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import gradio as gr
from huggingface_hub import InferenceClient
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(
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 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
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
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)",
),
],
)
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load your fine-tuned GPT-2 model from Hugging Face
MODEL_NAME = "hackergeek98/therapist01" # Replace w
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
# Initialize conversation history
conversation_history = ""
# Function to generate responses
def generate_response(user_input):
global conversation_history
# Update conversation history with user input
conversation_history += f"User: {user_input}\n"
# Tokenize the conversation history
inputs = tokenizer(conversation_history, return_tensors="pt", truncation=True, max_length=1024)
# Generate a response from the model
outputs = model.generate(inputs['input_ids'], max_length=1024, num_return_sequences=1, no_repeat_ngram_size=2)
# Decode the model's output
response = 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
# Create Gradio interface
interface = gr.Interface(fn=generate_response,
inputs=gr.Textbox(label="Enter your message", lines=2),
outputs=gr.Textbox(label="Therapist Response", lines=2),
title="Virtual Therapist",
description="A fine-tuned GPT-2 model acting as a virtual therapist. Chat with the model and receive responses as if you are talking to a therapist.")
# Launch the app
interface.launch()
if __name__ == "__main__":
demo.launch()
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