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Upload 5 files
Browse files- app.py +53 -0
- index.html +19 -0
- script.js +29 -0
- styles.css +35 -0
- trained.h5 +3 -0
app.py
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from flask import Flask, request, jsonify
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import tensorflow as tf
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import numpy as np
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from tensorflow.keras.preprocessing import image
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import os
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from werkzeug.utils import secure_filename
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app = Flask(__name__)
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# Load trained model
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model = tf.keras.models.load_model("trained.h5")
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# Define allowed extensions
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ALLOWED_EXTENSIONS = {"png", "jpg", "jpeg"}
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def allowed_file(filename):
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return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
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def preprocess_image(img_path):
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img = image.load_img(img_path, target_size=(150, 150)) # Resize to model input size
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img_array = image.img_to_array(img) / 255.0 # Normalize
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img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
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return img_array
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@app.route("/predict", methods=["POST"])
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def predict():
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if "file" not in request.files:
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return jsonify({"error": "No file uploaded"}), 400
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file = request.files["file"]
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if file.filename == "":
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return jsonify({"error": "No selected file"}), 400
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if file and allowed_file(file.filename):
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filename = secure_filename(file.filename)
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file_path = os.path.join("uploads", filename)
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file.save(file_path)
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img_array = preprocess_image(file_path)
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prediction = model.predict(img_array)[0][0]
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os.remove(file_path) # Clean up
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result = "Pneumonia Detected" if prediction > 0.5 else "No Pneumonia"
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confidence = float(prediction) if prediction > 0.5 else 1 - float(prediction)
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return jsonify({"result": result, "confidence": round(confidence * 100, 2)})
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return jsonify({"error": "Invalid file format"}), 400
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if __name__ == "__main__":
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app.run(debug=True)
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index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Pneumonia Detection</title>
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<link rel="stylesheet" href="styles.css">
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</head>
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<body>
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<div class="container">
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<h1>Pneumonia Detection from X-ray</h1>
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<input type="file" id="fileInput" accept="image/*">
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<button onclick="uploadImage()">Upload & Predict</button>
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<div id="result"></div>
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</div>
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<script src="script.js"></script>
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</body>
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</html>
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script.js
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function uploadImage() {
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let fileInput = document.getElementById("fileInput");
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let file = fileInput.files[0];
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if (!file) {
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alert("Please select an image file.");
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return;
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}
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let formData = new FormData();
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formData.append("file", file);
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fetch("http://127.0.0.1:5000/predict", {
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method: "POST",
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body: formData
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})
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.then(response => response.json())
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.then(data => {
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if (data.error) {
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document.getElementById("result").innerText = "Error: " + data.error;
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} else {
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document.getElementById("result").innerText = `Result: ${data.result} (Confidence: ${data.confidence}%)`;
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}
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})
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.catch(error => {
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console.error("Error:", error);
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document.getElementById("result").innerText = "Prediction failed.";
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});
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}
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styles.css
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body {
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font-family: Arial, sans-serif;
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background: #f8f9fa;
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text-align: center;
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padding: 50px;
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}
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.container {
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background: white;
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padding: 20px;
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border-radius: 8px;
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box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
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width: 300px;
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margin: auto;
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}
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button {
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background: #007bff;
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color: white;
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border: none;
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padding: 10px 15px;
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cursor: pointer;
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margin-top: 10px;
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border-radius: 5px;
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}
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button:hover {
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background: #0056b3;
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}
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#result {
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margin-top: 20px;
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font-size: 18px;
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font-weight: bold;
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}
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trained.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:a829fd6b8fcb8c6f8be5849e73a88f2a4c7dd9a4d303e4ff0f3b5ae7b21ebe79
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size 23877888
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