app.py

import torch 
import re 
import gradio as gr
from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel
'''
device='cpu'
encoder_checkpoint = "Thibalte/captionning_project"
decoder_checkpoint = "Thibalte/captionning_project"
model_checkpoint = "Thibalte/captionning_project"
feature_extractor= ViTImageProcessor.from_pretrained(model_path)
feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
'''
# Load the trained model
model_path = "./image-captioning-output"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path)

#Load ImageProcessor
feature_extractor= ViTImageProcessor.from_pretrained(model_path)

# Load model
model = VisionEncoderDecoderModel.from_pretrained(model_path)
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
# use GPT2's eos_token as the pad as well as eos token


# generation




print(captions)


def predict(image,max_length=24, num_beams=4):
    image = image.convert('RGB')
    sequences = model.generate(pixel_values, num_beams=4, max_length=25)
    sequences = model.generate(pixel_values, num_beams=4, max_length=25)
    captions = tokenizer.batch_decode(sequences, skip_special_tokens=True)
  return caption


# Gradio Interface
gradio_app = gr.Interface(
    fn=predict,
    inputs=gr.Image(label="Select image for captioning", sources=['upload', 'webcam'], type="pil"),
    outputs=[gr.Textbox(label="Image Caption")],
    examples = [f"example{i}.jpg" for i in range(1,7)],
    title="Image Captioning with our model",
)

if __name__ == "__main__":
    gradio_app.launch()