Updated `app.py`. Made it accessible from external machines.

app.py

@@ -1,43 +1,42 @@
-from flask import Flask, render_template, request
+import gradio as gr
 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']
+CLASS_LABELS = ["NORMAL", "PNEUMONIA"]
 CONFIDENCE_THRESHOLD = 0.7
 
-def preprocess_image(file_path):
+
+def preprocess_image(image):
     """
     Preprocesses the input image for the model.
     Args:
-        file_path (str): Path to the input image.
+        image (PIL.Image): Input image.
     Returns:
         numpy.ndarray: Preprocessed image ready for prediction.
     """
-    img = Image.open(file_path).convert('RGB')  # Ensure the image is RGB
+    img = image.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):
+
+def predict_image(image):
     """
     Predicts the class of the input image with confidence-based filtering.
     Args:
-        file_path (str): Path to the input image.
+        image (PIL.Image): Input image.
     Returns:
         str: Predicted class label or uncertainty message.
         float: Confidence score (if applicable).
     """
-    img_array = preprocess_image(file_path)
+    img_array = preprocess_image(image)
     prediction = model.predict(img_array)
     confidence = np.max(prediction)
 
@@ -48,44 +47,16 @@ def predict_image(file_path):
     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,
-            )
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Textbox(label="Predicted Class"), gr.Textbox(label="Confidence")],
+    title="Pneumonia Detection CNN",
+    description="Upload an image to classify it as NORMAL or PNEUMONIA.",
+)
 
+# Launch the interface
 if __name__ == "__main__":
-    app.run(debug=True)
+    interface.launch(server_name="0.0.0.0", server_port=7860)