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from flask import Flask, render_template, request
import numpy as np
from tensorflow.keras.models import load_model
from PIL import Image
import os
app = Flask(__name__)
# Load the trained model
MODEL_PATH = "vgg19_fine_tuned_block5_91.keras"
model = load_model(MODEL_PATH)
# Define class labels and confidence threshold
CLASS_LABELS = ['NORMAL', 'PNEUMONIA']
CONFIDENCE_THRESHOLD = 0.7
def preprocess_image(file_path):
"""
Preprocesses the input image for the model.
Args:
file_path (str): Path to the input image.
Returns:
numpy.ndarray: Preprocessed image ready for prediction.
"""
img = Image.open(file_path).convert('RGB') # Ensure the image is RGB
img = img.resize((128, 128)) # Resize to model's input size
img_array = np.array(img) / 255.0 # Normalize pixel values to [0, 1]
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
return img_array
def predict_image(file_path):
"""
Predicts the class of the input image with confidence-based filtering.
Args:
file_path (str): Path to the input image.
Returns:
str: Predicted class label or uncertainty message.
float: Confidence score (if applicable).
"""
img_array = preprocess_image(file_path)
prediction = model.predict(img_array)
confidence = np.max(prediction)
# Apply confidence threshold
if confidence < CONFIDENCE_THRESHOLD:
return "Uncertain: Low confidence", confidence
predicted_class = CLASS_LABELS[np.argmax(prediction)]
return predicted_class, confidence
@app.route("/", methods=["GET"])
def home():
return render_template("index.html")
@app.route("/predict", methods=["POST"])
def predict():
if "file" not in request.files:
return "No file uploaded", 400
file = request.files["file"]
if file.filename == "":
return "No file selected", 400
if file:
# Save the uploaded file temporarily
upload_path = os.path.join("static/uploads", file.filename)
os.makedirs("static/uploads", exist_ok=True)
file.save(upload_path)
# Make prediction
predicted_class, confidence = predict_image(upload_path)
# Format the result based on prediction type
if "Uncertain" in predicted_class:
message = "The model is uncertain about the prediction. Please try another image."
return render_template(
"result.html",
prediction=message,
confidence=f"{confidence*100:.2f}%",
image_path=upload_path,
)
else:
return render_template(
"result.html",
prediction=predicted_class,
confidence=f"{confidence*100:.2f}%",
image_path=upload_path,
)
if __name__ == "__main__":
app.run(debug=True) |