Update app.py

app.py

@@ -1,61 +1,61 @@
-import subprocess
-
-# Define the list of libraries to install with specific versions
-libraries = [
-    'gradio==2.3.0',
-    'tensorflow==2.7.0',
-    'numpy==1.19.5',
-    'Pillow==8.4.0',
-    'opencv-python-headless==4.5.3.56',  # This installs OpenCV without GUI support
-]
-
-# Install each library using pip
-for library in libraries:
-    try:
-        subprocess.check_call(['pip', 'install', library])
-    except subprocess.CalledProcessError as e:
-        print(f"Error installing {library}: {e}")
-
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import io
-
-# Load the pre-trained TensorFlow model from the specified path
-model_path = r"C:\Users\Administrator\Desktop\proj\imageclassifier.keras"
-model = tf.keras.models.load_model(model_path)
-
-# Define the function to predict the teeth health
-def predict_teeth_health(image):
-    # Convert the PIL image object to a file-like object
-    image_bytes = io.BytesIO()
-    image.save(image_bytes, format="JPEG")
-
-    # Load the image from the file-like object
-    image = tf.keras.preprocessing.image.load_img(image_bytes, target_size=(256, 256))
-    image = tf.keras.preprocessing.image.img_to_array(image)
-    image = np.expand_dims(image, axis=0)
-
-    # Make a prediction
-    prediction = model.predict(image)
-
-    # Get the probability of being 'Good'
-    probability_good = prediction[0][0]  # Assuming it's a binary classification
-
-    # Return the predicted class name
-    if probability_good > 0.5:
-        return f"Predicted: Your Teeth are Good And You Don't Need To Visit Doctor"
-    else:
-        return f"Predicted: Your Teeth are Bad And You Need To Visit Doctor"
-
-# Define the Gradio interface
-iface = gr.Interface(
-    fn=predict_teeth_health,
-    inputs=gr.inputs.Image(type="pil"),
-    outputs="text",
-    title="<h1 style='color: lightgreen; text-align: center;'>Dentella</h1>",
-)
-
-# Deploy the Gradio interface using Gradio's hosting service
-iface.launch(share=True)
+import subprocess
+
+# Define the list of libraries to install with specific versions
+libraries = [
+    'gradio==2.3.0',                # You can update to a more recent version if needed
+    'tensorflow==2.12.0',           # Use a more recent version available on your platform
+    'numpy==1.21.0',                # Compatible with TensorFlow 2.12.0
+    'Pillow==8.4.0',
+    'opencv-python-headless==4.5.3.56',  # This installs OpenCV without GUI support
+]
+
+# Install each library using pip
+for library in libraries:
+    try:
+        subprocess.check_call(['pip', 'install', library])
+    except subprocess.CalledProcessError as e:
+        print(f"Error installing {library}: {e}")
+
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+
+# Load the pre-trained TensorFlow model from the specified path
+model_path = r"C:\Users\Administrator\Desktop\proj\imageclassifier.keras"
+model = tf.keras.models.load_model(model_path)
+
+# Define the function to predict the teeth health
+def predict_teeth_health(image):
+    # Convert the PIL image object to a file-like object
+    image_bytes = io.BytesIO()
+    image.save(image_bytes, format="JPEG")
+
+    # Load the image from the file-like object
+    image = tf.keras.preprocessing.image.load_img(image_bytes, target_size=(256, 256))
+    image = tf.keras.preprocessing.image.img_to_array(image)
+    image = np.expand_dims(image, axis=0)
+
+    # Make a prediction
+    prediction = model.predict(image)
+
+    # Get the probability of being 'Good'
+    probability_good = prediction[0][0]  # Assuming it's a binary classification
+
+    # Return the predicted class name
+    if probability_good > 0.5:
+        return f"Predicted: Your Teeth are Good And You Don't Need To Visit Doctor"
+    else:
+        return f"Predicted: Your Teeth are Bad And You Need To Visit Doctor"
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_teeth_health,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs="text",
+    title="<h1 style='color: lightgreen; text-align: center;'>Dentella</h1>",
+)
+
+# Deploy the Gradio interface using Gradio's hosting service
+iface.launch(share=True)