From 80637cc26ca66959400c7d34c1dd00fb3b7f287a Mon Sep 17 00:00:00 2001
From: Pat Wendorf <dungeons@gmail.com>
Date: Fri, 29 Nov 2024 09:06:22 -0500
Subject: [PATCH] Simplified the transcriber by using the openai library to
access llama.cpp server or ollama server mode.
---
requirements.txt | 5 ++++-
summarize.py | 29 +++++++++++------------------
2 files changed, 15 insertions(+), 19 deletions(-)
diff --git a/requirements.txt b/requirements.txt
index 3f0b211..96af08d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,4 +7,7 @@ pyaudio
# Transcriber
requests
-dotenv
\ No newline at end of file
+dotenv
+
+# LLM Interface
+openai
\ No newline at end of file
diff --git a/summarize.py b/summarize.py
index 52f004b..a89fcd7 100644
--- a/summarize.py
+++ b/summarize.py
@@ -5,6 +5,9 @@ import tempfile
import subprocess
from dotenv import load_dotenv
+# Use local models with the OpenAI library and a custom baseurl
+from openai import OpenAI
+
# Load environment variables from .env file
load_dotenv()
@@ -15,8 +18,6 @@ SYSTEM_MESSAGE = os.getenv("SYSTEM_MESSAGE")
SUMMARY_PROMPT = os.getenv("SUMMARY_PROMPT")
FACT_PROMPT = os.getenv("FACT_PROMPT")
SENTIMENT_PROMPT = os.getenv("SENTIMENT_PROMPT")
-PROMPT_FORMAT = os.getenv("PROMPT_FORMAT")
-STOP_TOKEN = os.getenv("STOP_TOKEN")
CHUNK_SIZE = int(os.getenv("CHUNK_SIZE"))
TEMPERATURE = float(os.getenv("TEMPERATURE"))
@@ -30,19 +31,11 @@ def whisper_api(file):
response = requests.post(WHISPERCPP_URL, data=api_data, files=files)
return response.json()["text"]
-def llama_api(prompt):
- """Generate response using llama.cpp server API."""
- formatted_prompt = PROMPT_FORMAT.format(system=SYSTEM_MESSAGE, prompt=prompt)
- api_data = {
- "prompt": formatted_prompt,
- "n_predict": -1,
- "temperature": TEMPERATURE,
- "stop": [STOP_TOKEN],
- "tokens_cached": 0
- }
- response = requests.post(LLAMACPP_URL, headers={"Content-Type": "application/json"}, json=api_data)
- json_output = response.json()
- return json_output['content']
+def llm_local(prompt):
+ client = OpenAI(api_key="doesntmatter", base_url=LLAMACPP_URL)
+ messages=[{"role": "system", "content": SYSTEM_MESSAGE},{"role": "user", "content": prompt}]
+ response = client.chat.completions.create(model="whatever", temperature=TEMPERATURE, messages=messages)
+ return response.choices[0].message.content
def trim_silence(filename):
"""Trim silence from audio file using FFmpeg."""
@@ -102,9 +95,9 @@ def summarize_transcripts():
with open(summary_filename, "a") as md_file:
for i, chunk in enumerate(chunked_data):
- summary = llama_api(SUMMARY_PROMPT.format(chunk=chunk))
- facts = llama_api(FACT_PROMPT.format(chunk=chunk))
- sentiment = llama_api(SENTIMENT_PROMPT.format(chunk=chunk))
+ summary = llm_local(SUMMARY_PROMPT.format(chunk=chunk))
+ facts = llm_local(FACT_PROMPT.format(chunk=chunk))
+ sentiment = llm_local(SENTIMENT_PROMPT.format(chunk=chunk))
md_file.write(f"# Call Transcript - {transcript} - Part {i + 1}\n\nSummary: {summary}\n\nFacts:\n{facts}\n\nSentiment: {sentiment}\n\n---\n")