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import gradio as gr
import requests
import os
# Define models
MODEL_OPTIONS = {
"Gemma 3-4B": "google/gemma-3-4b-it:free",
"Mistral 7B": "mistralai/mistral-7b-instruct:free",
"Qwen 0.6B": "qwen/qwen3-0.6b-04-28:free",
"DeepSeek 70B": "deepseek/deepseek-r1-distill-llama-70b:free"
}
EVALUATION_MODEL = "qwen/qwen3-32b:free"
#Load the open router api
OPENROUTER_API_KEY = os.getenv("OPENROUTERAI")
if not OPENROUTER_API_KEY:
raise RuntimeError("Missing OpenRouter API key. Please set OPENROUTERAI in your environment variables.")
# API query function
def query_openrouter(prompt, model_id):
headers = {
"Authorization": f"Bearer {OPENROUTER_API_KEY}",
"Content-Type": "application/json"
}
payload = {
"model": model_id,
"messages": [
{"role": "user", "content": prompt}
]
}
response = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload)
result = response.json()
try:
return result["choices"][0]["message"]["content"]
except Exception as e:
return f"❌ Error: {result.get('error', str(e))}"
# Main logic
def generate_and_evaluate(prompt, model_name):
model_id = MODEL_OPTIONS[model_name]
# Step 1: Generate math problem
generated = query_openrouter(prompt, model_id)
# Step 2: Evaluate using Qwen 32B
eval_prompt = (
f"Evaluate the following math problem for originality, clarity, and difficulty. "
f"Give a score out of 10 with a short explanation:\n\n{generated}"
)
evaluation = query_openrouter(eval_prompt, EVALUATION_MODEL)
return generated.strip(), evaluation.strip()
# Gradio UI
gr.Interface(
fn=generate_and_evaluate,
inputs=[
gr.Textbox(label="🧠 Math Concept Prompt", placeholder="e.g., Generate a creative trigonometry problem.", lines=2),
gr.Dropdown(choices=list(MODEL_OPTIONS.keys()), label="🧠 Choose LLM")
],
outputs=[
gr.Textbox(label="📐 Generated Math Problem", lines=6),
gr.Textbox(label="🔍 Qwen 32B Evaluation", lines=4)
],
title="🔢 Multi-LLM Math Generator + Evaluator",
description="Generate math problems with different open-source LLMs and evaluate with Qwen 32B."
).launch()
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