import os
import gradio as gr

from haystack.nodes import TransformersImageToText
from haystack.nodes import PromptNode, PromptTemplate
from haystack import Pipeline

image_to_text = TransformersImageToText(
  model_name_or_path="nlpconnect/vit-gpt2-image-captioning",
  use_gpu=True,
  batch_size=16,
  progress_bar=True
)

prompt_template = PromptTemplate(prompt="""
You will receive a describing text of a photo.
Try to come up with a nice Instagram caption that has a phrase rhyming with the text

Describing text:{documents};
Caption:
""")

# prompt_node = PromptNode(model_name_or_path="gpt-3.5-turbo", api_key=api_key, default_prompt_template=pt)
hf_api_key = os.environ["HF_API_KEY"]
# prompt_node = PromptNode(model_name_or_path="google/flan-t5-large", default_prompt_template=prompt_template)
prompt_node = PromptNode(model_name_or_path="tiiuae/falcon-7b-instruct", api_key=hf_api_key, default_prompt_template=prompt_template, model_kwargs={"trust_remote_code":True})

captioning_pipeline = Pipeline()
captioning_pipeline.add_node(component=image_to_text, name="image_to_text", inputs=["File"])
captioning_pipeline.add_node(component=prompt_node, name="prompt_node", inputs=["image_to_text"])

def generate_caption(image_file_paths):
    print(image_file_paths)
    # documents = image_to_text.generate_captions(image_file_paths=[image_file_paths])
    # print(documents[0].content)
    caption = captioning_pipeline.run(file_paths=[image_file_paths])
    print(caption)
    return caption["results"][0]

with gr.Blocks(theme="soft") as demo:
    image = gr.Image(type="filepath")
    submit_btn = gr.Button("✨ Captionate ✨")
    caption = gr.Textbox(label="Caption")
    submit_btn.click(fn=generate_caption, inputs=[image], outputs=[caption])

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