸ਼ੀਅਮ, ਤਾਪਮਾਨ, ਨਮੀ, ਮਿੱਟੀ_pH, ਵਰਖਾ):
    input_data = np.array([[ਨਾਈਟ੍ਰੋਜਨ, ਫਾਸਫੋਰਸ, ਪੋਟਾਸ਼ੀਅਮ, ਤਾਪਮਾਨ, ਨਮੀ, ਮਿੱਟੀ_pH, ਵਰਖਾ]])
    pred = model.predict(input_data)[0]
    crop_name = le.inverse_transform([pred])[0]
    image_path = f"crop_images/{crop_name}.jpeg"
    if not os.path.exists(image_path):
        image_path = None  
    return image_path, f"🌾ਤੁਹਾਡੇ ਖੇਤ ਲਈ ਸੁਝਾਈ ਗਈ ਫਸਲ:  *{crop_name}*"

with gr.Blocks() as demo:
    gr.Markdown("# 🌾 **ਕਿਹੜੀ ਫਸਲ ਲਾਈਏ?**")

    with gr.Tabs():
            with gr.Row():
                ਨਾਈਟ੍ਰੋਜਨ= gr.Slider(0, 140, step=1, label="ਨਾਈਟ੍ਰੋਜਨ (kg/ha)")
                ਫਾਸਫੋਰਸ= gr.Slider(5, 95, step=1, label="ਫਾਸਫੋਰਸ (kg/ha)")
                ਪੋਟਾਸ਼ੀਅਮ= gr.Slider(5, 82, step=1, label="ਪੋਟਾਸ਼ੀਅਮ (kg/ha)")
            with gr.Row():
                ਤਾਪਮਾਨ= gr.Slider(15.63, 36.32, step=0.1, label="ਤਾਪਮਾਨ (°C)")
                ਨਮੀ= gr.Slider(14.2,99.98 , step=1, label="ਨਮੀ (%)")
            with gr.Row():
                ਮਿੱਟੀ_pH= gr.Slider(0, 14, step=0.1, label="ਮਿੱਟੀ ਦਾ pH")
                ਵਰਖਾ= gr.Slider(20.21, 253.72, step=1, label="ਵਰਖਾ (mm)")
            predict_btn = gr.Button("ਫਸਲ ਦੀ ਭਵਿੱਖਬਾਣੀ ਕਰੋ")
            crop_image_output = gr.Image(label="🌿 ਫਸਲ ਦੀ ਤਸਵੀਰ")
            crop_text_output = gr.Markdown()
            predict_btn.click(fn=predict_crop,
                              inputs=[ਨਾਈਟ੍ਰੋਜਨ,ਫਾਸਫੋਰਸ,ਪੋਟਾਸ਼ੀਅਮ,ਤਾਪਮਾਨ,ਨਮੀ,ਮਿੱਟੀ_pH,ਵਰਖਾ],
                              outputs=[crop_image_output, crop_text_output])
demo.launch()