delete

app.py

@@ -1,80 +0,0 @@
-import os
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.models import load_model # type: ignore
-from tensorflow.keras.preprocessing import image # type: ignore
-from tensorflow.keras.applications.densenet import preprocess_input # type: ignore
-
-from fastapi import FastAPI, File, UploadFile, Request
-from fastapi.templating import Jinja2Templates
-from fastapi.responses import HTMLResponse
-import uvicorn
-
-# Initialize FastAPI app
-app = FastAPI()
-
-# Set up template rendering (similar to Flask’s render_template)
-templates = Jinja2Templates(directory="templates")
-
-# Define paths
-BASE_DIR = os.path.dirname(__file__)
-MODEL_PATH = os.path.join(BASE_DIR, 'uploads', "densenet_ship.h5")
-
-# Load the model
-model = load_model(MODEL_PATH)
-
-# Define ship categories
-val_dict = {
-    0: 'Aircraft Carrier',
-    1: 'Bulkers',
-    2: 'Car Carrier',
-    3: 'Container Ship',
-    4: 'Cruise',
-    5: 'DDG',
-    6: 'Recreational',
-    7: 'Sailboat',
-    8: 'Submarine',
-    9: 'Tug'
-}
-
-# Define Routes
-
-@app.get("/", response_class=HTMLResponse)
-async def index(request: Request):
-    return templates.TemplateResponse("index.html", {"request": request})
-
-@app.get("/about", response_class=HTMLResponse)
-async def about(request: Request):
-    return templates.TemplateResponse("about.html", {"request": request})
-
-@app.get("/service", response_class=HTMLResponse)
-async def service(request: Request):
-    return templates.TemplateResponse("service.html", {"request": request})
-
-@app.post("/predict/")
-async def predict(image_file: UploadFile = File(...)):
-    # Save uploaded file
-    file_path = os.path.join(BASE_DIR, 'uploads', image_file.filename)
-    
-    with open(file_path, "wb") as buffer:
-        buffer.write(await image_file.read())
-
-    # Load and preprocess image
-    img = image.load_img(file_path, target_size=(224, 224))
-    img = image.img_to_array(img)
-    img = img.reshape((1, img.shape[0], img.shape[1], img.shape[2]))
-    img = preprocess_input(img)
-
-    # Make prediction
-    pred = model.predict(img)
-    pred = pred.flatten()
-    
-    # Get predicted category
-    predicted_class = val_dict[np.argmax(pred)]
-    
-    # Return result
-    return {"category": predicted_class, "message": f"The Ship Category is {predicted_class}"}
-
-# Run the FastAPI app with uvicorn (needed when not using Docker Spaces)
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)