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| { | |
| "name": "Lonely Stonebraker", | |
| "description": "Design Dialogues with Langflow.", | |
| "data": { | |
| "nodes": [ | |
| { | |
| "width": 384, | |
| "height": 461, | |
| "id": "CustomComponent-MtJjl", | |
| "type": "genericNode", | |
| "position": { | |
| "x": 534.3712097224906, | |
| "y": -135.01908566635723 | |
| }, | |
| "data": { | |
| "type": "CustomComponent", | |
| "node": { | |
| "template": { | |
| "code": { | |
| "type": "code", | |
| "required": true, | |
| "placeholder": "", | |
| "list": false, | |
| "show": true, | |
| "multiline": true, | |
| "value": "from langflow.custom import CustomComponent\nfrom langflow.field_typing import Data\nfrom pathlib import Path\nfrom platformdirs import user_cache_dir\nimport os\n\nclass Component(CustomComponent):\n documentation: str = \"http://docs.langflow.org/components/custom\"\n\n def build_config(self):\n return {\"text_input\":{\"display_name\":\"Text Input\", \"input_types\":[\"str\"]},\"save_path\":{\"display_name\":\"Save Path\",\n \"info\":\"Put the full path with the file name and extension\",\"value\":Path(user_cache_dir(\"langflow\"))/\"text.t1.txt\"}}\n\n def build(self, text_input:str,save_path:str) -> str:\n try:\n # Create the directory if it doesn't exist\n os.makedirs(os.path.dirname(save_path), exist_ok=True)\n\n # Open the file in write mode and save the text\n with open(save_path, 'w') as file:\n file.write(text_input)\n except Exception as e:\n raise e\n self.status = text_input\n return text_input", | |
| "fileTypes": [], | |
| "file_path": "", | |
| "password": false, | |
| "name": "code", | |
| "advanced": false, | |
| "dynamic": true, | |
| "info": "" | |
| }, | |
| "save_path": { | |
| "type": "str", | |
| "required": true, | |
| "placeholder": "", | |
| "list": false, | |
| "show": true, | |
| "multiline": false, | |
| "value": "/home/vazz/.cache/langflow/text.t1.txt", | |
| "fileTypes": [], | |
| "file_path": "", | |
| "password": false, | |
| "name": "save_path", | |
| "display_name": "Save Path", | |
| "advanced": false, | |
| "dynamic": false, | |
| "info": "Put the full path with the file name and extension" | |
| }, | |
| "text_input": { | |
| "type": "str", | |
| "required": true, | |
| "placeholder": "", | |
| "list": false, | |
| "show": true, | |
| "multiline": false, | |
| "fileTypes": [], | |
| "file_path": "", | |
| "password": false, | |
| "name": "text_input", | |
| "display_name": "Text Input", | |
| "advanced": false, | |
| "input_types": ["str"], | |
| "dynamic": false, | |
| "info": "", | |
| "value": "" | |
| }, | |
| "_type": "CustomComponent" | |
| }, | |
| "base_classes": ["str"], | |
| "display_name": "text checkpoint", | |
| "documentation": "http://docs.langflow.org/components/custom", | |
| "custom_fields": { | |
| "save_path": null, | |
| "text_input": null | |
| }, | |
| "output_types": ["str"], | |
| "field_formatters": {}, | |
| "beta": true | |
| }, | |
| "id": "CustomComponent-MtJjl" | |
| }, | |
| "selected": false, | |
| "dragging": false, | |
| "positionAbsolute": { | |
| "x": 534.3712097224906, | |
| "y": -135.01908566635723 | |
| } | |
| }, | |
| { | |
| "width": 384, | |
| "height": 453, | |
| "id": "CustomComponent-7NQoq", | |
| "type": "genericNode", | |
| "position": { | |
| "x": 27.487979888011637, | |
| "y": -414.43998171034826 | |
| }, | |
| "data": { | |
| "type": "CustomComponent", | |
| "node": { | |
| "template": { | |
| "audio": { | |
| "type": "file", | |
| "required": true, | |
| "placeholder": "", | |
| "list": false, | |
| "show": true, | |
| "multiline": false, | |
| "fileTypes": [], | |
| "file_path": "/home/vazz/.cache/langflow/1b0814b7-2964-4e09-9b4b-f7413c4fb50b/b56b043d8940daecbdec03b97ad4346488c58d7cc62016560dd333aa7a6a12ce.m4a", | |
| "password": false, | |
| "name": "audio", | |
| "display_name": "audio", | |
| "advanced": false, | |
| "dynamic": false, | |
| "info": "", | |
| "value": "Audio Recording 2023-12-13 at 16.35.22.m4a" | |
| }, | |
| "OpenAIKey": { | |
| "type": "str", | |
| "required": true, | |
| "placeholder": "", | |
| "list": false, | |
| "show": true, | |
| "multiline": false, | |
| "fileTypes": [], | |
| "file_path": "", | |
| "password": true, | |
| "name": "OpenAIKey", | |
| "display_name": "OpenAIKey", | |
| "advanced": false, | |
| "dynamic": false, | |
| "info": "", | |
| "value": "" | |
| }, | |
| "code": { | |
| "type": "code", | |
| "required": true, | |
| "placeholder": "", | |
| "list": false, | |
| "show": true, | |
| "multiline": true, | |
