Revert "Update app.py"

This reverts commit d991f510f852779e08f85425b8b8fd5ea59e6332.

app.py

@@ -1,55 +1,67 @@
-import os
 import numpy as np
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
-from tensorflow.keras.applications.densenet import preprocess_input
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import HTMLResponse
-from starlette.requests import Request
-from starlette.staticfiles import StaticFiles
-from pathlib import Path
-
-# Initialize FastAPI app
-app = FastAPI()
+import os
+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
 
-# Define paths
-basepath = Path(__file__).parent
-modelpath = basepath / 'uploads' / 'best1den.keras'
-model = load_model(modelpath, compile=False, safe_mode=False)
+from flask import Flask , request, render_template
+#from werkzeug.utils import secure_filename
+#from gevent.pywsgi import WSGIServer
 
-# Serve static files if necessary
-app.mount("/static", StaticFiles(directory="static"), name="static")
+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')
 
-# 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'
-}
+@app.route('/about')
+def about():
+    return render_template('about.html')
 
-@app.get("/", response_class=HTMLResponse)
-async def index():
-    return "<h1>Welcome to the Ship Classification API</h1>"
+@app.route('/service')
+def service():
+    return render_template('service.html')
 
-@app.get("/about", response_class=HTMLResponse)
-async def about():
-    return "<h1>About the Ship Classification Model</h1>"
+@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)
 
-@app.get("/service", response_class=HTMLResponse)
-async def service():
-    return "<h1>Ship Classification Service</h1>"
+        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
 
-@app.post("/predict")
-async def predict(image_file: UploadFile = File(...)):
-    file_path = basepath / 'uploads' / image_file.filename
-    with file_path.open("wb") as buffer:
-        buffer.write(await image_file.read())
-    
-    img = image.load_img(file_path, target_size=(224, 224))
-    img = image.img_to_array(img)
-    img = np.expand_dims(img, axis=0)
-    img = preprocess_input(img)
-    
-    pred = model.predict(img).flatten()
-    result = val_dict[np.argmax(pred)]
-    
-    return {"ship_category": result}
+if __name__ == '__main__':
+    app.run(debug = True)
+        
+