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DST-227: demo code for call summaries (#31)
Resolves DST-227 adds chainlit interface for demo
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,150 @@ | ||
| #!/usr/bin/env chainlit run | ||
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| import pprint | ||
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| import chainlit as cl | ||
| from chainlit.input_widget import Select, Switch, Slider | ||
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| from chunking import chunking_ingest | ||
| from llm import LLM | ||
| from refinement import refinement_ingest | ||
| from run import get_transcript, stuffing_summary | ||
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| OLLAMA_LLMS = ["openhermes", "dolphin-mistral"] | ||
| GOOGLE_LLMS = ["gemini-pro", "gemini-flash"] | ||
| OPENAI_LLMS = ["gpt-4", "gpt-4o"] | ||
| ANTHROPIC_LLMS = ["claude"] | ||
| SUMMARIZATION_TECHNIQUE = ["stuffing", "chunking", "refinement"] | ||
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| @cl.on_chat_start | ||
| async def init_chat(): | ||
| await cl.Message( | ||
| content="Hi, this is a transcript summarizer, upload a transcript file to summarize it.", | ||
| actions=[ | ||
| cl.Action(name="settingsAct", value="chat_settings", label="Show settings"), | ||
| cl.Action( | ||
| name="uploadTranscriptAct", | ||
| value="upload_transcript_file", | ||
| label="Upload transcript file for summarization", | ||
| ), | ||
| ], | ||
| ).send() | ||
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| settings = await cl.ChatSettings( | ||
| [ | ||
| Select( | ||
| id="model", | ||
| label="LLM Model", | ||
| values=OLLAMA_LLMS + GOOGLE_LLMS + OPENAI_LLMS + ANTHROPIC_LLMS, | ||
| initial_index=0, | ||
| ), | ||
| Select( | ||
| id="summarization_technique", | ||
| label="Choose summarization technique", | ||
| values=SUMMARIZATION_TECHNIQUE, | ||
| initial_index=0, | ||
| ), | ||
| Slider( | ||
| id="temperature", | ||
| label="LLM Temperature", | ||
| initial=0.1, | ||
| min=0, | ||
| max=1, | ||
| step=0.1, | ||
| ), | ||
| Switch(id="streaming", label="Stream response tokens", initial=False), | ||
| ] | ||
| ).send() | ||
| cl.user_session.set("settings", settings) | ||
| await init_llm_client_if_needed() | ||
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| @cl.action_callback("settingsAct") | ||
| async def on_click_settings(action: cl.Action): | ||
| settings = cl.user_session.get("settings") | ||
| settings_str = pprint.pformat(settings, indent=4) | ||
| await cl.Message(content=f"{action.value}:\n`{settings_str}`").send() | ||
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| @cl.on_settings_update | ||
| async def update_settings(settings): | ||
| print("Settings updated:", pprint.pformat(settings, indent=4)) | ||
| cl.user_session.set("settings", settings) | ||
| await set_llm_model() | ||
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| async def set_llm_model(): | ||
| settings = cl.user_session.get("settings") | ||
| model_name = settings["model"] | ||
| llm_settings = dict((k, settings[k]) for k in ["temperature"] if k in settings) | ||
| msg = cl.Message( | ||
| author="backend", | ||
| content=f"Setting up LLM: {model_name} with `{llm_settings}`...\n", | ||
| ) | ||
| client = None | ||
| if model_name in OLLAMA_LLMS: | ||
| client = LLM(client_name="ollama", model_name=model_name, settings=llm_settings) | ||
| elif model_name in GOOGLE_LLMS: | ||
| client = LLM(client_name="gemini", model_name=model_name, settings=llm_settings) | ||
| elif model_name in OPENAI_LLMS: | ||
| client = LLM(client_name="gpt", model_name=model_name, settings=llm_settings) | ||
| elif model_name in ANTHROPIC_LLMS: | ||
| client = LLM(client_name="claude", settings=llm_settings) | ||
| else: | ||
| await cl.Message(content=f"Could not initialize model: {model_name}").send() | ||
| return | ||
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| client.init_client() | ||
| cl.user_session.set("client", client) | ||
| await msg.stream_token(f"Done setting up {model_name} LLM") | ||
| await msg.send() | ||
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| async def init_llm_client_if_needed(): | ||
| client = cl.user_session.get("client") | ||
| if not client: | ||
| await set_llm_model() | ||
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| @cl.action_callback("uploadTranscriptAct") | ||
| async def on_click_upload_file_query(action: cl.Action): | ||
| files = None | ||
| # Wait for the user to upload a file | ||
| while files is None: | ||
| files = await cl.AskFileMessage( | ||
| content="Please upload a transcript as a text file.", | ||
| accept=["text/plain", "application/pdf", "application/json"], | ||
| max_size_mb=20, | ||
| timeout=180, | ||
| ).send() | ||
| file = files[0] | ||
| transcript = get_transcript(file_path=file.path) | ||
| settings = cl.user_session.get("settings") | ||
| msg = cl.Message(content=f"Processing `{file.name}`...", disable_feedback=True) | ||
| await msg.send() | ||
| msg.content = f"Processing `{file.name}` completed." | ||
| await msg.update() | ||
| msg.content = "Generating transcript..." | ||
| await msg.update() | ||
| response = await run_summarization_technique( | ||
| technique=settings["summarization_technique"], transcript=transcript | ||
| ) | ||
| print(response) | ||
| answer = f"Result:\n{response}" | ||
| await cl.Message(content=answer).send() | ||
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| async def run_summarization_technique(transcript, technique): | ||
| await init_llm_client_if_needed() | ||
| client = cl.user_session.get("client") | ||
| print(f"client: {client}") | ||
| if technique == "stuffing": | ||
| print("running stuffing") | ||
| return stuffing_summary(transcript=transcript, client=client) | ||
| elif technique == "chunking": | ||
| print("running chunking") | ||
| return chunking_ingest(transcript=transcript, client=client) | ||
| elif technique == "refinement": | ||
| print("running refinement") | ||
| return refinement_ingest(transcript=transcript, client=client) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -8,4 +8,5 @@ sentence-transformers | |
| nltk | ||
| spacy==3.7.4 | ||
| langchain-text-splitters | ||
| langchain_openai | ||
| langchain_openai | ||
| chainlit | ||
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