Delete app.py

app.py

@@ -1,46 +0,0 @@
-
-from transformers import AutoProcessor, AutoModelForCausalLM
-import gradio as gr
-import torch
-
-# Load the processor and model
-processor = AutoProcessor.from_pretrained("microsoft/git-base")
-model = AutoModelForCausalLM.from_pretrained("./")
-
-def predict(image):
-    try:
-        # Prepare the image using the processor
-        inputs = processor(images=image, return_tensors="pt")
-
-        # Move inputs to the appropriate device
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        inputs = {key: value.to(device) for key, value in inputs.items()}
-        model.to(device)
-
-        # Generate the caption
-        outputs = model.generate(**inputs)
-
-        # Decode the generated caption
-        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)
-
-# https://www.gradio.app/guides
-with gr.Blocks() as demo:
-    image = gr.Image(type="pil")
-    predict_btn = gr.Button("Predict", variant="primary")
-    output = gr.Label(label="Generated Caption")
-
-    inputs = [image]
-    outputs = [output]
-
-    predict_btn.click(predict, inputs=inputs, outputs=outputs)
-
-if __name__ == "__main__":
-    demo.launch()  # Local machine only
-    # demo.launch(server_name="0.0.0.0")  # LAN access to local machine
-    # demo.launch(share=True)  # Public access to local machine