Update app.py

app.py

@@ -3,16 +3,30 @@ from tensorflow.keras.models import load_model
 import numpy as np
 from PIL import Image
 
+# Load model
 model = load_model('xray_image_classifier_model.keras')
 
+# Define solutions
+solutions = {
+    "Pneumonia": "Consult a doctor immediately. Follow prescribed antibiotics if given, rest well, and stay hydrated.",
+    "Normal": "Your X-ray appears normal. However, if you experience symptoms, consult a doctor for further evaluation."
+}
+
+# Prediction function
 def predict(image):
     img = image.resize((150, 150))
     img_array = np.array(img) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
+    
     prediction = model.predict(img_array)
-    predicted_class = 'Pneumonia' if prediction > 0.5 else 'Normal'
-    return predicted_class
+    predicted_class = "Pneumonia" if prediction > 0.5 else "Normal"
+    
+    # Get the corresponding solution
+    solution = solutions.get(predicted_class, "No specific advice available.")
+    
+    return predicted_class, solution
 
+# CSS Styling
 css = """
     .gradio-container {
         background-color: #f5f5f5;
@@ -20,36 +34,40 @@ css = """
     }
     
     .gr-button {
-    background-color:#007bff;
-    color: blue; 
-    border: none; /* Remove default border */
-    border-radius: 5px; /* Rounded corners */
-    font-size: 16px; /* Adjust font size */
-    padding: 10px 20px; /* Add padding for a larger clickable area */
-    cursor: pointer; /* Change cursor to pointer to indicate it's clickable */
-    transition: background-color 0.3s ease; /* Smooth hover transition */
+        background-color:#007bff;
+        color: white; 
+        border: none;
+        border-radius: 5px;
+        font-size: 16px;
+        padding: 10px 20px;
+        cursor: pointer;
+        transition: background-color 0.3s ease;
     }
 
     .gr-button:hover {
-    background-color: #0056b3; /* Darker blue on hover */
+        background-color: #0056b3;
     }
+
     .gr-textbox, .gr-image {
         border: 2px dashed #007bff;
         padding: 20px;
         border-radius: 10px;
         background-color: #ffffff;
     }
+
     .gr-box-text {
         color: #007bff;
         font-size: 22px;
         font-weight: bold;
         text-align: center;
     }
+
     h1 {
         font-size: 36px;
-        color: #007bff; 
+        color: #007bff;
         text-align: center;
     }
+
     p {
         font-size: 20px;
         color: #333;
@@ -57,6 +75,7 @@ css = """
     }
 """
 
+# Description
 description = """
 **Automated Pneumonia Detection via Chest X-ray Classification**
 
@@ -67,34 +86,22 @@ This model leverages deep learning techniques to classify chest X-ray images as
 - InceptionV3 for transfer learning
 - Numpy, Pandas, and Matplotlib for data handling and visualization
 - Flask and Gradio for deployment and user interaction
-
 """
 
-examples = [
-    ["samples/normal_xray1.png"],
-    ["samples/pneumonia_xray1.png"],
-]
-
+# Gradio UI
 with gr.Blocks(css=css) as interface:
     gr.Markdown("<h1>Automated Pneumonia Detection via Chest X-ray Classification</h1>")
     gr.Markdown("<p>Submit a chest X-ray image below.</p>")
     
     with gr.Row():
         image_input = gr.Image(label="Drop Image Here", type="pil", elem_classes=["gr-image", "gr-box-text"])
-        output = gr.Textbox(label="Model Analysis Output", elem_classes=["gr-textbox", "gr-box-text"])
+        output_prediction = gr.Textbox(label="Model Analysis Output", elem_classes=["gr-textbox", "gr-box-text"])
+        output_solution = gr.Textbox(label="Recommended Solution", elem_classes=["gr-textbox", "gr-box-text"])
 
     submit_btn = gr.Button("Initiate Diagnostic Analysis", elem_classes=["gr-button"])
-    submit_btn.click(fn=predict, inputs=image_input, outputs=output)
-
-    gr.Markdown(
-    '<div style="background-color: yellow; padding: 10px; border-radius: 5px; font-weight: bold;">'
-    'Sample Images: To test the model, select one of the sample images provided below. '
-    'Click on an image and then press the "Initiate Diagnostic Analysis" button to receive the results.'
-    '</div>'
-    )
-
-    gr.Examples(examples=examples, inputs=image_input)
+    submit_btn.click(fn=predict, inputs=image_input, outputs=[output_prediction, output_solution])
     
     gr.Markdown(description)
 
+# Launch the app
 interface.launch()