Update app.py

app.py

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+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 torch
+from torchvision import models, transforms
+
+
+
+# ---------------------------
+# Reverse Prediction Setup
+# ---------------------------
+
+crops = [
+    'ਚੌਲ',
+    'ਮੱਕੀ',
+    'ਛੋਲੇ',
+    'ਰਾਜ਼ਮਾ',
+    'ਅਰਹਰ ਦੀ ਦਾਲ',
+    'ਮੋਠ ਦੀ ਦਾਲ',
+    'ਮੂੰਗ ਦੀ ਦਾਲ',
+    'ਮਾਂਹ ਦੀ ਦਾਲ',
+    'ਮਸਰ ਦੀ ਦਾਲ',
+    'ਅਨਾਰ',
+    'ਕੇਲਾ',
+    'ਅੰਬ',
+    'ਤਰਬੂਜ਼',
+    'ਖਰਬੂਜ਼ਾ',
+    'ਸੰਤਰਾ',
+    'ਪਪੀਤਾ',
+    'ਨਾਰੀਅਲ',
+    'ਕਪਾਹ',
+    'ਜੂਟ',
+    'ਕੌਫ਼ੀ'
+]
+
+data['crop_encoded'] = le.transform(data['label'])
+reverse_X = data[['crop_encoded']]
+y_cols = ['ਨਾਈਟ੍ਰੋਜਨ (kg/ha)', 'ਫਾਸਫੋਰਸ (kg/ha)', 'ਪੋਟਾਸ਼ੀਅਮ (kg/ha)', 'ਤਾਪਮਾਨ (°C)', 'ਨਮੀ (%)', 'ਮਿੱਟੀ ਦਾ pH', 'ਵਰਖਾ (mm)']
+reverse_models = {}
+for col in y_cols:
+    y = data[col]
+    X_train, X_test, y_train, y_test = train_test_split(reverse_X, y, test_size=0.2, random_state=42)
+    model_r = lgb.LGBMRegressor()
+    model_r.fit(X_train, y_train)
+    reverse_models[col] = model_r
+
+label_to_encoded = {label: le.transform([label])[0] for label in le.classes_}
+
+
+def predict_crop_parameters(crop_name):
+    crop_name_lower = crop_name.lower()
+    if crop_name_lower not in label_to_encoded:
+        return f"❌ '<b>{crop_name}</b>' ਲਈ ਡਾਟਾ ਨਹੀਂ ਮਿਲਿਆ।"
+
+    encoded_value = label_to_encoded[crop_name_lower]
+    input_data = [[encoded_value]]
+    
+    predictions = {}
+    for param, model_r in reverse_models.items():
+        predicted_value = model_r.predict(input_data)[0]
+        predictions[param] = round(predicted_value, 2)
+    
+    formatted_output = ""
+    for k, v in predictions.items():
+        formatted_output += f"<b>{k}</b>: {v}<br>"
+
+    return formatted_output
+
+    
+with gr.Blocks() as demo:
+    gr.Markdown("# 🌾 **AgroVision: ਫਸਲ ਤੋਂ ਪੈਰਾਮੀਟਰ**")
+            crop_input = gr.Dropdown(choices=crops, label="🌿 ਫਸਲ ਦਾ ਨਾਂ ਲਿਖੋ")
+            result_output = gr.Markdown(label="🧪 ਅਨੁਕੂਲ ਪੈਰਾਮੀਟਰ")
+            run_btn = gr.Button("➡️ ਭਵਿੱਖਬਾਣੀ ਲਵੋ")
+            run_btn.click(fn=predict_crop_parameters, inputs=[crop_input], outputs=[result_output])
+
+
+demo.launch()