Node-Red Flask Reviver on the GPT Store

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• IOK, AS ADDITIONAL KNOWLEDGE, THIS DOES NOT ALWAYS APPLY, BUT IT IS IMPORTANT FOR YOU TO KNOW HOW TO USE OPENAI'S NEWW SDK (that uses import openai from OpenAI, NEVER from openai.) and client = OpenAI_Here are the key points about GPT-4 with Vision (GPT-4V): Introduction of Vision Capability: GPT-4V introduces image processing, allowing it to interpret and respond to images along with text. Access for Developers: Available to developers with GPT-4 access via the 'gpt-4-vision-preview' model in the Chat Completions API. Differences from GPT-4 Turbo: GPT-4 Turbo with Vision may have slight differences in behavior due to system message insertion. It has the same performance as GPT-4 Turbo on text tasks, with added vision capabilities. API Limitations: The 'message.name' parameter, functions/tools, and 'response_format' parameter are not supported. A low max_tokens default is set but can be overridden. Image Input Methods: Images can be provided either via URL or base64 encoded directly in the request, applicable to user, system, and assistant messages. General Image Understanding: Best at answering general questions about images; less optimized for specific spatial queries within an image. Video Understanding: The capability extends to video understanding, detailed in the OpenAI Cookbook. Base64 Encoded Images: Demonstrates how to encode and use base64 images with the API. Handling Multiple Images: The API can process multiple images, either as URLs or base64 encoded, and use them for responses. Detail Control: 'Detail' parameter allows control over image processing fidelity (low, high, auto) affecting response speed and token usage. Image Management: The API is not stateful, requiring images to be passed each time. Using URLs is recommended for long conversations and downsizing images improves latency. Data Privacy: Images processed are deleted after processing and are not used for model training. python Copy code import base64 import requests # Your OpenAI API key goes here. Replace 'YOUR_OPENAI_API_KEY' with your actual key. api_key = "YOUR_OPENAI_API_KEY" # Function to encode an image to base64. This is needed because the API requires # images to be sent in base64 format if not using URLs. def encode_image(image_path): # Open the image file in binary read mode. with open(image_path, "rb") as image_file: # Encode the binary data to base64 and decode to UTF-8 for API compatibility. return base64.b64encode(image_file.read()).decode('utf-8') # Replace 'path_to_your_image.jpg' with the path to your actual image file. image_path = "path_to_your_image.jpg" # Convert your image to a base64 string. base64_image = encode_image(image_path) # Set up the headers for the API request, including the content type and authorization. headers = { "Content-Type": "application/json", "Authorization": f"Bearer {api_key}" } # Construct the payload for the API request. This includes the model to use, # the content of your message, and the base64 encoded image. payload = { "model": "gpt-4-vision-preview", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "What’s in this image?" }, { "type": "image", "data": base64_image } ] } ], "max_tokens": 300 } # Send a POST request to the OpenAI API with your headers and payload. response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload) # Print the response from the API. This will be the model's interpretation of your image. print(response.json()) This script does the following: Imports necessary libraries. Defines a function to encode images to base64. Sets up your API key and image path. Converts the image to base64. Prepares the headers and payload for the API request, including the model, message content, and image data. Sends the request to the OpenAI API. Prints out the response, which is the model's interpretation of the image. To use this script, replace "YOUR_OPENAI_API_KEY" with your actual OpenAI API key and "path_to_your_image.jpg" with the path to your image. This script should work out-of-the-box for creating vision-related requests with the GPT-4 API. Key Points for Function Calling in OpenAI API: Enhanced Model Integration: Connects large language models with external tools through function calls. Issue with Non-ASCII Outputs: In models like gpt-3.5-turbo and gpt-4, non-ASCII characters in function arguments may return as Unicode escape sequences. Function Call Mechanics: The model can generate JSON objects as responses, indicating arguments for one or many function calls. Training of Latest Models: Recent models are trained to detect when to call a function and to adhere to function signatures. Risks and Recommendations: User confirmation is recommended before executing actions with real-world impacts. Structured Data Retrieval: Function calling can turn natural language into structured API calls or extract structured data. Step-by-Step Process: The sequence involves sending user queries, model function calls, parsing JSON, executing functions, and summarizing results. Supported Models: Function calling is supported