Build initial app.py code

app.py

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+import gradio as gr
+from transformers import TFAutoModel
+
+PATH = "./4-15_ViT-B16_Lung3_v1"
+model = TFAutoModel.from_pretrained(PATH)
+tokenizer = AutoTokenizer.from_pretrained(PATH)
+
+
+# Define preprocessing function
+# def preprocess_image(image):
+#     # Add preprocessing steps here (e.g., resizing, normalization)
+#     # Return preprocessed image
+#     return image
+
+# Define inference function
+def predict(preprocessed_image):
+    # Preprocess the image
+    # preprocessed_image = preprocess_image(image)
+
+    # Convert preprocessed image to input format expected by the model
+    inputs = tokenizer(preprocessed_image, return_tensors="tf")
+
+    # Perform model inference
+    outputs = model(**inputs)
+    
+    # Process model outputs as needed
+    # For example, extract predicted labels
+    predicted_labels = np.argmax(outputs.logits.numpy(), axis=1)
+
+    # Return predicted labels
+    return predicted_labels
+
+image_component = gr.Image(type="numpy", label="Chest X-ray Image")
+label_component = gr.Label(label="Classification result", num_top_classes=1)
+description = ""
+title = "Pneumonia Classifier 🐣"
+
+demo = gr.Interface(fn=predict, inputs=image_component, outputs=label_component, description=description, title=title)
+
+demo.launch()