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import torch
import gradio as gr
from PIL import Image
from transformers import AutoProcessor, AutoModel
# Load the model and processor
model_id = "OpenGVLab/InternVL2_5-78B"
device = "cuda" if torch.cuda.is_available() else "cpu"
# Initialize the model and processor
model = AutoModel.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
use_flash_attn=True,
trust_remote_code=True
).eval().to(device)
processor = AutoProcessor.from_pretrained(model_id)
def generate_model_response(image_file, user_query):
"""
Processes the uploaded image and user query to generate a response from the model.
Parameters:
- image_file: The uploaded image file.
- user_query: The user's question about the image.
Returns:
- str: The generated response from the model.
"""
try:
# Load and prepare the image
raw_image = Image.open(image_file).convert("RGB")
# Prepare inputs for the model using the processor
inputs = processor(images=raw_image, text=user_query, return_tensors="pt").to(device)
# Generate response from the model
outputs = model.generate(**inputs)
# Decode and return the response
response_text = processor.decode(outputs[0], skip_special_tokens=True)
return response_text
except Exception as e:
print(f"Error in generating response: {e}")
return f"An error occurred: {str(e)}"
# Gradio Interface
iface = gr.Interface(
fn=generate_model_response,
inputs=[
gr.Image(type="file", label="Upload Image"),
gr.Textbox(label="Enter your question", placeholder="What do you want to know about this image?")
],
outputs="text",
)
iface.launch(share=True)
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