Update app.py

app.py

@@ -5,31 +5,31 @@ from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 
 # Load dataset
-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)
 
 # Define the crops in Punjabi
 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'
+    'rice',
+    'maize',
+    'chickpea',
+    'kidneybeans',
+    'pigeonpeas',
+    'mothbeans',
+    'mungbean',
+    'blackgram',
+    'lentil',
+    'pomegranate',
+    'banana',
+    'mango',
+    'watermelon',
+    'muskmelon',
+    'orange',
+    'papaya',
+    'coconut',
+    'cotton',
+    'jute',
+    'coffee'
 ]
 
 # Fit the label encoder
@@ -39,15 +39,8 @@ data['crop_encoded'] = le.transform(data['label'])  # ✅ Then transform
 
 # Prepare for reverse prediction
 reverse_X = data[['crop_encoded']]
-y_cols = [
-    'ਨਾਈਟ੍ਰੋਜਨ (kg/ha)',
-    'ਫਾਸਫੋਰਸ (kg/ha)',
-    'ਪੋਟਾਸ਼ੀਅਮ (kg/ha)',
-    'ਤਾਪਮਾਨ (°C)',
-    'ਨਮੀ (%)',
-    'ਮਿੱਟੀ ਦਾ pH',
-    'ਵਰਖਾ (mm)'
-]
+y_cols = ['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall']
+
 
 # Train reverse models
 reverse_models = {}
@@ -65,7 +58,7 @@ label_to_encoded = {label: le.transform([label])[0] for label in le.classes_}
 def predict_crop_parameters(crop_name):
     crop_name = crop_name.strip()
     if crop_name not in label_to_encoded:
-        return f"❌ '<b>{crop_name}</b>' ਲਈ ਡਾਟਾ ਨਹੀਂ ਮਿਲਿਆ।"
+        return f"❌ No data found for '<b>{crop_name}</b>'."
 
     encoded_value = label_to_encoded[crop_name]
     input_data = [[encoded_value]]
@@ -84,10 +77,10 @@ def predict_crop_parameters(crop_name):
 
     
 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")
+    gr.Markdown("# 🌾 **AgroVision: Crop-Based Parameters**")
+    crop_input = gr.Dropdown(choices=crops, label="🌿 Select Crop Name")
+    result_output = gr.Markdown(label="🧪 Recommended Parameters")
+    run_btn = gr.Button("➡️ Predict Parameters")
     run_btn.click(fn=predict_crop_parameters, inputs=[crop_input], outputs=[result_output])