Update app.py

app.py

@@ -85,16 +85,22 @@ for col in y_cols:
 label_to_encoded = {label: le.transform([label])[0] for label in le.classes_}
 
 def predict_crop_parameters(crop_name):
-    crop_name = crop_name.lower()
-    if crop_name not in label_to_encoded:
-        return f"❌ Error: '{crop_name}' not found in crop labels."
-    encoded_value = label_to_encoded[crop_name]
+    crop_name_lower = crop_name.lower()
+    if crop_name_lower not in label_to_encoded:
+        return f"❌ '{crop_name}' 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)
-    return predictions
+    
+    # Format output as markdown (clean readable list)
+    formatted_output = "\n".join([f"<b>{k}</b>: {v}" for k, v in predictions.items()])
+    return formatted_output
+
 
 
 
@@ -202,7 +208,7 @@ with gr.Blocks() as demo:
             
         with gr.TabItem("🔁 ਫਸਲ ਤੋਂ ਪੈਰਾਮੀਟਰ"):
             crop_input = gr.Dropdown(choices=crops, label="🌿 ਫਸਲ ਦਾ ਨਾਂ ਲਿਖੋ")
-            result_output = gr.JSON(label="🧪 ਅਨੁਕੂਲ ਪੈਰਾਮੀਟਰ")
+            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])