File size: 1,874 Bytes
0ff4a53 b0d7d58 0ff4a53 7372484 0ff4a53 4b15709 0ff4a53 7372484 b0d7d58 9c5bb0c b0d7d58 9c5bb0c b0d7d58 9c5bb0c 0ff4a53 b57258a 0ff4a53 6feb8f6 0ff4a53 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import gradio as gr
from huggingface_hub import InferenceClient
import google.generativeai as genai
"""
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 = [{"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})
print (message,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),
)
if __name__ == "__main__":
demo.launch() |