Create app.py

app.py

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+import subprocess
+
+# Define the list of libraries to install
+libraries = [
+    'gradio',
+    'tensorflow',
+    'numpy',
+    'Pillow',
+    'opencv-python-headless',  # 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
+model = tf.keras.models.load_model("imageclassifier.h5")
+
+# 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.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)