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import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoModelForCausalLM, AutoTokenizer
# Initialize the Zephyr-7B client
zephyr_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Load your fine-tuned GPT-2 model from Hugging Face
MODEL_NAME = "hackergeek98/therapist01" # Replace with your model name
gpt2_tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
gpt2_model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
# Initialize conversation history for GPT-2
conversation_history = ""
# Function to generate responses using Zephyr-7B
def respond_with_zephyr(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in zephyr_client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
# Function to generate responses using GPT-2
def respond_with_gpt2(user_input):
global conversation_history
# Update conversation history with user input
conversation_history += f"User: {user_input}\n"
# Tokenize the conversation history
inputs = gpt2_tokenizer(conversation_history, return_tensors="pt", truncation=True, max_length=1024)
# Generate a response from the model
outputs = gpt2_model.generate(inputs['input_ids'], max_length=1024, num_return_sequences=1, no_repeat_ngram_size=2)
# Decode the model's output
response = gpt2_tokenizer.decode(outputs[0], skip_special_tokens=True)
# Update conversation history with the model's response
conversation_history += f"Therapist: {response}\n"
# Return the therapist's response
return response
# Function to handle the model selection and response generation
def respond(message, history, model_choice, system_message, max_tokens, temperature, top_p):
if model_choice == "Zephyr-7B":
return respond_with_zephyr(message, history, system_message, max_tokens, temperature, top_p)
elif model_choice == "GPT-2 Therapist":
return respond_with_gpt2(message)
else:
return "Invalid model selection."
# Create Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Dropdown(choices=["Zephyr-7B", "GPT-2 Therapist"], label="Model", value="Zephyr-7B"),
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)"),
],
title="Multi-Model Chat Interface",
description="Choose between Zephyr-7B and a fine-tuned GPT-2 model to chat with."
)
# Launch the app
if __name__ == "__main__":
demo.launch() |