Update app.py

app.py

@@ -1,3 +1,22 @@
+import subprocess
+
+# Define the list of libraries to install
+libraries = [
+    'gradio',
+    'tensorflow',
+    'numpy',
+    'Pillow',
+    'opencv-python-headless',
+    'Flask'  # Add Flask here
+]
+
+# 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
@@ -5,7 +24,7 @@ from PIL import Image
 import io
 
 # Load the pre-trained TensorFlow model
-model = tf.keras.models.load_model("/content/models/imageclassifier.h5")
+model = tf.keras.models.load_model("imageclassifier.h5")
 
 # Define the function to predict the teeth health
 def predict_teeth_health(image):
@@ -24,18 +43,19 @@ def predict_teeth_health(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 prediction result
+    result = {
+        "prediction": "Your Teeth are Good & You Don't Need To Visit Doctor" if probability_good > 0.5 else "Your Teeth are Bad & You Need To Visit Doctor"
+    }
+
+    return result
 
 # 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>",
+    outputs="json",
+    title="<h1 style='color: lightgreen; text-align: center;'>Dentella</h1><p style='text-align: center; color: skyblue; font-size: 30px;'>Please Enter Your Teeth Here...</p>",
 )
 
 # Deploy the Gradio interface using Gradio's hosting service