Upload model files

app.py

@@ -1,8 +1,52 @@
-from fastapi import FastAPI
+import numpy as np
+from PIL import Image
+import torch
+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
 
 app = FastAPI()
 
+# Load the pre-trained model
+model_uri = "model.pth"
+model = torch.load(model_uri)
 
+
+# Define input schema for JSON requests
+class ImageInput(BaseModel):
+    image_path: str
+
+
+# Preprocess the image
+def preprocess_image(image):
+    image = image.convert('L')  # Convert to grayscale
+    image = image.resize((28, 28))
+    image = np.array(image) / 255.0  # Normalize to [0, 1]
+    image = (image - 0.1307) / 0.3081  # Standardize
+    image = torch.tensor(image).unsqueeze(0).float()  # Convert to tensor with batch dimension
+    return image
+
+
+# Root endpoint
 @app.get("/")
 def greet_json():
     return {"Hello": "World!"}
+
+
+# Predict endpoint for JSON input
+@app.post("/predict")
+async def predict_image(file: UploadFile = File(...)):
+    try:
+        # Read and preprocess the uploaded image
+        image = Image.open(file.file)
+        image = preprocess_image(image)
+
+        # Make prediction
+        model.eval()
+        with torch.no_grad():
+            output = model(image)
+            prediction = output.argmax(dim=1).item()
+
+        return JSONResponse(content={"prediction": f"The digit is {prediction}"})
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)

model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a25d6fbe70a15a02f24ff1e586b44b4fb0a626193293b44fb718a18851b9f12
+size 445812

requirements.txt

@@ -1,2 +1,5 @@
 fastapi
-uvicorn[standard]
+uvicorn[standard]
+numpy
+Pillow
+torch