Create app.py

app.py

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+import gradio as gr
+from fastapi import FastAPI
+from pydantic import BaseModel
+from huggingface_hub import hf_hub_download, HfApi
+import joblib
+import os
+from datetime import datetime, timedelta
+import pandas as pd
+
+REPO_ID = "GodfreyOwino/NPK_needs_mode2"
+FILENAME = "npk_needs_model.joblib"
+UPDATE_FREQUENCY = timedelta(days=1)
+
+class InputData(BaseModel):
+    crop_name: str
+    target_yield: float
+    field_size: float
+    ph: float
+    organic_carbon: float
+    nitrogen: float
+    phosphorus: float
+    potassium: float
+    soil_moisture: float
+
+def get_latest_model():
+    try:
+        api = HfApi()
+        remote_info = api.model_info(repo_id=REPO_ID)
+        remote_mtime = remote_info.lastModified
+        
+        cached_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+        
+        if os.path.exists(cached_path):
+            local_mtime = datetime.fromtimestamp(os.path.getmtime(cached_path))
+            
+            if datetime.now() - local_mtime < UPDATE_FREQUENCY:
+                print("Using cached model (checked recently)")
+                return joblib.load(cached_path)
+            
+            if remote_mtime > local_mtime:
+                print("Downloading updated model")
+                cached_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, force_download=True)
+            else:
+                print("Cached model is up-to-date")
+        else:
+            print("Downloading model for the first time")
+            cached_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+        
+    except Exception as e:
+        print(f"Error checking/downloading model: {e}")
+        print(f"Error type: {type(e)}")
+        print(f"Error details: {str(e)}")
+        raise Exception("Unable to download or find the model.")
+    
+    return joblib.load(cached_path)
+
+def predict(crop_name, target_yield, field_size, ph, organic_carbon, nitrogen, phosphorus, potassium, soil_moisture):
+    try:
+        model = get_latest_model()
+        
+        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]
+        }
+        
+        input_df = pd.DataFrame(input_data)
+        
+        prediction = model.predict(input_df)
+        
+        results = {
+            'nitrogen_need': float(prediction[0][0]),
+            'phosphorus_need': float(prediction[0][1]),
+            'potassium_need': float(prediction[0][2]),
+            'organic_matter_need': float(prediction[0][3]),
+            'lime_need': float(prediction[0][4])
+        }
+        
+        return results
+    except Exception as e:
+        return {"error": str(e)}
+
+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=gr.JSON(label="Prediction Results"),
+    title="NPK Needs Prediction",
+    description="Enter soil and crop details to predict NPK needs."
+)
+
+iface.launch()