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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T")
model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T")
def generate_text(prompt, max_length=100, min_length=20, temperature=1.0):
# Tokenize the prompt
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# Generate text
output = model.generate(
input_ids,
max_length=max_length,
min_length=min_length,
num_return_sequences=1,
temperature=temperature
)
# Decode the generated output
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
return generated_text
with gr.Blocks() as demo:
# Left Sidebar
gr.Text("TinyLlama Text Generator")
prompt_txt = gr.Textbox(label="User:", lines=2)
max_len_slider = gr.Slider(0, 2048, 100, label="Max Length")
min_len_slider = gr.Slider(0, 2048, 20, label="Min Length")
temp_slider = gr.Slider(0.1, 2.0, 1.0, label="Temperature")
submit_btn = gr.Button(value="Submit")
# Right Conversation Panel
chat_history = []
def respond(message, chat_history):
bot_message = generate_text(message, max_length=max_len_slider.value, min_length=min_len_slider.value, temperature=temp_slider.value)
chat_history.append((message, bot_message))
return "", chat_history
submit_btn.click(respond, [prompt_txt, chat_history], [prompt_txt, chat_history])
gr.Conversation([prompt_txt, max_len_slider, min_len_slider, temp_slider, submit_btn], [chat_history])
if __name__ == "__main__":
demo.launch()
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