Week 4 Contribution: Code conversion using QWEN Coder and Free Inference Provider
This commit is contained in:
Praveen M
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4c07cdc9-bce0-49ad-85c7-14f1872b8519",
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"metadata": {},
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"source": [
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"# Python to CPP using Qwen2.5-Coder-32B-Instruct with Hyperbolic Inference Endpoint in Windows"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f051c517-c4fd-4248-98aa-b808fae76cf6",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import io\n",
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"import sys\n",
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"import gradio as gr\n",
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"import subprocess\n",
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"from dotenv import load_dotenv\n",
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"from huggingface_hub import InferenceClient\n",
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"from google import genai\n",
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"from google.genai import types\n",
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"from mistralai import Mistral"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c6c8777b-57bc-436a-978f-21a37ea310ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load Api Keys from env\n",
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"\n",
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"load_dotenv(override=True)\n",
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"\n",
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"hf_api_key = os.getenv(\"HF_TOKEN\")\n",
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"gemini_api_key = os.getenv(\"GOOGLE_API_KEY\")\n",
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"mistral_api_key = os.getenv(\"MISTRAL_API_KEY\")\n",
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"\n",
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"if not mistral_api_key or not gemini_api_key or not hf_api_key:\n",
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" print(\"API Key not found!\")\n",
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"else:\n",
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" print(\"API Key loaded in memory\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e5cf6f93-7e07-40e0-98b8-d4e74ea18402",
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"metadata": {},
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"outputs": [],
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"source": [
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"# MODELs \n",
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"\n",
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"MODEL_QWEN = \"Qwen/Qwen2.5-Coder-32B-Instruct\"\n",
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"MODEL_GEMINI = 'gemini-2.5-flash'\n",
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"MODEL_CODESTRAL = 'codestral-latest'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "689547c3-aaa5-4800-86a2-da52765997d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load Clients\n",
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"\n",
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"try:\n",
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" gemini_client = genai.Client(api_key=gemini_api_key)\n",
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" print(\"Google GenAI Client initialized successfully!\")\n",
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"\n",
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" codestral_client = Mistral(api_key=mistral_api_key)\n",
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" print(\"Mistral Client initialized successfully!\")\n",
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" \n",
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" hf_client = InferenceClient(provider=\"hyperbolic\",api_key=hf_api_key)\n",
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" print(\"Hyperbolic Inference Client initialized successfully!\")\n",
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"except Exception as e:\n",
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" print(f\"Error initializing Client: {e}\")\n",
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" exit() "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1c3a81f4-99c3-463a-ae30-4656a7a246d2",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_message = \"You are an assistant that reimplements Python code in high-performance C++ optimized for a Windows PC. \"\n",
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"system_message += \"Use Windows-specific optimizations where applicable (e.g., multithreading with std::thread, SIMD, or WinAPI if necessary). \"\n",
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"system_message += \"Respond only with the equivalent C++ code; include comments only where absolutely necessary. \"\n",
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"system_message += \"Avoid any explanation or text outside the code. \"\n",
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"system_message += \"The C++ output must produce identical functionality with the fastest possible execution time on Windows.\"\n",
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"\n",
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"generate_content_config = types.GenerateContentConfig(system_instruction=system_message)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0fde9514-1005-4539-b01b-0372730ce67b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def user_prompt_for(python):\n",
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" user_prompt = (\n",
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" \"Convert the following Python code into high-performance C++ optimized for Windows. \"\n",
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" \"Use standard C++20 or newer with Windows-compatible libraries and best practices. \"\n",
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" \"Ensure the implementation runs as fast as possible and produces identical output. \"\n",
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" \"Use appropriate numeric types to avoid overflow or precision loss. \"\n",
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" \"Avoid unnecessary abstraction; prefer direct computation and memory-efficient structures. \"\n",
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" \"Respond only with C++ code, include all required headers (like <iomanip>, <vector>, etc.), and limit comments to only what's essential.\\n\\n\"\n",
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" )\n",
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" user_prompt += python\n",
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" return user_prompt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "89c8b010-08dd-4695-a784-65162d82a24b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def user_message_gemini(python): \n",
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" return types.Content(role=\"user\", parts=[types.Part.from_text(text=user_prompt_for(python))]) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "66923158-983d-46f7-ab19-f216fb1f6a87",
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"metadata": {},
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"outputs": [],
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"source": [
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"def messages_for(python):\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": system_message},\n",
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" {\"role\": \"user\", \"content\": user_prompt_for(python)}\n",
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" ]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9ab59a54-b28a-4d07-b04f-b568e6e25dfb",
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"metadata": {},
