Update app.py

app.py

@@ -1,92 +1,92 @@
-import gradio as gr
-import joblib
-import numpy as np
-
-# Load the trained models
-with open('rf_crop.pkl', 'rb') as file:
-    crop_model = joblib.load(file)
-
-with open('knn_fertilizer.pkl', 'rb') as file:
-    fertilizer_model = joblib.load(file)
-    
-with open('Scaler_fertilizer.pkl', 'rb') as file:  # Assuming you saved the scaler during model training
-    scaler = joblib.load(file)
-
-# Label Encoders for the models
-crop_label_encoder = {
-    0: "Sugarcane", 1: "Wheat", 2: "Cotton", 3: "Jowar", 4: "Rice",
-    5: "Maize", 6: "Groundnut", 7: "Grapes", 8: "Tur", 9: "Ginger",
-    10: "Turmeric", 11: "Urad", 12: "Gram", 13: "Moong", 14: "Soybean", 15: "Masoor"
-}
-
-fertilizer_label_encoder = {
-    0: "Urea", 1: "DAP", 2: "MOP", 3: "SSP", 4: "19:19:19 NPK",
-    5: "Chilated Micronutrient", 6: "50:26:26 NPK", 7: "Magnesium Sulphate", 
-    8: "10:26:26 NPK", 9: "Ferrous Sulphate", 10: "13:32:26 NPK", 
-    11: "10:10:10 NPK", 12: "Ammonium Sulphate", 13: "12:32:16 NPK", 
-    14: "White Potash", 15: "Hydrated Lime", 16: "20:20:20 NPK", 
-    17: "18:46:00 NPK", 18: "Sulphur"
-}
-
-# Prediction functions
-def predict_crop(Nitrogen, Phosphorus, Potassium, pH, Rainfall, Temperature):
-    # Prepare the input data and scale it
-    crop_input = np.array([[Nitrogen, Phosphorus, Potassium, pH, Rainfall, Temperature]])
-    crop_input_scaled = scaler.transform(crop_input)
-    
-    # Predict the crop
-    crop_prediction = crop_model.predict(crop_input_scaled)
-    return crop_prediction[0]
-
-def predict_fertilizer(Nitrogen, Phosphorus, Potassium, pH, Rainfall, Temperature, Crop):
-    # Prepare the input data and scale it
-    crop_index = list(crop_label_encoder.keys())[list(crop_label_encoder.values()).index(Crop)]
-    fertilizer_input = np.array([[Nitrogen, Phosphorus, Potassium, pH, Rainfall, Temperature, crop_index]])
-    fertilizer_input_scaled = scaler.transform(fertilizer_input[:, :-1])
-    
-    # Add crop index back to the scaled input
-    fertilizer_input_scaled = np.hstack([fertilizer_input_scaled, [[crop_index]]])
-    
-    # Predict the fertilizer
-    fertilizer_prediction = fertilizer_model.predict(fertilizer_input_scaled)
-
-    return fertilizer_label_encoder[int(fertilizer_prediction[0])]
-
-# Gradio Interface for Crop Prediction
-crop_interface = gr.Interface(
-    fn=predict_crop,
-    inputs=[
-        gr.Number(label="Nitrogen"),
-        gr.Number(label="Phosphorus"),
-        gr.Number(label="Potassium"),
-        gr.Number(label="pH"),
-        gr.Number(label="Rainfall"),
-        gr.Number(label="Temperature")
-    ],
-    outputs=gr.Label(num_top_classes=1),
-    title="Crop Prediction",
-    allow_flagging='never'
-)
-
-# Gradio Interface for Fertilizer Prediction
-fertilizer_interface = gr.Interface(
-    fn=predict_fertilizer,
-    inputs=[
-        gr.Number(label="Nitrogen"),
-        gr.Number(label="Phosphorus"),
-        gr.Number(label="Potassium"),
-        gr.Number(label="pH"),
-        gr.Number(label="Rainfall"),
-        gr.Number(label="Temperature"),
-        gr.Dropdown(label="Crop", choices=list(crop_label_encoder.values()))
-    ],
-    outputs=gr.Label(num_top_classes=1),
-    title="Fertilizer Prediction",
-    allow_flagging='never'
-)
-
-# Create a Tabbed Interface in Gradio
-app = gr.TabbedInterface([crop_interface, fertilizer_interface], ["Crop Prediction", "Fertilizer Prediction"])
-
-# Launch the app
