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pip install openai
pip install -q gradio
import os
import openai
from getpass import getpass
openai.api_key = getpass("sk- ")
import base64
# Function to encode the image
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
"""# Image Captioning AI App with Gradio"""
def caption_image(image_path):
encoded_image = encode_image(image_path)
result = openai.chat.completions.create(
model = "gpt-4-vision-preview",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Describe the image. Additionally outline from image the caption objects and items with the attribute recognition. If there are OCR captions, also extract this data from the image.Outline the image description like a plain paragraph text with few sentences.Additional outline in bullets points the KEY ELEMENTS if there is present the:"},
{"type": "text", "text": "-[item] caption\n-[object] detection\n-[attribute] recognition\n-[image] segmentation\n-[OCR] text and number extraction\n-[adjective] semantic search\n-[subject] from the image\n-[doing action]\n-[mobility]\n-[transportation]\n-[person]\n-[animal]\n-[furniture]\n-[electronics]\n-[house appliances]\n-[landscape]\n-[environment]\n-[liability] context search\n Outline only the captioned KEY ELEMENTS categories as bullet points."},
{"type": "image_url",
"image_url": f"data:image/jpeg;base64,{encoded_image}"},
]
},
],
max_tokens=600,
)
return result.choices[0].message.content
import gradio as gr
import os
demo = gr.Interface(
caption_image,
gr.Image(type="filepath"),
"text",
live=True,
css = """.gradio-container {background-color: cyan;}.btn-primary {background-color: cyan;color: white;border: 2px solid white;}"""
)
if __name__ == "__main__":
demo.launch()
git clone https://huggingface.co/spaces/designfailure/Insur.Co
git add app.py
git commit -m "Add application file"
git push |