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from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
import torch
# Load pre-trained model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("ahmed792002/alzheimers_memory_support_ai")
model = AutoModelForCausalLM.from_pretrained("ahmed792002/alzheimers_memory_support_ai")
# Chatbot function
def chatbot(query, history, system_message, max_length, temperature, top_k, top_p):
"""
Processes a user query through the specified model to generate a response.
"""
# Tokenize input query
input_ids = tokenizer.encode(query, return_tensors="pt")
# Generate text using the model
final_outputs = model.generate(
input_ids,
do_sample=True,
max_length=int(max_length), # Convert max_length to integer
temperature=float(temperature), # Convert temperature to float
top_k=int(top_k), # Convert top_k to integer
top_p=float(top_p), # Convert top_p to float
pad_token_id=tokenizer.pad_token_id,
)
# Decode generated text
response = tokenizer.decode(final_outputs[0], skip_special_tokens=True)
return response.split('"')[1]
# Gradio ChatInterface
demo = gr.ChatInterface(
chatbot,
additional_inputs=[
gr.Textbox(value="You are a friendly chatbot.", label="System message"),
gr.Slider(128, 1024, value=256, step=64, label="Max Length"), # Slider for max_length
gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature"), # Slider for temperature
gr.Slider(1, 100, value=50, step=1, label="Top-K"), # Slider for top_k
gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-P"), # Slider for top_p
],
)
if __name__ == "__main__":
demo.launch(share=True) # Set `share=True` to create a public link
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