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 image-to-text caption generation task. For Instagrammable captions, `mistralai/Mistral-7B-Instruct-v0.1` 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":100}) 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-v0.1","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-v0.1", 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()