Create app.py

app.py

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+# Importing some modules
+import gradio as gr
+from transformers import pipeline
+
+# Loading in the model
+MODEL_AGE = pipeline('image-classification', model='nateraw/vit-age-classifier', device=-1)
+MODEL_EMOTION = pipeline('image-classification', model='dennisjooo/emotion_classification', device=-1)
+
+def classify_image(image, top_k):
+    # Getting the classification result
+    age_result = MODEL_AGE(image)
+    emotion_result = MODEL_EMOTION(image)
+
+    # Reformating the classification result into a dictionary
+    age_result = {result['label']: result['score'] for result in age_result[:min(int(top_k), 8)]}
+    emotion_result = {result['label']: result['score'] for result in emotion_result[:min(int(top_k), 7)]}
+
+    # Add some text comment to it lol
+    comment = text_comment(list(age_result.keys())[0])
+
+    # Returning the classification result
+    return age_result, comment, emotion_result
+
+# Snarky comment based on age
+def text_comment(pred_class):
+    match pred_class:
+        case "3-9":
+            return "Lost your way to the playground?"
+        case "10-19":
+            return "But Mom, I'm not a kid anymore!"
+        case "20-29":
+            return "You're in your prime!"
+        case "30-39":
+            return "Oof, watch out for those wrinkles!"
+        case "40-49":
+            return "You're still young at heart!"
+        case "50-59":
+            return "Retirement is just around the corner!"
+        case "60-69":
+            return "You're a senior citizen now!"
+        case "more than 70":
+            return "Hey Siri, play 'My Way' by Frank Sinatra"
+        
+
+if __name__ == "__main__":
+    # Definining the title of the interface
+    title_text = """
+        # I will guess your age and mood based on your picture!
+        ---
+        Totally not creepy, I promise :)
+        <br>Made by [Dennis Jonathan](dennisjooo.github.io). A project for REA Mastering AI course.
+        Age guessing model from [nateraw/vit-age-classifier](https://huggingface.co/nateraw/vit-age-classifier)
+        <br>Mood-guessing model is a [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
+        trained on [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis)
+    """
+
+    # Creating the Gradio interface
+    with gr.Blocks() as demo:
+        gr.Markdown(title_text)
+        with gr.Row(equal_height=True):
+            with gr.Column():
+                # Creating the input block
+                image = gr.Image(label="Upload a picture of yourself", type="pil", scale=2)
+
+                # Creating the example block
+                gr.Examples(examples=[
+                    "./images/andrew.jpg",
+                    "./images/feifei.jpg",
+                    "./images/geoff.jpg",
+                    "./images/ilya.jpg",
+                    "./images/karpathy.jpg",
+                    "./images/lex.jpg"
+                    ], inputs=[image], label="Or choose an example")
+
+
+            with gr.Column():
+                # Getting the top k hyperparameter
+                top_k = gr.Number(label="How many guesses do I get?", value=1)
+
+                # Creating the output block
+                age_label = gr.Label(label="Hey it's me, your age!")
+                comment = gr.Textbox(label="Based on your age, I think you are...",
+                                    placeholder="I'm still learning, so I might be wrong!")
+                emotion_label = gr.Label(label="Hey it's me, your emotion!")
+
+        with gr.Row():
+            # Submit button
+            btn = gr.Button("Beep boop, guess my age and emotion!")
+            btn.click(classify_image, inputs=[image, top_k], outputs=[age_label, comment, emotion_label])
+
+            # Clear button
+            clear = gr.Button("Poof begone!")
+            clear.click(lambda: [None, None, None, None], inputs=[], outputs=[image, age_label, comment, emotion_label])
+
+    # Launching the interface
+    demo.launch(share=True, debug=True)