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from transformers import AutoProcessor, AutoModelForCausalLM, AutoConfig
import gradio as gr
import torch
# # Load the processor and model
processor = AutoProcessor.from_pretrained("microsoft/git-base")
# config = AutoConfig.from_pretrained("./adapter_config.json")
# # model = AutoModelForCausalLM.from_pretrained("microsoft/git-base")
# model_path = "./adapter_model.safetensors"
# model = AutoModelForCausalLM.from_pretrained(model_path)
from transformers import AutoModelForCausalLM
from peft import PeftModel
#Base model on your local filesystem
base_model_dir = "microsoft/git-base"
base_model = AutoModelForCausalLM.from_pretrained(base_model_dir)
#Adaptor directory on your local filesystem
adaptor_dir = "./"
merged_model = PeftModel.from_pretrained(base_model,adaptor_dir)
merged_model = merged_model.merge_and_unload()
merged_model.save_pretrained("./Merged-Model/")
model = merged_model
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
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