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import llama_cpp
import llama_cpp.llama_tokenizer
import gradio as gr
llama = llama_cpp.Llama.from_pretrained(
repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF",
filename="*q8_0.gguf",
tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B"),
verbose=False
)
model = "gpt-3.5-turbo"
def predict(message, history):
messages = []
for user_message, assistant_message in history:
messages.append({"role": "user", "content": user_message})
messages.append({"role": "assistant", "content": assistant_message})
messages.append({"role": "user", "content": message})
response = llama.create_chat_completion_openai_v1(
model=model,
messages=messages,
stream=True
)
text = ""
for chunk in response:
content = chunk.choices[0].delta.content
if content:
text += content
yield text
js = """function () {
gradioURL = window.location.href
if (!gradioURL.endsWith('?__theme=dark')) {
window.location.replace(gradioURL + '?__theme=dark');
}
}"""
css = """
footer {
visibility: hidden;
}
full-height {
height: 100%;
}
"""
with gr.Blocks(theme=gr.themes.Soft(), js=js, css=css, fill_height=True) as demo:
gr.ChatInterface(predict, fill_height=True, examples=["What is the capital of France?", "Who was the first person on the moon?"])
if __name__ == "__main__":
demo.launch()
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