Create app.py

app.py

@@ -0,0 +1,64 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import numpy as np
+from huggingface_hub import hf_hub_download, HfApi
+import joblib
+import os
+from datetime import datetime, timedelta
+
+app = FastAPI()
+
+REPO_ID = "GodfreyOwino/NPK_needs_mode2"
+FILENAME = "npk_needs_model.joblib"
+UPDATE_FREQUENCY = timedelta(days=1)
+
+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 HTTPException(status_code=500, detail="Unable to download or find the model.")
+    
+    return joblib.load(cached_path)
+
+model = get_latest_model()
+print("Model loaded successfully")
+
+class InputData(BaseModel):
+    features: list[float]
+
+@app.post("/predict")
+async def predict(data: InputData):
+    try:
+        input_data = np.array(data.features).reshape(1, -1)
+        prediction = model.predict(input_data)
+        return {"prediction": prediction.tolist()}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
+
+@app.get("/")
+async def root():
+    return {"message": "NPK Needs Prediction Model API"}