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import os
import gradio as gr
from dotenv import load_dotenv
from huggingface_hub import InferenceClient
# Load environment variables
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
raise ValueError("Please set HF_TOKEN environment variable")
# Available models
AVAILABLE_MODELS = [
"HuggingFaceH4/zephyr-7b-beta",
"NousResearch/Hermes-3-Llama-3.1-8B",
"mistralai/Mistral-Nemo-Base-2407",
"meta-llama/Llama-2-70b-hf",
"aaditya/Llama3-OpenBioLLM-8B",
]
# Initialize inference client
inference_client = InferenceClient(token=HF_TOKEN)
def get_model_response(prompt, model_name, temperature_value, do_sample):
"""Get response from a Hugging Face model."""
try:
# Build kwargs dynamically
generation_args = {
"prompt": prompt,
"model": model_name,
"max_new_tokens": 100,
"do_sample": do_sample,
"return_full_text": False
}
# Only include temperature if sampling is enabled
if do_sample and temperature_value > 0:
generation_args["temperature"] = temperature_value
response = inference_client.text_generation(**generation_args)
return response
except Exception as e:
return f"Error: {str(e)}"
def compare_models(prompt, model1, model2, temp1, temp2, do_sample1, do_sample2):
"""Compare outputs from two selected models."""
if not prompt.strip():
return (
[{"role": "user", "content": prompt}, {"role": "assistant", "content": "Please enter a prompt"}],
[{"role": "user", "content": prompt}, {"role": "assistant", "content": "Please enter a prompt"}],
gr.update(interactive=True)
)
response1 = get_model_response(prompt, model1, temp1, do_sample1)
response2 = get_model_response(prompt, model2, temp2, do_sample2)
# Format responses for chatbot display
chat1 = [{"role": "user", "content": prompt}, {"role": "assistant", "content": response1}]
chat2 = [{"role": "user", "content": prompt}, {"role": "assistant", "content": response2}]
return chat1, chat2, gr.update(interactive=True)
# Update temperature slider interactivity based on sampling checkbox
def update_slider_state(enabled):
return [
gr.update(interactive=enabled),
gr.update(
elem_classes=[] if enabled else ["disabled-slider"],
value=0 if not enabled else None
)
]
# Create the Gradio interface
with gr.Blocks(css="""
.disabled-slider { opacity: 0.5; pointer-events: none; }
""") as demo:
gr.Markdown("# LLM Comparison Tool")
gr.Markdown("Compare outputs from different Hugging Face models side by side.")
with gr.Row():
prompt = gr.Textbox(
label="Enter your prompt",
placeholder="Type your prompt here...",
lines=3
)
with gr.Row():
submit_btn = gr.Button("Generate Responses")
with gr.Row():
with gr.Column():
model1_dropdown = gr.Dropdown(
choices=AVAILABLE_MODELS,
value=AVAILABLE_MODELS[0],
label="Select Model 1"
)
do_sample1 = gr.Checkbox(
label="Enable sampling (random outputs)",
value=False
)
temp1 = gr.Slider(
label="Temperature (Higher = more creative, lower = more predictable)",
minimum=0,
maximum=1,
step=0.1,
value=0.0,
interactive=False,
elem_classes=["disabled-slider"]
)
chatbot1 = gr.Chatbot(
label="Model 1 Output",
show_label=True,
height=300,
type="messages"
)
with gr.Column():
model2_dropdown = gr.Dropdown(
choices=AVAILABLE_MODELS,
value=AVAILABLE_MODELS[1],
label="Select Model 2"
)
do_sample2 = gr.Checkbox(
label="Enable sampling (random outputs)",
value=False
)
temp2 = gr.Slider(
label="Temperature (Higher = more creative, lower = more predictable)",
minimum=0,
maximum=1,
step=0.1,
value=0.0,
interactive=False,
elem_classes=["disabled-slider"]
)
chatbot2 = gr.Chatbot(
label="Model 2 Output",
show_label=True,
height=300,
type="messages"
)
def start_loading():
return gr.update(interactive=False)
# Handle form submission
submit_btn.click(
fn=start_loading,
inputs=None,
outputs=submit_btn,
queue=False
).then(
fn=compare_models,
inputs=[prompt, model1_dropdown, model2_dropdown, temp1, temp2, do_sample1, do_sample2],
outputs=[chatbot1, chatbot2, submit_btn]
)
do_sample1.change(
fn=update_slider_state,
inputs=[do_sample1],
outputs=[temp1, temp1]
)
do_sample2.change(
fn=update_slider_state,
inputs=[do_sample2],
outputs=[temp2, temp2]
)
if __name__ == "__main__":
demo.launch()
# demo.launch(share=True) |