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app.py

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+import gradio as gr
+from transformers import AutoConfig, AutoModel
+import torch
+
+# Load the model
+config = AutoConfig.from_pretrained("GodfreyOwino/NPK_prediction_model2", trust_remote_code=True)
+model = AutoModel.from_pretrained("GodfreyOwino/NPK_prediction_model2", config=config, trust_remote_code=True)
+
+def predict(crop_name, target_yield, field_size, ph, organic_carbon, nitrogen, phosphorus, potassium, soil_moisture):
+    input_data = {
+        'crop_name': [crop_name],
+        'target_yield': [target_yield],
+        'field_size': [field_size],
+        'ph': [ph],
+        'organic_carbon': [organic_carbon],
+        'nitrogen': [nitrogen],
+        'phosphorus': [phosphorus],
+        'potassium': [potassium],
+        'soil_moisture': [soil_moisture]
+    }
+    
+    # Convert input data to tensors
+    input_tensors = {k: torch.tensor(v) for k, v in input_data.items()}
+    
+    # Make prediction
+    with torch.no_grad():
+        prediction = model(input_tensors)
+    
+    # Convert prediction to a list if it's a tensor
+    result = prediction.tolist() if isinstance(prediction, torch.Tensor) else prediction
+    
+    return str(result)  # Convert to string for Gradio output
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Textbox(label="Crop Name"),
+        gr.Number(label="Target Yield"),
+        gr.Number(label="Field Size"),
+        gr.Number(label="pH"),
+        gr.Number(label="Organic Carbon"),
+        gr.Number(label="Nitrogen"),
+        gr.Number(label="Phosphorus"),
+        gr.Number(label="Potassium"),
+        gr.Number(label="Soil Moisture")
+    ],
+    outputs="text",
+    title="NPK Prediction Model",
+    description="Enter the details to get NPK predictions."
+)
+
+iface.launch()