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"outputs": [],
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"source": [
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"def write_output(cpp):\n",
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" code = cpp.replace(\"```cpp\", \"\").replace(\"```c++\", \"\").replace(\"```\", \"\").strip()\n",
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" \n",
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" if not \"#include\" in code:\n",
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" raise ValueError(\"C++ code appears invalid: missing #include directives.\")\n",
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"\n",
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" with open(\"qwenOptimized.cpp\", \"w\", encoding=\"utf-8\", newline=\"\\n\") as f:\n",
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" f.write(code) "
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]
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},
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{
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"cell_type": "markdown",
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"id": "e05ea9f0-6ade-4699-b5fa-fb8ef9f16bcb",
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"metadata": {},
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"source": [
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"### Python Codes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c515ce2c-1f8d-4484-8d34-9ffe1372dad4",
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"metadata": {},
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"outputs": [],
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"source": [
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"python_easy = \"\"\"\n",
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"import time\n",
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"\n",
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"def calculate(iterations, param1, param2):\n",
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" result = 1.0\n",
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" for i in range(1, iterations+1):\n",
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" j = i * param1 - param2\n",
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" result -= (1/j)\n",
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" j = i * param1 + param2\n",
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" result += (1/j)\n",
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" return result\n",
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"\n",
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"start_time = time.time()\n",
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"result = calculate(100_000_000, 4, 1) * 4\n",
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"end_time = time.time()\n",
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"\n",
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"print(f\"Result: {result:.12f}\")\n",
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"print(f\"Execution Time: {(end_time - start_time):.6f} seconds\")\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "83ab4080-71ae-45e6-970b-030dc462f571",
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"metadata": {},
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"outputs": [],
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"source": [
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"python_hard = \"\"\"# Be careful to support large number sizes\n",
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"\n",
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"def lcg(seed, a=1664525, c=1013904223, m=2**32):\n",
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" value = seed\n",
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" while True:\n",
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" value = (a * value + c) % m\n",
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" yield value\n",
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" \n",
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"def max_subarray_sum(n, seed, min_val, max_val):\n",
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" lcg_gen = lcg(seed)\n",
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" random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n",
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" max_sum = float('-inf')\n",
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" for i in range(n):\n",
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" current_sum = 0\n",
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" for j in range(i, n):\n",
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" current_sum += random_numbers[j]\n",
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" if current_sum > max_sum:\n",
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" max_sum = current_sum\n",
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" return max_sum\n",
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"\n",
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"def total_max_subarray_sum(n, initial_seed, min_val, max_val):\n",
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" total_sum = 0\n",
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" lcg_gen = lcg(initial_seed)\n",
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" for _ in range(20):\n",
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" seed = next(lcg_gen)\n",
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" total_sum += max_subarray_sum(n, seed, min_val, max_val)\n",
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" return total_sum\n",
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"\n",
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"# Parameters\n",
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"n = 10000 # Number of random numbers\n",
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"initial_seed = 42 # Initial seed for the LCG\n",
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"min_val = -10 # Minimum value of random numbers\n",
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"max_val = 10 # Maximum value of random numbers\n",
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"\n",
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"# Timing the function\n",
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"import time\n",
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"start_time = time.time()\n",
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"result = total_max_subarray_sum(n, initial_seed, min_val, max_val)\n",
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"end_time = time.time()\n",
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"\n",
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"print(\"Total Maximum Subarray Sum (20 runs):\", result)\n",
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"print(\"Execution Time: {:.6f} seconds\".format(end_time - start_time))\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "31498c5c-ecdd-4ed7-9607-4d09af893b98",
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"metadata": {},
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"source": [
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"## Code Implementation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ea4a4968-e04f-4939-8c42-32c960699354",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_gemini(python):\n",
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" stream = gemini_client.models.generate_content_stream(\n",
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" model = MODEL_GEMINI,\n",
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" config=generate_content_config,\n",
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" contents=user_message_gemini(python)\n",
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" )\n",
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"\n",
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" cpp_code = \"\"\n",