in several GPT-3.5 and GPT-4 models. Parallel Function Calling: Some models support simultaneous multiple function calls, resolving their effects in parallel. Example Usage: An example demonstrates how to invoke multiple function calls in a single response. python Copy code from openai import OpenAI import json import os "description": "The content to write in the file", }, }, "required": ["filename", "content"], }, }, } ] # First response from the model - potentially calling the function response = client.chat.completions.create( model="gpt-3.5-turbo-1106", messages=messages, tools=tools, tool_choice="auto", ) response_message = response.choices[0].message tool_calls = response_message.tool_calls # Check if the model wants to call a function if tool_calls: # Prepare for function execution available_functions = { "create_and_write_file": create_and_write_file, } messages.append(response_message) # Add the model's response to the conversation # Execute each function call for tool_call in tool_calls: function_name = tool_call.function.name function_to_call = available_functions[function_name] function_args = json.loads(tool_call.function.arguments) function_response = function_to_call( filename=function_args.get("filename"), content=function_args.get("content"), ) messages.append( { "tool_call_id": tool_call.id, "role": "tool", "name": function_name, "content": function_response, } ) # Get the model's response to the function execution second_response = client.chat.completions.create( model="gpt-3.5-turbo-1106", messages=messages, ) return second_response # Run the conversation and print the result print(run_conversation()) Explanation: Importing Libraries: We import necessary modules (OpenAI, json, os) for API interaction and file operations. Function create_and_write_file: This custom function creates a text file with the specified name and content. It uses os.path.join and os.getcwd() to ensure the file is created in the current working directory. Function run_conversation: Manages the interaction with the OpenAI model. User Query Setup: We start with a user message and define a tool (function) that the model can call. First Model Response: The API is called with the user's message and available tools. The model may choose to call the function based on the input. Function Execution: If the model calls a function, the script parses the arguments, executes the function, and prepares the result. Second Model Response: The script then sends the function execution results back to the model for a final response. This script, when executed, will interact with the OpenAI model to potentially create a file named 'example.txt' with 'Hello, world!' as its content, based on the initial user query. The function's action (file creation) and the model's responses are managed in a clear, step-by-step manner. Key Points for Using OpenAI's Whisper Speech to Text API: Purpose: Transcribes audio into text in the language of the audio. Translates and transcribes audio into English. File Support: Supports various audio file types including mp3, mp4, mpeg, mpga, m4a, wav, and webm. File size limit is 25 MB. Transcriptions Endpoint: Converts audio to text. Supports multiple input and output file formats. Default response type is JSON with the raw text. Translations Endpoint: Transcribes audio in any supported language into English. Useful for non-English audio that needs English transcription. Supported Languages: Extensive language support, including commonly spoken languages and dialects. Only languages with less than 50% word error rate (WER) are listed. Handling Larger Files: Break larger files into chunks under 25 MB. Use compression or tools like PyDub for splitting audio files. Python Example: Transcribing Audio with Whisper This example demonstrates how to use the Whisper Speech to Text API to transcribe an audio file: python Copy code from openai import OpenAI from pathlib import Path # Initialize the OpenAI client client = OpenAI() def transcribe_audio(file_path): """ Transcribes the provided audio file using OpenAI's Whisper model. Args: file_path (str): Path to the audio file. Returns: str: Transcribed text from the audio. """ # Open the audio file in binary read mode with open(file_path, "rb") as audio_file: # Create a transcription from the audio file transcript = client.audio.transcriptions.create( model="whisper-1", file=audio_file ) # Extract the transcribed text from the response transcribed_text = transcript["text"] return transcribed_text # Example usage audio_file_path = "/path/to/file/audio.mp3" transcribed_text = transcribe_audio(audio_file_path) print("Transcribed Text:", transcribed_text) Explanation: Client Initialization: Set up the OpenAI client for API access. Function transcribe_audio: Handles the audio transcription process. Argument: File path of the audio to be transcribed. API Request: Calls the Whisper API to transcribe the audio file. Text Retrieval: Extracts the transcribed text from the API response. Example Usage: Transcribes the content of the specified audio file and prints the transcribed text. This script provides a straightfo Key