Create app.py

app.py

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+import gradio as gr
+from transformers import AutoProcessor, AutoModelForCausalLM
+import torch
+from PIL import Image
+
+from caption import predict_step
+
+with gr.Blocks() as demo:
+    image = gr.Image(type='pil', label='Image')
+    label = gr.Text(label='Generated Caption')
+    image.upload(
+        predict_step,
+        [image],
+        [label]
+    )
+
+model = AutoModelForCausalLM.from_pretrained("Chesscorner/git-chess-v3")
+processor = AutoProcessor.from_pretrained("Chesscorner/git-chess-v3")
+
+# Set up device and move model to it
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Enable mixed precision if on GPU
+use_fp16 = device.type == "cuda"
+if use_fp16:
+    model.half()
+
+# Set generation parameters
+gen_kwargs = {'max_length': 100, 'num_beams': 2}  # Adjust num_beams if needed
+
+
+# Prediction function
+def predict_step(image):
+    # Preprocess the image
+    pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(device)
+
+    # Generate predictions with no_grad for efficiency
+    with torch.no_grad():
+        output_ids = model.generate(pixel_values=pixel_values, **gen_kwargs)
+
+    # Decode predictions
+    preds = processor.batch_decode(output_ids, skip_special_tokens=True)
+    return preds[0].strip()
+
+if __name__ == '__main__':
+    demo.launch(share=True)