Spaces:
Runtime error
Runtime error
File size: 1,834 Bytes
321d575 3902745 321d575 3902745 61827ce 321d575 3902745 321d575 3902745 9f62218 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
import gradio as gr
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
# Load the model and processor
model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-72B-Instruct", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
# Define a function to process input and generate a response
def generate_response(image, text):
# Prepare the input
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": text},
],
}
]
# Process the input data
text_data = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text_data],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
# Generate the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return output_text[0]
# Create the Gradio interface
interface = gr.Interface(
fn=generate_response,
inputs=[gr.Image(type="pil", label="Input Image"), gr.Textbox(label="Input Text")],
outputs="text",
title="Qwen2-VL-72B-Instruct",
description="Generate AI responses based on image and text input using Qwen2-VL-72B-Instruct.",
)
# Launch the app
interface.launch() |