Update app.py

app.py

@@ -3,32 +3,61 @@ import tensorflow as tf
 import numpy as np
 from PIL import Image
 
-# ---------------- LOAD PRE-TRAINED MODEL ---------------- #
-model = tf.keras.models.load_model("chest_xray_model.h5")
+# ---------------- LOAD TRAINED MODEL ---------------- #
+model = tf.keras.models.load_model("chest_xray_model.h5")  # Make sure this file is uploaded
 class_labels = ["Normal", "Pneumonia"]
 
 # ---------------- PREDICTION FUNCTION ---------------- #
-def predict_xrays(images):
-    results = []
-    for img in images:
-        img = img.resize((224, 224))
-        img_array = np.array(img) / 255.0
-        img_array = np.expand_dims(img_array, axis=0)
-        prediction = model.predict(img_array, verbose=0)[0][0]
-        label = class_labels[int(prediction > 0.5)]
-        confidence = prediction if prediction > 0.5 else 1 - prediction
-        results.append(f"Prediction: {label} ({confidence*100:.2f}% confidence)")
-    return results
+def predict_xray(img):
+    # Preprocess
+    img = img.resize((224, 224))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    # Model prediction
+    prediction = model.predict(img_array, verbose=0)[0][0]
+    label = class_labels[int(prediction > 0.5)]
+    confidence = prediction if prediction > 0.5 else 1 - prediction
+    
+    # Detailed preliminary radiology report and first aid
+    if label == "Pneumonia":
+        report = (
+            "Preliminary Radiology Report:\n"
+            "- The chest X-ray shows opacities or infiltrates consistent with pneumonia.\n"
+            "- Findings suggest possible lung inflammation or infection.\n"
+            "- Further diagnostic tests (blood tests, sputum culture, oxygen saturation) recommended.\n\n"
+            "First Aid / Immediate Actions:\n"
+            "1. Seek medical attention immediately for confirmation and treatment.\n"
+            "2. Monitor for severe symptoms: high fever, shortness of breath, chest pain, confusion.\n"
+            "3. Ensure hydration and rest.\n"
+            "4. Avoid self-medicating with antibiotics without doctor supervision.\n"
+            "5. Use a mask and maintain good hygiene to prevent spread if contagious.\n"
+            "6. Keep a pulse oximeter if available; seek emergency care if oxygen saturation < 94%.\n"
+            "7. Note any worsening symptoms and report them to healthcare providers promptly.\n"
+        )
+    else:
+        report = (
+            "Preliminary Radiology Report:\n"
+            "- No visible signs of pneumonia detected on this X-ray.\n"
+            "- Lungs appear clear, but clinical correlation is advised.\n\n"
+            "General Advice:\n"
+            "1. Maintain healthy habits: good hydration, nutrition, and regular exercise.\n"
+            "2. Seek medical attention if respiratory symptoms develop.\n"
+            "3. Continue monitoring for cough, fever, or shortness of breath.\n"
+        )
+    
+    return f"Prediction: {label} ({confidence*100:.2f}% confidence)\n\n{report}"
 
 # ---------------- GRADIO INTERFACE ---------------- #
 interface = gr.Interface(
-    fn=predict_xrays,
-    inputs=gr.Gallery(label="Upload X-ray Images", type="pil"),  # removed .style()
-    outputs=gr.Textbox(label="Predictions"),
+    fn=predict_xray,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
     title="Chest X-Ray Pneumonia Classifier",
-    description="Upload multiple X-ray images to get predictions at once."
+    description="Upload a chest X-ray to get a detailed preliminary report and first-aid recommendations."
 )
 
+# Launch the app
 if __name__ == "__main__":
     interface.launch()