Fixing image upload

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,15 @@
+FROM python:3.9-slim
+
+WORKDIR /app
+
+# Copy all files
+COPY . .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Hugging Face expects port 7860
+ENV PORT=7860
+
+# Run with Gunicorn (Production Server)
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "app:app"]

app.py

@@ -0,0 +1,69 @@
+import os
+from flask import Flask, render_template, request, jsonify
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from PIL import Image
+import io
+
+app = Flask(__name__)
+
+# 1. Load Model (Load once at startup)
+MODEL_PATH = 'model.h5'
+try:
+    model = load_model(MODEL_PATH)
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {e}")
+
+# Class names matching your Classification Report
+CLASS_NAMES = ['NonDemented', 'VeryMildDemented', 'MildDemented', 'ModerateDemented']
+
+def prepare_image(img_bytes):
+    # Convert bytes to PIL Image
+    img = Image.open(io.BytesIO(img_bytes))
+    
+    # Resize to 224x224 (As we found earlier)
+    img = img.resize((224, 224))
+    
+    # Convert to array and normalize
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0
+    return img_array
+
+# --- ROUTES ---
+
+# 1. Serve the Website
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+# 2. Handle Predictions
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file uploaded'}), 400
+    
+    file = request.files['file']
+    
+    try:
+        # Process image
+        processed_img = prepare_image(file.read())
+        
+        # Predict
+        prediction = model.predict(processed_img)
+        class_index = np.argmax(prediction)
+        confidence = float(np.max(prediction))
+        
+        result = {
+            'class': CLASS_NAMES[class_index],
+            'confidence': f"{confidence:.2%}"
+        }
+        return jsonify(result)
+        
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)

model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b892a36a277c2210a545eb8743425188a3ab8893196512be55ac5d7250dec445
+size 296838384

requirements.txt

@@ -0,0 +1,6 @@
+flask
+tensorflow-cpu
+numpy
+pillow
+h5py
+gunicorn

static/assets/index-CtQJEsfm.css

@@ -0,0 +1 @@
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templates/index.html

@@ -0,0 +1,16 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/brain.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta name="description" content="NeuraScan - Advanced AI-powered brain MRI Alzheimer's disease analysis tool for medical professionals" />
+    <meta name="author" content="Team 2 Batch 59 Biomedical Engineering Department Faculty of Engineering Helwan University" />
+    <title>NeuraScan | AI Brain MRI Alzheimer's Disease Analysis</title>
+    <script type="module" crossorigin src="/assets/index-DBG8gcSd.js"></script>
+    <link rel="stylesheet" crossorigin href="/assets/index-CtQJEsfm.css">
+  </head>
+  <body>
+    <div id="root"></div>
+  </body>
+</html>