feat(quivr-whisper): add initial project files (#3492)

# Description

Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.

## Checklist before requesting a review

Please delete options that are not relevant.

- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged

## Screenshots (if appropriate):

examples/quivr-whisper/.env_example

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+QUIVR_API_KEY=XXXX
+QUIVR_CHAT_ID=1XXXX
+QUIVR_BRAIN_ID=XXXX
+QUIVR_URL=XXXX
+OPENAI_API_KEY=XXXX

examples/quivr-whisper/.gitignore

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+.env

examples/quivr-whisper/.python-version

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+3.11.9

examples/quivr-whisper/README.md

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+# quivr-whisper
+
+Describe your project here.

examples/quivr-whisper/Readme.md

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+# Quivr-Whisper
+
+Quivr-Whisper is a web application that allows users to ask questions via audio input. It leverages OpenAI's Whisper model for speech transcription and synthesizes responses using OpenAI's text-to-speech capabilities. The application queries the Quivr API to get a response based on the transcribed audio input.
+
+
+
+https://github.com/StanGirard/quivr-whisper/assets/19614572/9cc270c9-07e4-4ce1-bcff-380f195c9313
+
+
+
+## Features
+
+- Audio input for asking questions
+- Speech transcription using OpenAI's Whisper model
+- Integration with Quivr API for intelligent responses
+- Speech synthesis of the response for audio playback
+
+## Getting Started
+
+These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
+
+### Prerequisites
+
+What things you need to install the software and how to install them:
+
+- Python 3.6+
+- pip for Python 3
+- Flask
+- OpenAI Python package
+- Requests package
+
+### Installing
+
+A step by step series of examples that tell you how to get a development environment running:
+
+1. Clone the repository to your local machine.
+```bash
+git clone https://github.com/stangirard/quivr-whisper.git
+cd Quivr-talk
+```
+
+2. Install the required packages.
+```bash
+pip install flask openai requests python-dotenv
+```
+
+3. Create a `.env` file in the root directory of the project and add your API keys and other configuration variables.
+```env
+OPENAI_API_KEY='your_openai_api_key'
+QUIVR_API_KEY='your_quivr_api_key'
+QUIVR_CHAT_ID='your_quivr_chat_id'
+QUIVR_BRAIN_ID='your_quivr_brain_id'
+QUIVR_URL='https://api.quivr.app' # Optional, only if different from the default
+```
+
+4. Run the Flask application.
+```bash
+flask run
+```
+
+Your app should now be running on `http://localhost:5000`.
+
+## Usage
+
+To use Quivr-talk, navigate to `http://localhost:5000` in your web browser, click on "Ask a question to Quivr", and record your question. Wait for the transcription and response to be synthesized, and you will hear the response played back to you.

examples/quivr-whisper/app.py

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+from flask import Flask, render_template, request, jsonify
+import openai
+import base64
+import os
+import requests
+from dotenv import load_dotenv
+from tempfile import NamedTemporaryFile
+
+app = Flask(__name__)
+load_dotenv()
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+quivr_token = os.getenv("QUIVR_API_KEY", "")
+quivr_chat_id = os.getenv("QUIVR_CHAT_ID", "")
+quivr_brain_id = os.getenv("QUIVR_BRAIN_ID", "")
+quivr_url = (
+    os.getenv("QUIVR_URL", "https://api.quivr.app")
+    + f"/chat/{quivr_chat_id}/question?brain_id={quivr_brain_id}"
+)
+
+headers = {
+    "Content-Type": "application/json",
+    "Authorization": f"Bearer {quivr_token}",
+}
+
+
+@app.route("/")
+def index():
+    return render_template("index.html")
+
+
+@app.route("/transcribe", methods=["POST"])
+def transcribe_audio():
+    audio_file = request.files["audio_data"]
+    transcript = transcribe_audio_file(audio_file)
+    quivr_response = ask_quivr_question(transcript)
+    audio_base64 = synthesize_speech(quivr_response)
+    return jsonify({"audio_base64": audio_base64})
+
+
+def transcribe_audio_file(audio_file):
+    with NamedTemporaryFile(suffix=".webm", delete=False) as temp_audio_file:
+        audio_file.save(temp_audio_file)
+        temp_audio_file_path = temp_audio_file.name
+
+    try:
+        with open(temp_audio_file_path, "rb") as f:
+            transcript_response = openai.audio.transcriptions.create(
+                model="whisper-1", file=f
+            )
+        transcript = transcript_response.text
+    finally:
+        os.unlink(temp_audio_file_path)
+
+    return transcript
+
+
+def ask_quivr_question(transcript):
+    response = requests.post(quivr_url, headers=headers, json={"question": transcript})
+    if response.status_code == 200:
+        quivr_response = response.json().get("assistant")
+        return quivr_response
+    else:
+        print(f"Error from Quivr API: {response.status_code}, {response.text}")
+        return "Sorry, I couldn't understand that."
+
+
+def synthesize_speech(text):
+    speech_response = openai.audio.speech.create(
+        model="tts-1", voice="nova", input=text
+    )
+    audio_content = speech_response.content
+    audio_base64 = base64.b64encode(audio_content).decode("utf-8")
+    return audio_base64
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

examples/quivr-whisper/pyproject.toml

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+[project]
+name = "quivr-whisper"
+version = "0.1.0"
+description = "Add your description here"
+authors = [
+    { name = "Stan Girard", email = "[email protected]" }
+]
+dependencies = [
+    "flask>=3.1.0",
+    "openai>=1.54.5",
+    "quivr-core>=0.0.24",
+]
+readme = "README.md"
+requires-python = ">= 3.11"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.rye]
+managed = true
+dev-dependencies = []
+
+[tool.hatch.metadata]
+allow-direct-references = true
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/quivr_whisper"]