commit flask

app.py

@@ -1,80 +1,67 @@
-import os
 import numpy as np
-import tensorflow as tf
+import os
 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
+from tensorflow.keras.layers import Flatten # type: ignore
+from tensorflow.keras.applications.densenet import preprocess_input# type: ignore
+import tensorflow as tf
 
-@app.get("/", response_class=HTMLResponse)
-async def index(request: Request):
-    return templates.TemplateResponse("index.html", {"request": request})
+from flask import Flask , request, render_template
+#from werkzeug.utils import secure_filename
+#from gevent.pywsgi import WSGIServer
 
-@app.get("/about", response_class=HTMLResponse)
-async def about(request: Request):
-    return templates.TemplateResponse("about.html", {"request": request})
+app = Flask(__name__)
+basepath = os.path.dirname(__file__)
+modelpath = os.path.join(basepath,'uploads',"densenet_ship.h5")
+model = load_model(modelpath)
+@app.route('/')
+def index():
+    return render_template('index.html')
 
-@app.get("/service", response_class=HTMLResponse)
-async def service(request: Request):
-    return templates.TemplateResponse("service.html", {"request": request})
+@app.route('/about')
+def about():
+    return render_template('about.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('/service')
+def service():
+    return render_template('service.html')
 
-    # 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)
+@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)
 
-    # 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}"}
+        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
 
-# 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)
+if __name__ == '__main__':
+    app.run(debug = True)
+        
+        

dockerfile

@@ -1,23 +1,18 @@
-# Use official Python runtime
+# Use official Python runtime as base image
 FROM python:3.9
 
-# Create and switch to a non-root user
-RUN useradd -m -u 1000 user
-USER user
-ENV PATH="/home/user/.local/bin:$PATH"
-
-# Set working directory inside container
+# Set the working directory in the container
 WORKDIR /app
 
-# Copy requirements file and install dependencies
-COPY --chown=user ./requirements.txt requirements.txt
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+# Copy application files to the container
+COPY . .
 
-# Copy the entire app code
-COPY --chown=user . /app
+# Install required Python packages
+RUN pip install --no-cache-dir -r requirements.txt
 
-# Expose port 7860 (used by Hugging Face Spaces)
+# Expose Flask port (default: 5000)
 EXPOSE 7860
 
-# Run FastAPI app with Uvicorn
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+
+# Command to run the Flask application
+CMD ["gunicorn", "--bind", "0.0.0.0:5000", "app:app"]

requirements.txt

@@ -1,6 +1,6 @@
-fastapi
-uvicorn
-tensorflow
 numpy
-jinja2
-pillow
+tensorflow
+keras
+Keras-Preprocessing
+gunicorn
+flask