app.py

from PIL import Image
import requests
import gradio as gr
from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel
import torch
from label import predict_environment,recursion_change_bn,load_labels,hook_feature,returnCAM,returnTF,load_model



git_processor = AutoProcessor.from_pretrained("microsoft/git-large-r-textcaps")
git_model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-r-textcaps")

blip_processor = AutoProcessor.from_pretrained("jaimin/Imagecap")
blip_model = BlipForConditionalGeneration.from_pretrained("jaimin/Imagecap")

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

def generate_caption(processor, model, image, use_float_16=False):
    inputs = processor(images=image, return_tensors="pt").to(device)

    if use_float_16:
        inputs = inputs.to(torch.float16)
    
    generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
   
    return generated_caption

def generate_captions(image):
    
    img = Image.open(image)
    caption_git = generate_caption(git_processor, git_model, img)

    caption_blip = generate_caption(blip_processor, blip_model, img)
    env, scene = predict_environment(img)

    return env,scene,caption_git_large_textcaps, caption_blip_large

outputs = [gr.outputs.Textbox(label="Environment"), gr.outputs.Textbox(label="Objects detected"), gr.outputs.Textbox(label="Caption generated by GIT"), gr.outputs.Textbox(label="Caption generated by BLIP")] 

title = "Image Cap with Scene"
description = " Image caption with scene"

interface = gr.Interface(fn=generate_captions, 
                         inputs=gr.inputs.Image(type="pil"),
                         outputs=outputs,
                         title=title,
                         description=description,
                         enable_queue=True)
interface.launch(debug=True)