Update app

Untitled-1.ipynb

@@ -1,34 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "10 ** 10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1+1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Testing dev mode"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

app.py

@@ -1,7 +1,37 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# LLM performance data with scores
+performance_data = {
+    "Undergraduate level knowledge": [("Claude 3 Opus", 86.8), ("GPT-4", 86.4), ("Gemini 1.0 Ultra", 83.7)],
+    "Graduate level reasoning": [("Claude 3 Opus", 50.4), ("Claude 3 Sonnet", 40.4), ("GPT-4", 35.7)],
+    "Grade school math": [("Claude 3 Opus", 95.0), ("Gemini 1.0 Ultra", 94.4), ("GPT-4", 92.0)],
+    "Math problem-solving": [("Claude 3 Opus", 60.1), ("Gemini 1.0 Ultra", 53.2), ("GPT-4", 52.9)],
+    "Multilingual math": [("Claude 3 Opus", 90.7), ("Claude 3 Sonnet", 83.5), ("Gemini 1.0 Ultra", 79.0)],
+    "Code": [("Claude 3 Opus", 84.9), ("Gemini 1.0 Ultra", 74.4), ("Claude 3 Haiku", 75.9)],
+    "Reasoning over text": [("Claude 3 Opus", 83.1), ("Gemini 1.0 Ultra", 82.4), ("GPT-4", 80.9)],
+    "Mixed evaluations": [("Claude 3 Opus", 86.8), ("Gemini 1.0 Ultra", 83.6), ("GPT-4", 83.1)],
+    "Knowledge Q&A": [("Claude 3 Opus", 96.4), ("GPT-4", 96.3), ("Claude 3 Sonnet", 93.2)],
+    "Common Knowledge": [("Claude 3 Opus", 95.4), ("GPT-4", 95.3), ("Gemini 1.0 Ultra", 87.8)],
+}
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def recommend_llm(task):
+    recommendations = performance_data.get(task, [])
+    if not recommendations:
+        return "No data available"
+    recommendations_sorted = sorted(recommendations, key=lambda x: x[1], reverse=True)
+    result = f"For {task}, the recommended LLMs are:\n"
+    for i, (model, score) in enumerate(recommendations_sorted):
+        result += f"{i+1}. {model} with a score of {score}%\n"
+    return result
+
+# Gradio interface
+interface = gr.Interface(
+    fn=recommend_llm,
+    inputs=gr.Dropdown(list(performance_data.keys()), label="Select Task"),
+    outputs=gr.Textbox(label="LLM Recommendations"),
+    title="LLM Recommendation App",
+    description="Select a task to get recommendations for the best LLMs based on performance data."
+)
+
+# Launch the app
+interface.launch()