| import gradio as gr | |
| #from transformers import AutoModelForCausalLM, AutoProcessor | |
| # Load the model and processor | |
| model_id = "microsoft/Phi-3-vision-128k-instruct" | |
| #model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto", _attn_implementation='flash_attention_2') | |
| #processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) | |
| # Define the function to generate text | |
| def generate_text(image, prompt): | |
| # Process the input | |
| inputs = "" | |
| # Generate the text | |
| generation_args = { | |
| "max_new_tokens": 500, | |
| "temperature": 0.0, | |
| "do_sample": False, | |
| } | |
| return image + prompt | |
| # Create the Gradio application | |
| gr.Interface( | |
| fn=generate_text, | |
| inputs=[ | |
| gr.Image(type="pil"), | |
| gr.Textbox(label="Prompt") | |
| ], | |
| outputs=gr.Textbox(), | |
| title="Phi-3-Vision Model", | |
| description="Generate text based on an image and prompt using the Phi-3-Vision model." | |
| ).launch(share=True,show_error=True) |