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import gradio as gr
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "thrishala/mental_health_chatbot"
try:
# Load model with int8 quantization for CPU
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cpu",
torch_dtype=torch.float16, # Use float16 for reduced memory
low_cpu_mem_usage=True, # Enable memory optimization
)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Create pipeline with optimizations
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.float16,
)
except Exception as e:
print(f"Error loading model: {e}")
exit()
def respond(
message,
history: list[tuple[str, str]],
system_message, # You can use this for initial instructions
max_tokens,
temperature,
top_p,
):
# 2. Construct the Prompt (Crucial!)
prompt = f"{system_message}\n"
for user_msg, bot_msg in history:
prompt += f"User: {user_msg}\nAssistant: {bot_msg}\n"
prompt += f"User: {message}\nAssistant:"
# 3. Generate with the Pipeline
try:
response = pipe(
prompt,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)[0]["generated_text"]
#Extract the bot's reply (adjust if your model format is different)
bot_response = response.split("Assistant:")[-1].strip()
yield bot_response
except Exception as e:
print(f"Error during generation: {e}")
yield "An error occurred during generation." #Handle generation errors.
# 4. Gradio Interface (No changes needed here)
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(
value="You are a friendly and helpful mental health 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() |