Create app.py

app.py

@@ -0,0 +1,58 @@
+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.TabItem("🌾ਕਿਹੜੀ ਫਸਲ ਲਾਈਏ? "):
+            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()