app.py

import requests
from PIL import Image
from transformers import AutoProcessor, Blip2ForConditionalGeneration
import torch
import gradio as gr

processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# def predict(inp):
#   inp = transforms.ToTensor()(inp).unsqueeze(0)
#   with torch.no_grad():
#     prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
#     confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
#       return confidences

# demo = gr.Interface(fn=predict, 
#              inputs=gr.inputs.Image(type="pil"),
#              outputs=gr.outputs.Label(num_top_classes=3)
#              )
             
def predict(imageurl):
    image = Image.open(requests.get(imageurl, stream=True).raw).convert('RGB')
    inputs = processor(image, return_tensors="pt")
    generated_ids = model.generate(**inputs, max_new_tokens=20)
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
    return('caption: '+generated_text)
    
demo = gr.Interface(fn=predict, 
     inputs="text",
     outputs=gr.outputs.Label(num_top_classes=3)
     )   

demo.launch()