| "value": "from langflow.custom import CustomComponent\nfrom typing import Optional, List, Dict, Union\nfrom langflow.field_typing import (\n AgentExecutor,\n BaseChatMemory,\n BaseLanguageModel,\n BaseLLM,\n BaseLoader,\n BaseMemory,\n BaseOutputParser,\n BasePromptTemplate,\n BaseRetriever,\n Callable,\n Chain,\n ChatPromptTemplate,\n Data,\n Document,\n Embeddings,\n NestedDict,\n Object,\n PromptTemplate,\n TextSplitter,\n Tool,\n VectorStore,\n)\n\nfrom openai import OpenAI\nimport os\nimport ffmpeg\n\nclass Component(CustomComponent):\n display_name: str = \"Whisper Transcriber\"\n description: str = \"Converts audio to text using OpenAI's Whisper.\"\n\n def build_config(self):\n return {\"audio\": {\"field_type\": \"file\", \"suffixes\": [\".mp3\", \".mp4\", \".m4a\"]}, \"OpenAIKey\": {\"field_type\": \"str\", \"password\": True}}\n\n def calculate_segment_duration(self, audio_path, target_chunk_size_mb=24):\n # Calculate the target chunk size in bytes\n target_chunk_size_bytes = target_chunk_size_mb * 1024 * 1024\n\n # Use ffprobe to get the audio file information\n ffprobe_output = ffmpeg.probe(audio_path)\n print(ffprobe_output)\n # Convert duration to float\n duration = float(ffprobe_output[\"format\"][\"duration\"])\n\n # Calculate the approximate bitrate\n bitrate = os.path.getsize(audio_path) / duration\n\n # Calculate the segment duration to achieve the target chunk size\n segment_duration = target_chunk_size_bytes / bitrate\n\n return segment_duration\n\n def split_audio_into_chunks(self, audio_path, target_chunk_size_mb=24):\n # Calculate the segment duration\n segment_duration = self.calculate_segment_duration(audio_path, target_chunk_size_mb)\n\n # Create a directory to store the chunks\n output_directory = f\"{os.path.splitext(audio_path)[0]}_chunks\"\n os.makedirs(output_directory, exist_ok=True)\n\n # Use ffmpeg-python to split the audio file into chunks\n (\n ffmpeg.input(audio_path)\n .output(f\"{output_directory}/%03d{os.path.splitext(audio_path)[1]}\", codec=\"copy\", f=\"segment\", segment_time=segment_duration)\n .run()\n )\n\n # Get the list of generated chunk files\n chunks = [os.path.join(output_directory, file) for file in os.listdir(output_directory)]\n\n return chunks\n\n def build(self, audio: str, OpenAIKey: str) -> str:\n # Split audio into chunks\n audio_chunks = self.split_audio_into_chunks(audio)\n\n client = OpenAI(api_key=OpenAIKey)\n transcripts = []\n\n try:\n for chunk in audio_chunks:\n with open(chunk, \"rb\") as chunk_file:\n transcript = client.audio.transcriptions.create(\n model=\"whisper-1\",\n file=chunk_file,\n response_format=\"text\"\n )\n transcripts.append(transcript)\n finally:\n # Clean up temporary chunk files\n for chunk in audio_chunks:\n os.remove(chunk)\n\n # Concatenate transcripts into the final response\n final_response = \"\\n\".join(transcripts)\n self.status = final_response\n return final_response\n", | |
| "fileTypes": [], | |
| "file_path": "", | |
| "password": false, | |
| "name": "code", | |
| "advanced": false, | |
| "dynamic": true, | |
| "info": "" | |
| }, | |
| "_type": "CustomComponent" | |
| }, | |
| "description": "Converts audio to text using OpenAI's Whisper.", | |
| "base_classes": ["str"], | |
| "display_name": "Whisper Transcriber", | |
| "documentation": "", | |
| "custom_fields": { | |
| "OpenAIKey": null, | |
| "audio": null | |
| }, | |
| "output_types": ["str"], | |
| "field_formatters": {}, | |
| "beta": true | |
| }, | |
| "id": "CustomComponent-7NQoq" | |
| }, | |
| "selected": true, | |
| "positionAbsolute": { | |
| "x": 27.487979888011637, | |
| "y": -414.43998171034826 | |
| }, | |
| "dragging": false | |
| } | |
| ], | |
| "edges": [ | |
| { | |
| "source": "CustomComponent-7NQoq", | |
| "sourceHandle": "{œbaseClassesœ:[œstrœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-7NQoqœ}", | |
| "target": "CustomComponent-MtJjl", | |
| "targetHandle": "{œfieldNameœ:œtext_inputœ,œidœ:œCustomComponent-MtJjlœ,œinputTypesœ:[œstrœ],œtypeœ:œstrœ}", | |
| "data": { | |
| "targetHandle": { | |
| "fieldName": "text_input", | |
| "id": "CustomComponent-MtJjl", | |
| "inputTypes": ["str"], | |
| "type": "str" | |
| }, | |
| "sourceHandle": { | |
| "baseClasses": ["str"], | |
| "dataType": "CustomComponent", | |
| "id": "CustomComponent-7NQoq" | |
| } | |
| }, | |
| "style": { | |
| "stroke": "#555" | |
| }, | |
| "className": "stroke-gray-900 stroke-connection", | |
| "animated": false, | |
| "id": "reactflow__edge-CustomComponent-7NQoq{œbaseClassesœ:[œstrœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-7NQoqœ}-CustomComponent-MtJjl{œfieldNameœ:œtext_inputœ,œidœ:œCustomComponent-MtJjlœ,œinputTypesœ:[œstrœ],œtypeœ:œstrœ}" | |
| } | |
| ], | |
| "viewport": { | |
| "x": 119.37759169012509, | |
| "y": 351.3082742479685, | |
| "zoom": 1 | |
| } | |
| }, | |
| "is_component": false, | |
| "updated_at": "2023-12-13T23:51:56.874099", | |
| "folder": null, | |
| "id": "1b0814b7-2964-4e09-9b4b-f7413c4fb50b", | |
| "user_id": "8b5cf798-f1b8-4108-88fd-d7274d08d471" | |
| } | |