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import gradio as gr
from huggingface_hub import InferenceClient
import google.generativeai as genai
import numpy as np
import PIL.Image
import io, os
"""
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]],
image,
):
messages = ""
for val in history:
if val[0]:
messages += "{'role': 'user', 'content': "+val[0]+"}"
if val[1]:
messages += "{'role': 'user', 'content': "+val[1]+"}"
messages += "{'role': 'user', 'content': "+message+"}"
print (messages,image)
## for image
if isinstance(image, np.ndarray):
img = PIL.Image.fromarray(image)
else:
try:
img = PIL.Image.open(image)
except (AttributeError, IOError) as e:
return f"Invalid image provided. Please provide a valid image file. Error: {e}"
# Load model
model = genai.GenerativeModel("gemini-pro-vision")
## for image
response = model.generate_content([messages, img])
print (response)
return response
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.Image(show_label=False)
],
additional_inputs_accordion=gr.Accordion(open=True),
theme=gr.themes.Soft()
)
if __name__ == "__main__":
demo.launch() |