Spaces:
Runtime error
Runtime error
File size: 2,670 Bytes
a35c379 31ab657 a35c379 31ab657 a35c379 31ab657 a35c379 31ab657 a35c379 31ab657 a35c379 31ab657 a35c379 31ab657 a35c379 31ab657 a35c379 31ab657 a35c379 31ab657 a35c379 31ab657 a35c379 31ab657 a35c379 31ab657 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
# Load model & tokenizer
model_id = "sajeewa/empathy-chat-gemma"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto"
)
MAX_TOKENS = 2048
# System prompt
system_prompt = {
"role": "system",
"content": (
"You are an empathetic AI and your friend. Always give lovely caring messages. "
"Understand the user's feelings, then provide a caring response. "
"Talk like a sweet friend using words like 'baby', 'cutie', etc. "
"Use emojis when helpful. Try to continue the conversation in a gentle, emotional tone."
)
}
# Initialize chat history
chat_history = [system_prompt]
# Define a function to generate responses
def respond(user_input, history):
global chat_history
# Add user message
chat_history.append({"role": "user", "content": user_input})
# Token length control
chat_prompt = tokenizer.apply_chat_template(chat_history, tokenize=False)
while len(tokenizer(chat_prompt).input_ids) > MAX_TOKENS:
chat_history.pop(1) # Remove oldest non-system message
chat_prompt = tokenizer.apply_chat_template(chat_history, tokenize=False)
# Prepare model input
inputs = tokenizer(chat_prompt, return_tensors="pt").to(model.device)
# Generate response
output = model.generate(
**inputs,
max_new_tokens=128,
temperature=0.7,
top_p=0.95,
top_k=50,
do_sample=True,
)
response_text = tokenizer.decode(output[0], skip_special_tokens=True)
new_response = response_text[len(chat_prompt):].strip()
# Add assistant's response to history
chat_history.append({"role": "assistant", "content": new_response})
# Show full conversation
history.append((user_input, new_response))
return history, history
# Define reset function
def reset_chat():
global chat_history
chat_history = [system_prompt]
return [], []
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("## 💬 Empathy Chat with Gemma")
chatbot = gr.Chatbot()
with gr.Row():
msg = gr.Textbox(label="Your Message", placeholder="Tell me how you feel...")
with gr.Row():
send = gr.Button("Send")
clear = gr.Button("Reset Chat")
send.click(fn=respond, inputs=[msg, chatbot], outputs=[chatbot, chatbot])
clear.click(fn=reset_chat, outputs=[chatbot, chatbot])
msg.submit(fn=respond, inputs=[msg, chatbot], outputs=[chatbot, chatbot])
# Launch the app
demo.launch()
|