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# import gradio as gr
# from huggingface_hub import InferenceClient
# """
# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
# """
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# def respond(
# message,
# history: list[tuple[str, str]],
# system_message,
# max_tokens,
# temperature,
# top_p,
# ):
# messages = [{"role": "system", "content": system_message}]
# for val in history:
# if val[0]:
# messages.append({"role": "user", "content": val[0]})
# if val[1]:
# messages.append({"role": "assistant", "content": val[1]})
# messages.append({"role": "user", "content": message})
# response = ""
# for message in client.chat_completion(
# messages,
# max_tokens=max_tokens,
# stream=True,
# temperature=temperature,
# top_p=top_p,
# ):
# token = message.choices[0].delta.content
# response += token
# yield response
# """
# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
# """
# demo = gr.ChatInterface(
# respond,
# additional_inputs=[
# gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
# gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
# gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
# gr.Slider(
# minimum=0.1,
# maximum=1.0,
# value=0.95,
# step=0.05,
# label="Top-p (nucleus sampling)",
# ),
# ],
# )
# if __name__ == "__main__":
# demo.launch()
from PIL import Image
import base64
import io
import gradio as gr
from huggingface_hub import InferenceClient
import requests
# Function to convert image to base64
def image_to_base64(image: Image):
buffered = io.BytesIO()
image.save(buffered, format="PNG")
return base64.b64encode(buffered.getvalue()).decode("utf-8")
# Function to respond to input
def respond(message: str, image: Image):
# Convert image to base64
image_base64 = image_to_base64(image)
# Initialize the Hugging Face client
client = InferenceClient("your_huggingface_model")
try:
# Call the text-to-image method
response_data = client.text_to_image(images=image_base64, prompt=message)
# Convert the response data (image) into a PIL Image
image_response = Image.open(io.BytesIO(response_data))
# Format the response in the required 'messages' format
response_message = {
'role': 'assistant', # Assuming the response is from the assistant
'content': image_response
}
return response_message
except Exception as e:
return {"role": "assistant", "content": str(e)}
# Define the Gradio interface
def create_interface():
with gr.Blocks() as demo:
chatbot = gr.Chatbot(type='messages') # 'messages' format for chatbot
message_input = gr.Textbox()
image_input = gr.Image(type='pil') # Image input as PIL image
# Define the interaction
message_input.submit(respond, inputs=[message_input, image_input], outputs=[chatbot])
return demo
# Launch the interface
if __name__ == "__main__":
create_interface().launch(share=True) # Set share=True if you want to share the link publicly
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