diff --git a/week2/community-contributions/solisoma/end_of_week2_exercise.ipynb b/week2/community-contributions/solisoma/end_of_week2_exercise.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d6f21a6d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# week1 -> day1\n",
+ "import os\n",
+ "from dotenv import load_dotenv\n",
+ "from openai import OpenAI\n",
+ "\n",
+ "#week2 -> day2\n",
+ "import gradio as gr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "ea2e8c0c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "load_dotenv(override=True)\n",
+ "api_key:str = os.getenv('OPENAI_API_KEY')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "5c54f2f8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class SolveTechnicalQuestions:\n",
+ " _system_prompt = \"\"\"\n",
+ " You are a snarkyassistant that analyzes the contents of a website, \n",
+ " and provides a short, snarky, humorous summary, ignoring text that might be navigation related.\n",
+ " Respond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.\n",
+ " \"\"\"\n",
+ " _stream = False\n",
+ "\n",
+ " def __init__(self, model: str = \"gpt-4o-mini\") -> None:\n",
+ " self.openai_client = OpenAI()\n",
+ " self._chat_llm = OpenAI()\n",
+ " self._MODEL = model\n",
+ " \n",
+ " def set_system_prompt(self, system_prompt: str) -> None:\n",
+ " self._system_prompt = system_prompt \n",
+ "\n",
+ " def set_stream(self, stream: bool) -> None:\n",
+ " self._stream = stream\n",
+ " \n",
+ " def set_endpoint(self, endpoint: str, api_key: str = \"ollama\") -> None:\n",
+ " self.openai_client = OpenAI(base_url=endpoint, api_key=api_key)\n",
+ "\n",
+ " def set_model(self, model: str) -> None:\n",
+ " if model == \"GPT\" and self._MODEL != \"gpt-4o-mini\":\n",
+ " self._chat_llm = OpenAI()\n",
+ " self._MODEL = \"gpt-4o-mini\"\n",
+ " elif model == \"ollama\" and self._MODEL != \"llama3.2\":\n",
+ " self._chat_llm = OpenAI(base_url=\"http://localhost:11434/v1\", api_key=\"ollama\")\n",
+ " self._MODEL = \"llama3.2\"\n",
+ "\n",
+ " def talker(self, message):\n",
+ " response = self.openai_client.audio.speech.create(\n",
+ " model=\"gpt-4o-mini-tts\",\n",
+ " voice=\"onyx\", # Also, try replacing onyx with alloy or coral\n",
+ " input=message\n",
+ " )\n",
+ " return response.content\n",
+ "\n",
+ " def initiate_chat(self, message, history):\n",
+ " return \"\", history + [{\"role\":\"user\", \"content\":message}]\n",
+ "\n",
+ " def audio_to_text(self, audio_file, history):\n",
+ " \"\"\"Convert audio file to text using OpenAI Whisper\"\"\"\n",
+ "\n",
+ " \n",
+ " result = history + [{\"role\": \"user\", \"content\": \"\"}]\n",
+ " try:\n",
+ " if audio_file is None:\n",
+ " print(\"No audio file provided\")\n",
+ " result[-1][\"content\"] = \"No speech detected in audio\"\n",
+ " return result\n",
+ " \n",
+ " # Ensure we have the file path\n",
+ " if isinstance(audio_file, str):\n",
+ " file_path = audio_file\n",
+ " else:\n",
+ " file_path = audio_file.name if hasattr(audio_file, 'name') else str(audio_file)\n",
+ " \n",
+ " # Check if file exists\n",
+ " if not os.path.exists(file_path):\n",
+ " print(f\"Audio file not found: {file_path}\")\n",
+ " result[-1][\"content\"] = \"No speech detected in audio\"\n",
+ " return result\n",
+ " \n",
+ " # Check file size (Whisper has limits)\n",
+ " file_size = os.path.getsize(file_path)\n",
+ " if file_size > 25 * 1024 * 1024: # 25MB limit\n",
+ " result[-1][\"content\"] = \"Audio file too large (max 25MB)\"\n",
+ " return result\n",
+ " \n",
+ " # Transcribe using OpenAI Whisper\n",
+ " with open(file_path, \"rb\") as audio:\n",
+ " response = self.openai_client.audio.transcriptions.create(\n",
+ " model=\"whisper-1\",\n",
+ " file=audio,\n",
+ " response_format=\"text\"\n",
+ " )\n",
+ " \n",
+ " # Clean up the transcribed text\n",
+ " text = response.strip()\n",
+ " \n",
+ " if not text:\n",
+ " result[-1][\"content\"] =\"No speech detected in audio\"\n",
+ " return result\n",
+ " \n",
+ " result[-1][\"content\"] = text\n",
+ " return result\n",
+ " \n",
+ " except Exception as e:\n",
+ " error_msg = f\"Audio transcription error: {str(e)}\"\n",
+ " print(f\"{error_msg}\")\n",
+ " result[-1][\"content\"] = \"No speech detected in audio\"\n",
+ " return result\n",
+ "\n",
+ " def chat(self, history):\n",
+ " history = [{\"role\": h[\"role\"], \"content\": h[\"content\"]} for h in history]\n",
+ " messages = [{\"role\": \"system\", \"content\": self._system_prompt}] + history\n",
+ " stream = self._chat_llm.chat.completions.create(model=self._MODEL, messages=messages, stream=self._stream)\n",
+ "\n",
+ " if self._stream:\n",
+ " response = \"\"\n",
+ " voice = None\n",
+ "\n",
+ " for chunk in stream:\n",
+ " if chunk.choices[0].delta.content:\n",
+ " response += chunk.choices[0].delta.content\n",
+ " temp_history = history + [{\"role\": \"assistant\", \"content\": response}]\n",
+ " yield temp_history, voice \n",
+ " \n",
+ " voice = self.talker(response)\n",
+ " history += [{\"role\": \"assistant\", \"content\": response}]\n",
+ " yield history, voice\n",
+ " else:\n",
+ " response = stream.choices[0].message.content\n",
+ " history += [{\"role\": \"assistant\", \"content\": response}]\n",
+ " voice = self.talker(response)\n",
+ " yield history, voice\n",
+ "\n",
+ " def Interface(self, title, name, desc):\n",
+ " with gr.Blocks(title=title) as ui:\n",
+ " with gr.Column():\n",
+ " gr.Markdown(f\"\"\"\n",
+ " \n",
+ "
✈️ {name}
\n",
+ "
\n",
+ " {desc}\n",
+ "
\n",
+ "
\n",
+ "
✈️ {name}
\n",
+ "
\n",
+ " {desc}\n",
+ "
\n",
+ "