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" for chunk in stream:\n",
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" chunk_text = chunk.text or \"\"\n",
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" cpp_code += chunk_text\n",
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" yield cpp_code.replace('```cpp\\n','').replace('```','')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "69601eee-520f-4813-b796-aee9118e8a72",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_codestral(python):\n",
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" stream = codestral_client.chat.stream(\n",
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" model = MODEL_CODESTRAL,\n",
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" messages = messages_for(python), \n",
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" )\n",
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"\n",
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" cpp_code = \"\"\n",
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" for chunk in stream:\n",
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" chunk_text = chunk.data.choices[0].delta.content or \"\"\n",
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" cpp_code += chunk_text\n",
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" yield cpp_code.replace('```cpp\\n','').replace('```','') "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cb8899cf-54c0-4d2d-8772-42925c2e1d13",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_qwen(python):\n",
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" stream = hf_client.chat.completions.create(\n",
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" model = MODEL_QWEN,\n",
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" messages = messages_for(python),\n",
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" stream=True\n",
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" )\n",
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" cpp_code = \"\"\n",
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" for chunk in stream:\n",
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" chunk_text = chunk.choices[0].delta.content\n",
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" cpp_code += chunk_text\n",
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" yield cpp_code.replace('```cpp\\n','').replace('```','')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "98862fef-905c-4b50-bc7a-4c0462495b5c",
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"metadata": {},
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"outputs": [],
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"source": [
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"def optimize(python, model):\n",
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" if model.lower() == 'gemini':\n",
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" result = stream_gemini(python)\n",
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" elif model.lower() == 'codestral':\n",
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" result = stream_codestral(python)\n",
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" elif model.lower() == 'qwen_coder':\n",
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" result = stream_qwen(python)\n",
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" else:\n",
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" raise ValueError(\"Unknown model\")\n",
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" \n",
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" for stream_so_far in result:\n",
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" yield stream_so_far "
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]
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},
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{
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||||
"cell_type": "code",
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"execution_count": null,
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"id": "aa9372df-db01-41d0-842c-4857b20f93f0",
|
||||
"metadata": {},
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"outputs": [],
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"source": [
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"custom_css = \"\"\"\n",
|
||||
".scrollable-box textarea {\n",
|
||||
" overflow: auto !important;\n",
|
||||
" height: 400px;\n",
|
||||
"}\n",
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"\n",
|
||||
".python {background-color: #306998;}\n",
|
||||
".cpp {background-color: #050;}\n",
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"\n",
|
||||
"\"\"\"\n",
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"\n",
|
||||
"theme = gr.themes.Soft()"
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||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
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||||
"id": "dbcf9fe9-c3da-466b-8478-83dcdbe7d48e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def execute_python(code):\n",
|
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" try:\n",
|
||||
" result = subprocess.run(\n",
|
||||
" [\"python\", \"-c\", code],\n",
|
||||
" capture_output=True,\n",
|
||||
" text=True,\n",
|
||||
" timeout=60\n",
|
||||
" )\n",
|
||||
" if result.returncode == 0:\n",
|
||||
" return result.stdout or \"[No output]\"\n",
|
||||
" else:\n",
|
||||
" return f\"[Error]\\n{result.stderr}\"\n",
|
||||
" except subprocess.TimeoutExpired:\n",
|
||||
" return \"[Error] Execution timed out.\"\n",
|
||||
" except Exception as e:\n",
|
||||
" return f\"[Exception] {str(e)}\" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8029e00d-1ee8-43d1-8c87-2aa0544cf94c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def execute_cpp(code):\n",
|
||||
" write_output(code)\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" compile_cmd = [\"g++\", \"-O3\", \"-std=c++20\", \"-o\", \"optimized.exe\", \"optimized.cpp\"]\n",
|
||||
" compile_result = subprocess.run(compile_cmd, capture_output=True, text=True, check=True)\n",
|
||||
" \n",
|
||||
" run_cmd = [\"optimized.exe\"]\n",
|
||||
" run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True, timeout=60)\n",
|
||||
" \n",
|
||||
" return run_result.stdout or \"[No output]\"\n",
|
||||
" \n",
|
||||
" except subprocess.CalledProcessError as e:\n",
|
||||
" return f\"[Compile/Runtime Error]\\n{e.stderr}\"\n",
|
||||
" except subprocess.TimeoutExpired:\n",
|
||||
" return \"[Error] Execution timed out.\"\n",
|
||||
" except Exception as e:\n",
|
||||
" return f\"[Exception] {str(e)}\" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d5f4e88c-be15-4870-9f99-82b6273ee739",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with gr.Blocks(css=custom_css, theme=theme) as ui:\n",
|
||||
" gr.Markdown(\"## Convert code from Python to C++\")\n",
|
||||
" with gr.Row():\n",
|
||||
" python = gr.Textbox(label=\"Python code:\", lines=10, value=python_hard, elem_classes=[\"scrollable-box\"])\n",
|
||||
" cpp = gr.Textbox(label=\"C++ code:\", lines=10, elem_classes=[\"scrollable-box\"])\n",
|
||||
" with gr.Row():\n",
|
||||
" model = gr.Dropdown([\"Gemini\", \"Codestral\", \"QWEN_Coder\"], label=\"Select model\", value=\"Gemini\")\n",
|
||||
" convert = gr.Button(\"Convert code\")\n",
|
||||
" with gr.Row():\n",
|
||||
" python_run = gr.Button(\"Run Python\")\n",
|
||||
" cpp_run = gr.Button(\"Run C++\")\n",
|
||||
" with gr.Row():\n",
|
||||
" python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n",
|
||||
" cpp_out = gr.TextArea(label=\"C++ result:\", elem_classes=[\"cpp\"])\n",
|
||||
"\n",
|
||||
" convert.click(optimize, inputs=[python,model], outputs=[cpp])\n",
|
||||
" python_run.click(execute_python,inputs=[python], outputs=[python_out])\n",
|
||||
" cpp_run.click(execute_cpp, inputs=[cpp], outputs=[cpp_out])\n",
|
||||
"\n",
|
||||
"ui.launch(inbrowser=True) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "aa1a231e-2743-4cee-afe2-783d2b9513e5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in New Issue
Block a user