Upload 101234444_aml_assignment_1.py

101234444_aml_assignment_1.py

@@ -0,0 +1,70 @@
+# -*- coding: utf-8 -*-
+"""101234444_aml_assignment_1.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1GBU5kKqfnliMP-lElZZ4VgVgcsyqy1wQ
+"""
+
+!pip install --upgrade typing_extensions
+!pip install --upgrade fastapi
+!pip uninstall -y gradio
+!pip install gradio
+
+import requests
+import tensorflow as tf
+import PIL.Image
+import numpy as np
+import json
+import gradio as gr
+
+# Download the final_model.h5 file
+url_model = "https://huggingface.co/ImanAmran/ml_assignment_1/resolve/main/final_model.h5"
+response_model = requests.get(url_model)
+with open("final_model.h5", "wb") as f_model:
+    f_model.write(response_model.content)
+
+# Download the class_indices.json file
+url_indices = "https://huggingface.co/ImanAmran/ml_assignment_1/resolve/main/class_indices.json"
+response_indices = requests.get(url_indices)
+class_indices = response_indices.json()  # Parse the JSON response
+
+# Load the model
+model = tf.keras.models.load_model("final_model.h5")
+
+# Reverse the key-value pairs in the class_indices dictionary
+index_to_class = {v: k for k, v in class_indices.items()}
+
+def classify_image(image: PIL.Image.Image):
+    try:
+        # Ensure the input is a PIL Image, resize it, and then convert it to a NumPy array
+        if not isinstance(image, PIL.Image.Image):
+            image = PIL.Image.fromarray(image)
+        image_resized = image.resize((375, 375))
+        image_array = np.array(image_resized)
+        image_array = np.expand_dims(image_array, axis=0)  # Add a batch dimension
+
+        # Preprocess the image array in the same way as your manual prediction function
+        img_preprocessed = tf.keras.applications.resnet50.preprocess_input(image_array)
+
+        # Perform inference
+        predictions = model.predict(img_preprocessed)
+        predicted_class_idx = np.argmax(predictions)  # Get the predicted class index
+
+        # Map index to label using index_to_class
+        predicted_class_label = index_to_class[predicted_class_idx]
+        return predicted_class_label
+
+    except Exception as e:
+        return str(e)  # Return the exception message to help identify the issue
+
+# Create a Gradio Interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.components.Image(),
+    outputs=gr.components.Textbox(),
+    live=True,  # This line is optional, it enables real-time feedback but may slow down performance
+    share=True  # This line allows Gradio to be run in this Colab notebook
+)
+#iface.launch()