Update app.py

app.py

@@ -13,7 +13,7 @@ from PIL import Image
 # ---------------------------
 # Crop Recommendation Setup
 # ---------------------------
-url = "https://raw.githubusercontent.com/sehajpreet22/data/refs/heads/main/cleaned_crop_data_with_pbi_labels.csv"
+url = "https://raw.githubusercontent.com/Pushpinder-Singh06/CSV-Files/refs/heads/main/crop_cleaned%20data.csv "
 data = pd.read_csv(url)
 
 X = data.drop('label', axis=1)
@@ -25,33 +25,36 @@ X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.3,
 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]])
+def predict_crop(nitrogen, phosphorus, potassium, temperature, humidity, soil_pH, rainfall):
+    input_data = np.array([[nitrogen, phosphorus, potassium, temperature, humidity, soil_pH, rainfall]])
     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}*"
+    return image_path, f"🌾 Recommended crop for your field:  *{crop_name}*"
 
 with gr.Blocks() as demo:
-    gr.Markdown("# 🌾 **ਕਿਹੜੀ ਫਸਲ ਲਾਈਏ?**")
+    gr.Markdown("# 🌾 **Which Crop Should I Grow?**")
 
     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()
+        with gr.Row():
+            nitrogen = gr.Slider(0, 140, step=1, label="Nitrogen (kg/ha)")
+            phosphorus = gr.Slider(5, 95, step=1, label="Phosphorus (kg/ha)")
+            potassium = gr.Slider(5, 82, step=1, label="Potassium (kg/ha)")
+        with gr.Row():
+            temperature = gr.Slider(15.63, 36.32, step=0.1, label="Temperature (°C)")
+            humidity = gr.Slider(14.2, 99.98, step=1, label="Humidity (%)")
+        with gr.Row():
+            soil_pH = gr.Slider(0, 14, step=0.1, label="Soil pH")
+            rainfall = gr.Slider(20.21, 253.72, step=1, label="Rainfall (mm)")
+
+        predict_btn = gr.Button("Predict Crop")
+        crop_image_output = gr.Image(label="🌿 Crop Image")
+        crop_text_output = gr.Markdown()
+
+        predict_btn.click(fn=predict_crop,
+                          inputs=[nitrogen, phosphorus, potassium, temperature, humidity, soil_pH, rainfall],
+                          outputs=[crop_image_output, crop_text_output])
+
+demo.launch()