Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import numpy as np
 import tensorflow as tf
 import joblib
-import sklearn  # Ensure scikit-learn is installed
+import sklearn  # Required for unpickling label_encoder
 
 # Load the saved model and label encoder
 model = tf.keras.models.load_model("crop_recommendation_model.h5")
@@ -18,19 +18,19 @@ def predict_crop(N, P, K, temperature, humidity, ph, rainfall):
     predicted_crop = label_encoder.inverse_transform([pred_class])[0]
     return predicted_crop
 
-# Define Gradio Interface
+# Define Gradio Interface using the updated API
 iface = gr.Interface(
     fn=predict_crop,
     inputs=[
-        gr.inputs.Number(label="Nitrogen (N)"),
-        gr.inputs.Number(label="Phosphorous (P)"),
-        gr.inputs.Number(label="Potassium (K)"),
-        gr.inputs.Number(label="Temperature (°C)"),
-        gr.inputs.Number(label="Humidity (%)"),
-        gr.inputs.Number(label="pH"),
-        gr.inputs.Number(label="Rainfall (mm)")
+        gr.Number(label="Nitrogen (N)"),
+        gr.Number(label="Phosphorous (P)"),
+        gr.Number(label="Potassium (K)"),
+        gr.Number(label="Temperature (°C)"),
+        gr.Number(label="Humidity (%)"),
+        gr.Number(label="pH"),
+        gr.Number(label="Rainfall (mm)")
     ],
-    outputs=gr.outputs.Textbox(label="Recommended Crop"),
+    outputs=gr.Textbox(label="Recommended Crop"),
     title="Crop Recommendation System",
     description="Enter soil and climate parameters to get the recommended crop."
 )