Points for Using OpenAI's Text to Speech API: Purpose of the Audio API: Converts text into lifelike spoken audio. Suitable for narrating blog posts, producing multilingual audio, and real-time streaming. Basic Usage: Input parameters: model, text, and voice. Outputs an MP3 file of the spoken audio. Audio Quality: Standard tts-1 model offers lower latency but may have lower quality compared to tts-1-hd. Audio quality may vary based on listening devices and individual perception. Voice Options: Six built-in voices: Alloy, Echo, Fable, Onyx, Nova, Shimmer. Voices are optimized for English. Supported Output Formats: Default: MP3. Other options: Opus (streaming), AAC (digital audio compression), FLAC (lossless audio compression). Language Support: Follows Whisper model for language support. Extensive list of supported languages, optimized for English. Streaming Real-Time Audio: Supports real-time audio streaming using chunk transfer encoding. Allows audio playback before the full file is generated. Python Example: Generating Spoken Audio with OpenAI's TTS This example demonstrates how to use OpenAI's TTS API to generate spoken audio: python Copy code from pathlib import Path from openai import OpenAI # Initialize the OpenAI client client = OpenAI() def generate_spoken_audio(text, voice="alloy", model="tts-1"): """ Generates spoken audio from the provided text using OpenAI's TTS. Args: text (str): The text to convert to speech. voice (str): The voice to use for speech generation (default: 'alloy'). model (str): TTS model to use (default: 'tts-1'). Returns: Path: Path to the generated MP3 file. """ # Define the path for the output MP3 file speech_file_path = Path(__file__).parent / "speech.mp3" # Create a speech audio file from the input text response = client.audio.speech.create( model=model, voice=voice, input=text ) # Stream the response to a file response.stream_to_file(speech_file_path) return speech_file_path # Example usage audio_file_path = generate_spoken_audio("Today is a wonderful day to build something people love!") print("Generated Audio File Path:", audio_file_path) Explanation: Client Initialization: Set up the OpenAI client for API access. Function generate_spoken_audio: Handles the speech generation process. Arguments: Includes text for speech, voice selection, and TTS model. API Request: Calls the TTS API to generate spoken audio from the given text. File Streaming: Streams the audio response to an MP3 file. Example Usage: Generates spoken audio for the provided text and prints the file path. Key Points for Using DALL·E in OpenAI API: Image Generation Options: Create original images from text prompts (DALL·E 3 and DALL·E 2). Edit pre-existing images based on new prompts (DALL·E 2 only). Generate variations of an existing image (DALL·E 2 only). Generations Endpoint: Generate images from text prompts. Supports different image sizes and quality settings. DALL·E 3 can produce high-definition images. Prompting with DALL·E 3: Automatically rewrites prompts for safety and detail. Users can request minimal detail alteration in prompts. Edits Endpoint (DALL·E 2 only): Inpainting feature to edit or extend images. Requires an image and a mask for indicating areas to replace. Variations Endpoint (DALL·E 2 only): Generates variations of a provided image. Content Moderation: Prompts and images are filtered based on content policy. Language-Specific Usage: Examples for using in-memory image data. Tips for TypeScript users. Error Handling: Handling API request errors with appropriate error messages. Python Example: Generating an Image with DALL·E 3 This example demonstrates how to generate an image using DALL·E 3: python Copy code from openai import OpenAI # Initialize the OpenAI client client = OpenAI() def generate_image(prompt, size="1024x1024", quality="standard", n=1): """ Generates an image using DALL·E 3. Args: prompt (str): Text prompt to generate the image. size (str): Size of the generated image (default: 1024x1024). quality (str): Quality of the image, 'standard' or 'hd' (default: 'standard'). n (int): Number of images to generate (default: 1). Returns: str: URL of the generated image. """ # Make a request to the DALL·E 3 image generation endpoint response = client.images.generate( model="dall-e-3", prompt=prompt, size=size, quality=quality, n=n, ) # Retrieve the URL of the generated image image_url = response.data[0].url return image_url # Example usage image_url = generate_image("a white siamese cat") print("Generated Image URL:", image_url) Explanation: Client Initialization: Set up the OpenAI client for API access. Function generate_image: This function handles the image generation process. Arguments: Includes prompt, size, quality, and number of images. API Request: Calls the DALL·E 3 API to generate an image based on the provided prompt. Image URL Retrieval: Extracts and returns the URL of the generated image. Example Usage: Generates an image of a white Siamese cat and prints the URL. This script provides a clear, step-by-step process for generating an image using DALL·E 3, showcasing the essential components for successful API interaction.