Update app.py

app.py

@@ -1,3 +1,6 @@
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Force TensorFlow to use CPU
+
 import gradio as gr
 import tensorflow as tf
 import numpy as np
@@ -5,7 +8,6 @@ from tensorflow.keras.preprocessing import image
 from PIL import Image
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
-import os
 
 # Load the trained model
 model = tf.keras.models.load_model("my_keras_model.h5")
@@ -14,8 +16,8 @@ model = tf.keras.models.load_model("my_keras_model.h5")
 def generate_report(name, age, gender, xray1, xray2):
     image_size = (224, 224)
 
-    def predict_fracture(xray):
-        img = Image.open(xray).resize(image_size)
+    def predict_fracture(xray_path):
+        img = Image.open(xray_path).resize(image_size)
         img_array = image.img_to_array(img) / 255.0
         img_array = np.expand_dims(img_array, axis=0)
         prediction = model.predict(img_array)[0][0]
@@ -60,8 +62,8 @@ interface = gr.Interface(
         gr.Textbox(label="Patient Name"),
         gr.Number(label="Age"),
         gr.Radio(["Male", "Female", "Other"], label="Gender"),
-        gr.Image(type="file", label="Upload X-ray Image 1"),
-        gr.Image(type="file", label="Upload X-ray Image 2"),
+        gr.Image(type="filepath", label="Upload X-ray Image 1"),
+        gr.Image(type="filepath", label="Upload X-ray Image 2"),
     ],
     outputs=gr.File(label="Download Report"),
     title="Bone Fracture Detection & Medical Report",