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import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import os
# Replace 'your_huggingface_token' with your actual Hugging Face access token
access_token = os.getenv('token')
# Initialize the tokenizer and model with the Hugging Face access token
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it", use_auth_token=access_token)
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b-it",
torch_dtype=torch.bfloat16,
use_auth_token=access_token
)
model.eval() # Set the model to evaluation mode
# Initialize the inference client (if needed for other API-based tasks)
client = InferenceClient(token=access_token)
# Import required modules for E2-F5-TTS
from huggingface_hub import Client
# Initialize the E2-F5-TTS client
client_tts = Client("mrfakename/E2-F5-TTS")
def text_to_speech(text, sample):
result = client_tts.predict(
ref_audio_input=handle_file(f'input/{sample}.mp3'),
ref_text_input="",
gen_text_input=text,
remove_silence=False,
cross_fade_duration_slider=0.15,
speed_slider=1,
api_name="/basic_tts"
)
audio_file = open(result[0], "rb")
audio_bytes = audio_file.read()
return audio_bytes
def conversation_predict(input_text):
"""Generate a response for single-turn input using the model."""
# Tokenize the input text
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
# Generate a response with the model
outputs = model.generate(input_ids, max_new_tokens=2048)
# Decode and return the generated response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Convert the text response to speech using E2-F5-TTS
audio_bytes = text_to_speech(response, sample="input")
return response, audio_bytes
def respond(
message: str,
history: list[tuple[str, str]],
system_message: str,
max_tokens: int,
temperature: float,
top_p: float,
):
"""Generate a response for a multi-turn chat conversation."""
# Prepare the messages in the correct format for the API
messages = [{"role": "system", "content": system_message}]
for user_input, assistant_reply in history:
if user_input:
messages.append({"role": "user", "content": user_input})
if assistant_reply:
messages.append({"role": "assistant", "content": assistant_reply})
messages.append({"role": "user", "content": message})
response = ""
# Stream response tokens from the chat completion API
for message_chunk in client.chat_completion(
messages=messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message_chunk["choices"][0]["delta"].get("content", "")
response += token
yield response
# Create a Gradio ChatInterface demo
demo = gr.ChatInterface(
fn=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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