app.py

import gradio as gr
from PIL import Image
import requests
import base64
import io
import openai
openai.api_key = ""
openai.api_base = "https://api.deepinfra.com/v1/openai"
def todataimage(file, ext):
    buffered = io.BytesIO()
    file.save(buffered, format=ext)
    return "data:image/png;base64,"+base64.b64encode(buffered.getvalue()).decode("utf-8")
def caption(file, ext):
  datimg = todataimage(file, ext)
  response = requests.post("https://russellc-comparing-captioning-models.hf.space/run/predict", json={
    "data": [
      datimg,
  ]}).json()
  print(response)
  data = response["data"]
  chat_completion = openai.ChatCompletion.create(
    model="meta-llama/Llama-2-70b-chat-hf",
    messages=[{"role": "system", "content": "you will be given descriptions of one image from a varity of image captioning models with a varity of quality, what you need to do is combine them into one image caption and make that be your output, no extras words like \"here is your output\", remeber, don't take too much information from low quality, or too little from high. do NOT contain ANY text other than the description"},{"role":"user", "content":"High Quality:\n"+data[1]+"\n"+data[3]+"\nMedium Qualitt:\n"+data[2]+"\nLow Quality\n"+data[0]}],
)

  return chat_completion.choices[0].message.content
def image_predict(image):
    return caption(image, "png")

iface = gr.Interface(image_predict, inputs=gr.Image(type="pil"), outputs="label", flagging_options=[])
iface.launch()