File size: 4,862 Bytes
06fdab0 |
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 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
import gradio as gr
import random
import time
MAX_QUESTIONS = 10 # Maximum number of questions to support
######
# Fix the models
#
MODELS = [
"anthropic/claude-3-opus",
"anthropic/claude-3-sonnet",
"google/gemini-pro",
"meta-llama/llama-2-70b-chat",
"mistral/mistral-medium",
"deepseek/deepseek-coder",
"deepseek/deepseek-r1",
]
#
######
######
# Add OpenRouter here
#
def get_response(question, model):
# Simulate an API call with a random delay
time.sleep(random.uniform(0.5, 1.5))
return f"Sample response from {model} for: {question}"
#
######
def read_questions(file_obj):
"""Read questions from uploaded file and return as list"""
with open(file_obj.name, 'r') as file:
questions = [q.strip() for q in file.readlines() if q.strip()]
if len(questions) > MAX_QUESTIONS:
raise gr.Error(f"Maximum {MAX_QUESTIONS} questions allowed.")
return questions
with gr.Blocks() as demo:
gr.Markdown("# Vibes Benchmark\nUpload a `.txt` file with **one question per line**.")
file_input = gr.File(label="Upload your questions (.txt)")
run_button = gr.Button("Run Benchmark", variant="primary")
# Create dynamic response areas
response_areas = []
for i in range(MAX_QUESTIONS):
with gr.Group(visible=False) as group_i:
gr.Markdown(f"### Question {i+1}")
with gr.Row():
with gr.Column():
# Accordion for Model 1
with gr.Accordion("Model 1", open=False):
model1_i = gr.Markdown("")
response1_i = gr.Textbox(label="Response 1", interactive=False, lines=4)
with gr.Column():
# Accordion for Model 2
with gr.Accordion("Model 2", open=False):
model2_i = gr.Markdown("")
response2_i = gr.Textbox(label="Response 2", interactive=False, lines=4)
gr.Markdown("---")
response_areas.append({
'group': group_i,
'model1': model1_i,
'response1': response1_i,
'model2': model2_i,
'response2': response2_i
})
def process_file(file):
"""Show/hide question groups depending on how many questions are in the file."""
if file is None:
raise gr.Error("Please upload a file first.")
questions = read_questions(file)
# Show as many question groups as needed; hide the rest
updates = []
for i in range(MAX_QUESTIONS):
updates.append(gr.update(visible=(i < len(questions))))
return updates
def run_benchmark(file):
"""Generator function yielding partial updates in real time."""
questions = read_questions(file)
# Initialize all update values as blank
# We have 4 fields per question (model1, response1, model2, response2)
# => total of MAX_QUESTIONS * 4 output components
updates = [gr.update(value="")] * (MAX_QUESTIONS * 4)
# Process each question, 2 models per question
for i, question in enumerate(questions):
# 1) Pick first model, yield it
model_1 = random.choice(MODELS)
updates[i*4] = gr.update(value=f"**{model_1}**") # model1 for question i
yield updates # partial update (reveal model_1 accordion)
# 2) Get response from model_1
response_1 = get_response(question, model_1)
updates[i*4 + 1] = gr.update(value=response_1) # response1
yield updates
# 3) Pick second model (ensure different from first), yield it
remaining_models = [m for m in MODELS if m != model_1]
model_2 = random.choice(remaining_models)
updates[i*4 + 2] = gr.update(value=f"**{model_2}**") # model2
yield updates
# 4) Get response from model_2
response_2 = get_response(question, model_2)
updates[i*4 + 3] = gr.update(value=response_2) # response2
yield updates
# The outputs we update after each yield
update_targets = []
for area in response_areas:
update_targets.append(area['model1'])
update_targets.append(area['response1'])
update_targets.append(area['model2'])
update_targets.append(area['response2'])
# Connect events
file_input.change(
fn=process_file,
inputs=file_input,
outputs=[area['group'] for area in response_areas]
)
run_button.click(
fn=run_benchmark,
inputs=file_input,
outputs=update_targets
)
# Enable queue for partial outputs to appear as they are yielded
demo.queue()
demo.launch()
|