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from huggingface_hub import InferenceClient
import gradio as gr
client = InferenceClient(model="mistralai/Mixtral-8x7B-Instruct-v0.1")
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def should_stop_generation(output, stop_patterns):
for pattern in stop_patterns:
if pattern in output:
return True
return False
async def generate(
prompt, history, temperature=0.7, max_new_tokens=1024, top_p=0.95, repetition_penalty=1.0,
stop_patterns=None, max_loops=5
):
if stop_patterns is None:
stop_patterns = ["\n\n", ".", "The end", "Thank you"]
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
formatted_prompt = format_prompt(prompt, history)
output = ""
loop_count = 0
while loop_count < max_loops:
try:
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
async for response in stream:
output += response.token.text
yield output
if should_stop_generation(output, stop_patterns):
return # Stop if end pattern is detected
loop_count += 1 # Increment loop count to avoid infinite loops
# If the text isn't complete, use the last segment as a new prompt
formatted_prompt = format_prompt(output.split("\n")[-1], history)
except Exception as e:
print(f"Error during streaming: {e}")
break
# Non-streaming fallback
try:
response = client.text_generation(formatted_prompt, **generate_kwargs, stream=False, return_full_text=False)
output = response # Use response as a string if streaming failed
if not should_stop_generation(output, stop_patterns):
output += " [Additional text required to complete the response.]"
yield output
except Exception as e:
print(f"Error during non-streaming generation: {e}")
mychatbot = gr.Chatbot(
avatar_images=["./user.png", "./botm.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,
)
demo = gr.ChatInterface(fn=generate,
chatbot=mychatbot,
title="Mixtral 8x7b AI Chatbot By wifix199",
retry_btn=None,
undo_btn=None
)
demo.queue().launch(show_api=False)
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