Create app.py

app.py

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+from transformers import AutoProcessor, AutoModelForCausalLM
+import gradio as gr
+import torch
+
+processor = AutoProcessor.from_pretrained('microsoft/git-base')
+model = AutoModelForCausalLM.from_pretrained('./')
+
+def predict(image):
+    try:
+        inputs = processor(images=image, return_tensors="pt")
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        inputs = {key: value.to(device) for key, value in inputs.items()}
+        model.to(device)
+
+        outputs = model.generate(**inputs)
+
+        caption = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+        
+        return caption
+
+    except Exception as e:
+        print("Error during prediction:", str(e))
+        return "Error: " + str(e)
+
+with gr.Blocks() as demo:
+    image = gr.Image(type="pil")
+    predict_btn = gr.Button("Predict", variant="primary")
+    output = gr.Textbox(label="Generated Caption")
+
+    inputs = [image]
+    outputs = [output]
+    
+    predict_btn.click(predict, inputs=inputs, outputs=outputs)
+
+if __name__ == "__main__":
+    demo.launch()