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app.py

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+"""
+Gradio web app for Crop Recommendation System
+Deploy to Hugging Face Spaces
+"""
+
+import json
+import joblib
+import numpy as np
+import pandas as pd
+import gradio as gr
+from pathlib import Path
+
+# Load model and metadata
+MODEL_PATH = "model.pkl"
+META_PATH = "model_meta.json"
+
+def load_model_and_metadata():
+    """Load trained model and metadata."""
+    if not Path(MODEL_PATH).exists():
+        raise FileNotFoundError(f"Model not found at {MODEL_PATH}")
+    if not Path(META_PATH).exists():
+        raise FileNotFoundError(f"Metadata not found at {META_PATH}")
+    
+    model = joblib.load(MODEL_PATH)
+    with open(META_PATH) as f:
+        meta = json.load(f)
+    
+    return model, meta
+
+# Load model once at startup
+try:
+    model, meta = load_model_and_metadata()
+    numeric_cols = meta["numeric_cols"]
+    label_classes = meta["label_classes"]
+except Exception as e:
+    raise RuntimeError(f"Failed to load model: {e}")
+
+def predict_crop(N, P, K, temperature, humidity, ph, rainfall):
+    """
+    Predict crop recommendation based on soil and weather parameters.
+    
+    Args:
+        N: Nitrogen content in soil (0-140)
+        P: Phosphorus content in soil (5-145)
+        K: Potassium content in soil (5-205)
+        temperature: Average temperature in Celsius (8-44)
+        humidity: Relative humidity in % (14-100)
+        ph: pH of soil (3.5-10)
+        rainfall: Annual rainfall in mm (20-3000)
+    
+    Returns:
+        Tuple of (recommended_crop, confidence, top_3_dict)
+    """
+    # Create input DataFrame
+    input_data = pd.DataFrame({
+        'N': [N],
+        'P': [P],
+        'K': [K],
+        'temperature': [temperature],
+        'humidity': [humidity],
+        'ph': [ph],
+        'rainfall': [rainfall]
+    })
+    
+    # Predict
+    prediction_encoded = model.predict(input_data)[0]
+    probabilities = model.predict_proba(input_data)[0]
+    
+    # Map to crop name
+    recommended_crop = label_classes[prediction_encoded]
+    confidence = probabilities[prediction_encoded]
+    
+    # Get top 3
+    top_3_indices = np.argsort(probabilities)[::-1][:3]
+    top_3_dict = {}
+    for rank, idx in enumerate(top_3_indices, 1):
+        crop = label_classes[idx]
+        conf = probabilities[idx]
+        top_3_dict[f"{rank}. {crop}"] = f"{conf:.2%}"
+    
+    return recommended_crop, f"{confidence:.2%}", top_3_dict
+
+# Create Gradio interface
+with gr.Blocks(title="Crop Recommendation System", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🌾 Crop Recommendation System
+    
+    Enter soil and weather parameters to get AI-powered crop recommendations with confidence scores.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### Soil Parameters")
+            N = gr.Slider(label="Nitrogen (N)", minimum=0, maximum=140, value=90, step=1)
+            P = gr.Slider(label="Phosphorus (P)", minimum=5, maximum=145, value=42, step=1)
+            K = gr.Slider(label="Potassium (K)", minimum=5, maximum=205, value=43, step=1)
+        
+        with gr.Column(scale=1):
+            gr.Markdown("### Weather Parameters")
+            temperature = gr.Slider(label="Temperature (°C)", minimum=8, maximum=44, value=21, step=0.1)
+            humidity = gr.Slider(label="Humidity (%)", minimum=14, maximum=100, value=82, step=1)
+            ph = gr.Slider(label="Soil pH", minimum=3.5, maximum=10, value=6.5, step=0.1)
+            rainfall = gr.Slider(label="Annual Rainfall (mm)", minimum=20, maximum=3000, value=203, step=10)
+    
+    predict_btn = gr.Button("🔍 Get Crop Recommendation", variant="primary", size="lg")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            recommended = gr.Textbox(
+                label="🌾 Recommended Crop",
+                interactive=False,
+                text_align="center",
+                scale=1
+            )
+            confidence = gr.Textbox(
+                label="✅ Confidence",
+                interactive=False,
+                text_align="center",
+                scale=1
+            )
+        
+        with gr.Column(scale=1):
+            top_3 = gr.JSON(label="📈 Top 3 Recommendations", interactive=False)
+    
+    predict_btn.click(
+        fn=predict_crop,
+        inputs=[N, P, K, temperature, humidity, ph, rainfall],
+        outputs=[recommended, confidence, top_3]
+    )
+    
+    gr.Markdown("""
+    ---
+    ### Parameter Ranges (based on training data)
+    - **Nitrogen (N)**: 0-140 kg/ha
+    - **Phosphorus (P)**: 5-145 kg/ha
+    - **Potassium (K)**: 5-205 kg/ha
+    - **Temperature**: 8-44°C
+    - **Humidity**: 14-100%
+    - **pH**: 3.5-10
+    - **Rainfall**: 20-3000 mm/year
+    """)
+
+if __name__ == "__main__":
+    demo.launch(share=False)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:55f4db2247f7113353648d1a4cefb13a83c156387261660211d7937a5aae5772
+size 10637949

model_meta.json

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+{
+  "target_col": "label",
+  "numeric_cols": [
+    "N",
+    "P",
+    "K",
+    "temperature",
+    "humidity",
+    "ph",
+    "rainfall"
+  ],
+  "cat_cols": [],
+  "classes": [
+    "apple",
+    "banana",
+    "blackgram",
+    "chickpea",
+    "coconut",
+    "coffee",
+    "cotton",
+    "grapes",
+    "jute",
+    "kidneybeans",
+    "lentil",
+    "maize",
+    "mango",
+    "mothbeans",
+    "mungbean",
+    "muskmelon",
+    "orange",
+    "papaya",
+    "pigeonpeas",
+    "pomegranate",
+    "rice",
+    "watermelon"
+  ],
+  "label_classes": [
+    "apple",
+    "banana",
+    "blackgram",
+    "chickpea",
+    "coconut",
+    "coffee",
+    "cotton",
+    "grapes",
+    "jute",
+    "kidneybeans",
+    "lentil",
+    "maize",
+    "mango",
+    "mothbeans",
+    "mungbean",
+    "muskmelon",
+    "orange",
+    "papaya",
+    "pigeonpeas",
+    "pomegranate",
+    "rice",
+    "watermelon"
+  ],
+  "use_smote": false,
+  "optuna_best_params": {
+    "n_estimators": 679,
+    "max_depth": 11,
+    "learning_rate": 0.1491643700183361,
+    "subsample": 0.9942200798137729,
+    "colsample_bytree": 0.8219727639354084,
+    "gamma": 0.009290561506072492,
+    "reg_lambda": 0.05810214543756159,
+    "reg_alpha": 0.0030983439938857015
+  }
+}