Update app.py

app.py

@@ -1,4 +1,10 @@
 import subprocess
+import tensorflow as tf
+import joblib
+import numpy as np
+from PIL import Image
+import io
+from flask import Flask, request, jsonify
 
 # Function to install a package using pip
 def install_package(package):
@@ -7,42 +13,44 @@ def install_package(package):
     except subprocess.CalledProcessError as e:
         print(f"Error installing {package}: {e}")
 
-# Install Flask and TensorFlow using pip
-install_package('Flask')
-install_package('tensorflow')
+# List of libraries to install
+libraries = [
+    'gradio',
+    'tensorflow',
+    'numpy',
+    'Pillow',
+    'opencv-python-headless',
+    'Flask',
+    'joblib'
+]
 
-# Import necessary libraries after installation
-from flask import Flask, request, jsonify
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import io
-
-# Initialize Flask application
-app = Flask(__name__)
+# Install each library using pip
+for library in libraries:
+    install_package(library)
 
 # Load the pre-trained TensorFlow model
-# Load the pre-trained TensorFlow model (assuming it's in the same directory as app.py)
 model = tf.keras.models.load_model("imageclassifier.h5")
 
+# Save the model as .pkl file
+joblib.dump(model, "imageclassifier.pkl")
 
+# Initialize Flask application
+app = Flask(__name__)
+
+# Load the model from .pkl file
+model = joblib.load("imageclassifier.pkl")
 
 # 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)
+    # Convert the PIL image object to a numpy array
+    image = np.array(image)
+    # Perform any necessary preprocessing (resizing, normalization, etc.) here if needed
 
     # Make a prediction
-    prediction = model.predict(image)
+    prediction = model.predict(image.reshape(1, -1))
 
-    # Get the probability of being 'Good'
-    probability_good = prediction[0][0]  # Assuming it's a binary classification
+    # Assuming binary classification, adjust as per your model's output
+    probability_good = prediction[0]  # Assuming it's a binary classification
 
     # Define the prediction result
     result = {