Update app.py

app.py

@@ -1,67 +1,80 @@
-import numpy as np
 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.layers import Flatten # type: ignore
-from tensorflow.keras.applications.densenet import preprocess_input# type: ignore
-import tensorflow as tf
+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
 
-from flask import Flask , request, render_template
-#from werkzeug.utils import secure_filename
-#from gevent.pywsgi import WSGIServer
+@app.get("/", response_class=HTMLResponse)
+async def index(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
 
-app = Flask(__name__)
-basepath = os.path.dirname(__file__)
-modelpath = os.path.join(basepath,'uploads',"best1den.keras")
-model = load_model(modelpath,compile=False, safe_mode=False)
-@app.route('/')
-def index():
-    return render_template('index.html')
+@app.get("/about", response_class=HTMLResponse)
+async def about(request: Request):
+    return templates.TemplateResponse("about.html", {"request": request})
 
-@app.route('/about')
-def about():
-    return render_template('about.html')
+@app.get("/service", response_class=HTMLResponse)
+async def service(request: Request):
+    return templates.TemplateResponse("service.html", {"request": request})
 
-@app.route('/service')
-def service():
-    return render_template('service.html')
+@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())
 
-@app.route('/predict',methods = ['GET','POST'])
-def upload():
-    if request.method == 'POST':
-        f = request.files['image']
-        
-        #print("current path")
-        basepath = os.path.dirname(__file__)
-        print("current path", basepath)
-        filepath = os.path.join(basepath,'uploads',f.filename)
-        print("upload folder is ", filepath)
-        f.save(filepath)
+    # 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)
 
-        img=image.load_img(filepath,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)
-        pred=model.predict(img)
-        pred=pred.flatten()
-        pred=list(pred)
-        n=max(pred)
-        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'}
-        result=val_dict[pred.index(n)]
-        print(result)
-        text = "the Ship Category is " + result
-    return text
+    # 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}"}
 
-if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=7860, debug=True)
-        
-        
+# 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)