app.py

import os
import gradio as gr
import torch
import PIL

from open_flamingo import create_model_and_transforms

model, image_processor, tokenizer = create_model_and_transforms(
    clip_vision_encoder_path="ViT-L-14",
    clip_vision_encoder_pretrained="openai",
    lang_encoder_path="anas-awadalla/mpt-1b-redpajama-200b",
    tokenizer_path="anas-awadalla/mpt-1b-redpajama-200b",
    cross_attn_every_n_layers=1
)

# grab model checkpoint from huggingface hub
from huggingface_hub import hf_hub_download
import torch

checkpoint_path = hf_hub_download("openflamingo/OpenFlamingo-3B-vitl-mpt1b", "checkpoint.pt")
model.load_state_dict(torch.load(checkpoint_path), strict=False)

from PIL import Image
import requests
import torch

"""
Step 1: Load images
"""
demo_image_one = Image.open(
    requests.get(
        "http://images.cocodataset.org/val2017/000000039769.jpg", stream=True
    ).raw
)

demo_image_two = Image.open(
    requests.get(
        "http://images.cocodataset.org/test-stuff2017/000000028137.jpg",
        stream=True
    ).raw
)

query_image = Image.open(
    requests.get(
        "http://images.cocodataset.org/test-stuff2017/000000028352.jpg", 
        stream=True
    ).raw
)


"""
Step 2: Preprocessing images
Details: For OpenFlamingo, we expect the image to be a torch tensor of shape 
 batch_size x num_media x num_frames x channels x height x width. 
 In this case batch_size = 1, num_media = 3, num_frames = 1,
 channels = 3, height = 224, width = 224.
Step 3: Preprocessing text
Details: In the text we expect an <image> special token to indicate where an image is.
 We also expect an <|endofchunk|> special token to indicate the end of the text 
 portion associated with an image.
tokenizer.padding_side = "left" # For generation padding tokens should be on the left
lang_x = tokenizer(
    ["<image>An image of two cats.<|endofchunk|><image>An image of a bathroom sink.<|endofchunk|><image>An image of"],
    return_tensors="pt",
)
"""

"""
Step 4: Generate text
"""


#print("Generated text: ", tokenizer.decode(generated_text[0]))







def predict_caption(image, prompt):
    assert isinstance(prompt, str)

    
    vision_x = [image_processor(demo_image_one).unsqueeze(0), image_processor(demo_image_two).unsqueeze(0), image_processor(query_image).unsqueeze(0)]
    vision_x = torch.cat(vision_x, dim=0)
    vision_x = vision_x.unsqueeze(1).unsqueeze(0)
    
    
    tokenizer.padding_side = "left" # For generation padding tokens should be on the left
    lang_x = tokenizer(
        ["<image>An image of two cats.<|endofchunk|><image>An image of a bathroom sink.<|endofchunk|><image>An image of"],
        return_tensors="pt",
    )
    
    tokenizer.padding_side = "left" # For generation padding tokens should be on the left
    lang_x = tokenizer(
        ["<image>An image of two cats.<|endofchunk|><image>An image of a bathroom sink.<|endofchunk|><image>An image of"],
        return_tensors="pt",
    )

    caption = tokenizer.decode(generated_text[0])
    
    return caption


iface = gr.Interface(fn=predict_caption, 
        inputs=[gr.Image(type="pil"), gr.Textbox(value=DEFAULT_PROMPT, label="Prompt")], 
        examples=examples,
        outputs="text")

iface.launch()