Upload app.py

app.py

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+import gradio as gr
+import numpy as np
+import joblib
+
+# ==============================
+# LOAD MODELS
+# ==============================
+scaler   = joblib.load("scaler.pkl")
+le_label = joblib.load("le_label.pkl")
+le_type  = joblib.load("le_type.pkl")
+
+rf_label_model = joblib.load("rf_label_model.pkl")
+rf_type_model  = joblib.load("rf_type_model.pkl")
+
+# ==============================
+# PREDICTION FUNCTION
+# ==============================
+def predict(N, P, K, temperature, humidity, ph):
+    sample = np.array([[N, P, K, temperature, humidity, ph]])
+    sample_scaled = scaler.transform(sample)
+
+    pred_label = le_label.inverse_transform(
+        rf_label_model.predict(sample_scaled)
+    )[0]
+
+    pred_type = le_type.inverse_transform(
+        rf_type_model.predict(sample_scaled)
+    )[0]
+
+    return f"🌱 Culture: {pred_label}\n🌍 Soil Type: {pred_type}"
+
+# ==============================
+# GRADIO INTERFACE
+# ==============================
+interface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Nitrogen (N)"),
+        gr.Number(label="Phosphorus (P)"),
+        gr.Number(label="Potassium (K)"),
+        gr.Number(label="Temperature (°C)"),
+        gr.Number(label="Humidity (%)"),
+        gr.Number(label="pH"),
+    ],
+    outputs="text",
+    title="Crop & Soil Prediction Model",
+    description="Prediction using Random Forest model trained in Kaggle"
+)
+
+interface.launch()