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from PIL import Image
import base64
from io import BytesIO
import json
import os
import requests
from typing import Optional
from huggingface_hub import InferenceClient
from transformers import AutoProcessor, Tool
import uuid
import mimetypes
##from dotenv import load_dotenv
##load_dotenv(override=True)
idefics_processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b")
def process_images_and_text(image_path, query, client):
messages = [
{
"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": query},
]
},
]
prompt_with_template = idefics_processor.apply_chat_template(messages, add_generation_prompt=True)
# load images from local directory
# encode images to strings which can be sent to the endpoint
def encode_local_image(image_path):
# load image
image = Image.open(image_path).convert('RGB')
# Convert the image to a base64 string
buffer = BytesIO()
image.save(buffer, format="JPEG") # Use the appropriate format (e.g., JPEG, PNG)
base64_image = base64.b64encode(buffer.getvalue()).decode('utf-8')
# add string formatting required by the endpoint
image_string = f"data:image/jpeg;base64,{base64_image}"
return image_string
image_string = encode_local_image(image_path)
prompt_with_images = prompt_with_template.replace("<image>", " ").format(image_string)
payload = {
"inputs": prompt_with_images,
"parameters": {
"return_full_text": False,
"max_new_tokens": 200,
}
}
return json.loads(client.post(json=payload).decode())[0]
# Function to encode the image
def encode_image(image_path):
if image_path.startswith("http"):
user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0"
request_kwargs = {
"headers": {"User-Agent": user_agent},
"stream": True,
}
# Send a HTTP request to the URL
response = requests.get(image_path, **request_kwargs)
response.raise_for_status()
content_type = response.headers.get("content-type", "")
extension = mimetypes.guess_extension(content_type)
if extension is None:
extension = ".download"
fname = str(uuid.uuid4()) + extension
download_path = os.path.abspath(os.path.join("downloads", fname))
with open(download_path, "wb") as fh:
for chunk in response.iter_content(chunk_size=512):
fh.write(chunk)
image_path = download_path
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}"
}
def resize_image(image_path):
img = Image.open(image_path)
width, height = img.size
img = img.resize((int(width / 2), int(height / 2)))
new_image_path = f"resized_{image_path}"
img.save(new_image_path)
return new_image_path
class VisualQATool(Tool):
name = "visualizer"
description = "A tool that can answer questions about attached images."
inputs = {
"question": {"description": "the question to answer", "type": "text"},
"image_path": {
"description": "The path to the image on which to answer the question",
"type": "text",
},
}
output_type = "text"
client = InferenceClient("HuggingFaceM4/idefics2-8b-chatty")
def forward(self, image_path: str, question: Optional[str] = None) -> str:
add_note = False
if not question:
add_note = True
question = "Please write a detailed caption for this image."
try:
output = process_images_and_text(image_path, question, self.client)
except Exception as e:
print(e)
if "Payload Too Large" in str(e):
new_image_path = resize_image(image_path)
output = process_images_and_text(new_image_path, question, self.client)
if add_note:
output = f"You did not provide a particular question, so here is a detailed caption for the image: {output}"
return output
class VisualQAGPT4Tool(Tool):
name = "visualizer"
description = "A tool that can answer questions about attached images."
inputs = {
"question": {"description": "the question to answer", "type": "text"},
"image_path": {
"description": "The path to the image on which to answer the question. This should be a local path to downloaded image.",
"type": "text",
},
}
output_type = "text"
def forward(self, image_path: str, question: Optional[str] = None) -> str:
add_note = False
if not question:
add_note = True
question = "Please write a detailed caption for this image."
if not isinstance(image_path, str):
raise Exception("You should provide only one string as argument to this tool!")
base64_image = encode_image(image_path)
payload = {
"model": "gpt-4o",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": question
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
"max_tokens": 500
}
response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
try:
output = response.json()['choices'][0]['message']['content']
except Exception:
raise Exception(f"Response format unexpected: {response.json()}")
if add_note:
output = f"You did not provide a particular question, so here is a detailed caption for the image: {output}"
return output
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