Added the prediction python script

app.py

@@ -0,0 +1,91 @@
+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)