app.py

import os
import gradio as gr

from haystack.components.generators import HuggingFaceTGIGenerator
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack import Pipeline
from haystack.utils import Secret

from image_captioner import ImageCaptioner

description = """
# Captionate 📸
### Create Instagram captions for your pics!

* Upload your photo or select one from the examples
* Choose your model
* ✨ Captionate! ✨ 

It uses [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) model for image2task caption generation task.

For Instagrammable captions, `mistralai/Mistral-7B-Instruct-v0.2` performs best, but try different models to see how they react to the same prompt.

Built by [Bilge Yucel](https://twitter.com/bilgeycl) using [Haystack 2.0](https://github.com/deepset-ai/haystack) 💙
"""

prompt_template = """
You will receive a descriptive text of a photo.
Try to generate a nice Instagram caption with a phrase rhyming with the text. Include emojis in the caption.
                                                                                                                                   
Descriptive text: {{caption}};
Instagram Caption:
"""

hf_api_key = os.environ["HF_API_KEY"]

def generate_caption(image_file_path, model_name):
    image_to_text = ImageCaptioner(
        model_name="Salesforce/blip-image-captioning-base",
    )
    prompt_builder = PromptBuilder(template=prompt_template)
    generator = HuggingFaceTGIGenerator(model=model_name, token=Secret.from_token(hf_api_key), generation_kwargs={"max_new_tokens":50})
    captioning_pipeline = Pipeline()
    captioning_pipeline.add_component("image_to_text", image_to_text)
    captioning_pipeline.add_component("prompt_builder", prompt_builder)
    captioning_pipeline.add_component("generator", generator)
    
    captioning_pipeline.connect("image_to_text.caption", "prompt_builder.caption")
    captioning_pipeline.connect("prompt_builder", "generator")

    result = captioning_pipeline.run({"image_to_text":{"image_file_path":image_file_path}})
    return result["generator"]["replies"][0]

with gr.Blocks(theme="soft") as demo:
    gr.Markdown(value=description)
    with gr.Row():
        image = gr.Image(type="filepath")
        with gr.Column():
            model_name = gr.Dropdown(["mistralai/Mistral-7B-Instruct-v0.2","OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5", "tiiuae/falcon-7b-instruct", "tiiuae/falcon-7b", "HuggingFaceH4/starchat-beta", "bigscience/bloom", "google/flan-t5-xxl"], value="mistralai/Mistral-7B-Instruct-v0.2", label="Choose your model!")
            gr.Examples(["./whale.png", "./rainbow.jpeg", "./selfie.png"], inputs=image, label="Click on any example") 
    submit_btn = gr.Button("✨ Captionate ✨")
    caption = gr.Textbox(label="Caption", show_copy_button=True)
    submit_btn.click(fn=generate_caption, inputs=[image, model_name], outputs=[caption])

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