basic app for testing

app.py

@@ -1,44 +1,61 @@
-from fastai.vision.all import *
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from PIL import Image
-import io
-import base64
-
-def get_x(i):
-    # Convert NumPy array to a single-channel PIL image with inverted colors
-    return PILImageBW.create(all_noise[i])
-
-def get_y(i):
-    return all_thresh[i].astype(np.float32)
-
-def get_items(_):
-    return range(len(all_noise))
-
-# Load the model
-learn = load_learner('model.pkl')
+from fastapi import FastAPI
 
 app = FastAPI()
 
-class ImageData(BaseModel):
-    image: str
-
-def predict_image(img):
-    img = img.convert("L")
-    img = img.resize((28, 28))
-    img = np.array(img)
-    return f"{learn.predict(img)[0][0]:.2f}"
-
 @app.get("/")
 def read_root():
     return {"message": "Hello World"}
 
-@app.post("/predict")
-async def predict(data: ImageData):
-    try:
-        image_data = base64.b64decode(data.image)
-        img = Image.open(io.BytesIO(image_data))
-        probability = predict_image(img)
-        return {"probability": probability}
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=str(e))
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)
+
+# from fastai.vision.all import *
+# from fastapi import FastAPI, HTTPException
+# from pydantic import BaseModel
+# from PIL import Image
+# import io
+# import base64
+# import uvicorn
+
+# def get_x(i):
+#     # Convert NumPy array to a single-channel PIL image with inverted colors
+#     return PILImageBW.create(all_noise[i])
+
+# def get_y(i):
+#     return all_thresh[i].astype(np.float32)
+
+# def get_items(_):
+#     return range(len(all_noise))
+
+# # Load the model
+# learn = load_learner('model.pkl')
+
+# app = FastAPI()
+
+# class ImageData(BaseModel):
+#     image: str
+
+# def predict_image(img):
+#     img = img.convert("L")
+#     img = img.resize((28, 28))
+#     img = np.array(img)
+#     return f"{learn.predict(img)[0][0]:.2f}"
+
+# @app.get("/")
+# def read_root():
+#     return {"message": "Hello World"}
+
+# @app.post("/predict")
+# async def predict(data: ImageData):
+#     try:
+#         image_data = base64.b64decode(data.image)
+#         img = Image.open(io.BytesIO(image_data))
+#         probability = predict_image(img)
+#         return {"probability": probability}
+#     except Exception as e:
+#         raise HTTPException(status_code=400, detail=str(e))
+    
+# if __name__ == "__main__":
+#     import uvicorn
+#     uvicorn.run(app, host="0.0.0.0", port=7860)