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from huggingface_hub import InferenceClient
import gradio as gr
client = InferenceClient(
"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 generate_from_file(file_path, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
# Read the file content
with open(file_path, 'r', encoding='utf-8') as file:
file_content = file.read()
# You might need to modify this part to fit how you want to use the file content in your prompt
prompt = file_content[:1000] # Example: using first 1000 characters of the file content
return generate(prompt, history, system_prompt, temperature, max_new_tokens, top_p, repetition_penalty)
def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
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(f"{system_prompt}, {prompt}", history)
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield output
return output
iface = gr.Interface(
fn=generate_from_file,
inputs=[gr.File(label="Upload File"), gr.State(), gr.Textbox(label="System Prompt")],
outputs="text",
title="SRT File Translation",
concurrency_limit=20,
)
iface.launch()
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