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import gradio as gr
import openai
import pandas as pd
import numpy as np
# Example API key setup (for testing)
openai.api_key = "YOUR_API_KEY"
def evaluate_prompt(model_name, prompt_text):
"""
Runs a single evaluation against OpenAI or Anthropic API
Returns text output and token count
"""
if model_name.lower() == "gpt-4":
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role":"user","content":prompt_text}],
temperature=0.5
)
output_text = response['choices'][0]['message']['content']
tokens = response['usage']['total_tokens']
return f"Output:\n{output_text}\n\nTokens used: {tokens}"
else:
return "Model not supported yet"
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("## MotionEval: AI Model Evaluation MVP")
model_name = gr.Textbox(label="Model Name (e.g. GPT-4)")
prompt_text = gr.Textbox(label="Prompt", lines=5)
run_button = gr.Button("Run Evaluation")
output = gr.Textbox(label="Result", lines=15)
run_button.click(fn=evaluate_prompt, inputs=[model_name, prompt_text], outputs=output)
demo.launch()
import pandas as pd
def batch_evaluate(file, model_name):
df = pd.read_csv(file.name) # CSV must have a column called 'prompt'
results = []
for idx, row in df.iterrows():
prompt_text = row['prompt']
response = openai.ChatCompletion.create(
model=model_name,
messages=[{"role":"user","content":prompt_text}],
temperature=0.5
)
results.append({
'prompt': prompt_text,
'model': model_name,
'response': response['choices'][0]['message']['content']
})
return pd.DataFrame(results)
with gr.Blocks() as demo:
csv_input = gr.File(label="Upload CSV with 'prompt' column")
model_input = gr.Textbox(label="Model Name")
run_button = gr.Button("Run Batch Eval")
output_table = gr.Dataframe(headers=["prompt", "model", "response"])
run_button.click(batch_evaluate, inputs=[csv_input, model_input], outputs=output_table)
demo.launch()
import io
def results_to_csv(df):
buffer = io.StringIO()
df.to_csv(buffer, index=False)
buffer.seek(0)
return buffer
download_btn = gr.Button("Download CSV")
download_file = gr.File()
download_btn.click(results_to_csv, inputs=output_table, outputs=download_file)
def metrics(df):
df['length'] = df['response'].apply(len)
summary = df.groupby('model').agg(avg_length=('length','mean')).reset_index()
return summary |