-app.launch()
+import gradio as gr
+import pickle
+import numpy as np
+
+# Load the trained models
+with open('rf_crop.pkl', 'rb') as file:
+    crop_model = pickle.load(file)
+
+with open('knn_fertilizer.pkl', 'rb') as file:
+    fertilizer_model = pickle.load(file)
+    
+with open('Scaler_fertilizer.pkl', 'rb') as file:  # Assuming you saved the scaler during model training
+    scaler = pickle.load(file)
+
+# Label Encoders for the models
+crop_label_encoder = {
+    0: "Sugarcane", 1: "Wheat", 2: "Cotton", 3: "Jowar", 4: "Rice",
+    5: "Maize", 6: "Groundnut", 7: "Grapes", 8: "Tur", 9: "Ginger",
+    10: "Turmeric", 11: "Urad", 12: "Gram", 13: "Moong", 14: "Soybean", 15: "Masoor"
+}
+
+fertilizer_label_encoder = {
+    0: "Urea", 1: "DAP", 2: "MOP", 3: "SSP", 4: "19:19:19 NPK",
+    5: "Chilated Micronutrient", 6: "50:26:26 NPK", 7: "Magnesium Sulphate", 
+    8: "10:26:26 NPK", 9: "Ferrous Sulphate", 10: "13:32:26 NPK", 
+    11: "10:10:10 NPK", 12: "Ammonium Sulphate", 13: "12:32:16 NPK", 
+    14: "White Potash", 15: "Hydrated Lime", 16: "20:20:20 NPK", 
+    17: "18:46:00 NPK", 18: "Sulphur"
+}
+
+# Prediction functions
+def predict_crop(Nitrogen, Phosphorus, Potassium, pH, Rainfall, Temperature):
+    # Prepare the input data and scale it
+    crop_input = np.array([[Nitrogen, Phosphorus, Potassium, pH, Rainfall, Temperature]])
+    crop_input_scaled = scaler.transform(crop_input)
+    
+    # Predict the crop
+    crop_prediction = crop_model.predict(crop_input_scaled)
+    return crop_prediction[0]
+
+def predict_fertilizer(Nitrogen, Phosphorus, Potassium, pH, Rainfall, Temperature, Crop):
+    # Prepare the input data and scale it
+    crop_index = list(crop_label_encoder.keys())[list(crop_label_encoder.values()).index(Crop)]
+    fertilizer_input = np.array([[Nitrogen, Phosphorus, Potassium, pH, Rainfall, Temperature, crop_index]])
+    fertilizer_input_scaled = scaler.transform(fertilizer_input[:, :-1])
+    
+    # Add crop index back to the scaled input
+    fertilizer_input_scaled = np.hstack([fertilizer_input_scaled, [[crop_index]]])
+    
+    # Predict the fertilizer
+    fertilizer_prediction = fertilizer_model.predict(fertilizer_input_scaled)
+
+    return fertilizer_label_encoder[int(fertilizer_prediction[0])]
+
+# Gradio Interface for Crop Prediction
+crop_interface = gr.Interface(
+    fn=predict_crop,
+    inputs=[
+        gr.Number(label="Nitrogen"),
+        gr.Number(label="Phosphorus"),
+        gr.Number(label="Potassium"),
+        gr.Number(label="pH"),
+        gr.Number(label="Rainfall"),
+        gr.Number(label="Temperature")
+    ],
+    outputs=gr.Label(num_top_classes=1),
+    title="Crop Prediction",
+    allow_flagging='never'
+)
+
+# Gradio Interface for Fertilizer Prediction
+fertilizer_interface = gr.Interface(
+    fn=predict_fertilizer,
+    inputs=[
+        gr.Number(label="Nitrogen"),
+        gr.Number(label="Phosphorus"),
+        gr.Number(label="Potassium"),
+        gr.Number(label="pH"),
+        gr.Number(label="Rainfall"),
+        gr.Number(label="Temperature"),
+        gr.Dropdown(label="Crop", choices=list(crop_label_encoder.values()))
+    ],
+    outputs=gr.Label(num_top_classes=1),
+    title="Fertilizer Prediction",
+    allow_flagging='never'
+)
+
+# Create a Tabbed Interface in Gradio
+app = gr.TabbedInterface([crop_interface, fertilizer_interface], ["Crop Prediction", "Fertilizer Prediction"])
+
+# Launch the app
+app.launch()