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import gradio as gr
from huggingface_hub import InferenceClient
from llama_cpp import Llama
from llama_cpp.llama_chat_format import MoondreamChatHandler
chat_handler = MoondreamChatHandler.from_pretrained(
repo_id="vikhyatk/moondream2",
filename="*mmproj*",
)
llm = Llama.from_pretrained(
repo_id="eybro/model2",
filename="unsloth.Q4_K_M.gguf",
chat_handler=chat_handler,
n_ctx=2048,
)
"""
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")
client = InferenceClient("eybro/model")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Simplified to handle only text input (no image input)
messages = [{"role": "user", "content": message}]
# Use llm to generate the response
response = ""
try:
completion = llm.create_chat_completion(
messages=messages,
)
response = completion['choices'][0]['message']['content']
except Exception as e:
response = f"Error: {e}"
return 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()
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