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import os
import gradio as gr
# ดึงค่า API Key จาก Hugging Face Secrets
hf_api_key = os.environ.get("HF_API_KEY")
# ตรวจสอบว่า API Key ถูกดึงออกมาหรือยัง
if hf_api_key:
print(f"API Key Loaded: {hf_api_key}")
else:
print("API Key not found. Please check your Hugging Face secrets.")
from haystack.components.generators.chat import HuggingFaceAPIChatGenerator
from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
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, try different text-to-text models to see how they react to the same prompt.
Built by [Bilge Yucel](https://twitter.com/bilgeycl) using [Haystack](https://github.com/deepset-ai/haystack) 💙
"""
prompt_template =[ChatMessage.from_user("""
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.
Just return one option without alternatives. Don't use hashtags.
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 = ChatPromptBuilder(template=prompt_template, required_variables="*")
generator = HuggingFaceAPIChatGenerator(
api_type="serverless_inference_api",
api_params={"model": model_name},
token=Secret.from_token(hf_api_key))
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].text
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(
["deepseek-ai/DeepSeek-V3.1-Terminus", "meta-llama/Llama-3.3-70B-Instruct", "openai/gpt-oss-20b", "Qwen/Qwen3-4B-Instruct-2507"],
value="deepseek-ai/DeepSeek-V3.1-Terminus",
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() |