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Breaking app.py but fixing summarizer.py ....

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Robert
2024-05-11 00:10:48 -07:00
parent dd1339ee3e
commit be6ad63925
2 changed files with 455 additions and 488 deletions
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HF/app.py
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@@ -1,14 +1,22 @@
#!/usr/bin/env python3 #!/usr/bin/env python3
import gradio as gr import argparse
import argparse, configparser, datetime, json, logging, os, platform, requests, shutil, subprocess, sys, time, unicodedata import configparser
import json
import logging
import os
import platform
import requests
import shutil
import subprocess
import sys
import time
import unicodedata
import zipfile import zipfile
from datetime import datetime
import contextlib import gradio as gr
import ffmpeg
import torch import torch
import yt_dlp import yt_dlp
####### #######
# Function Sections # Function Sections
# #
@@ -349,7 +357,7 @@ def process_local_file(file_path):
# Video Download/Handling # Video Download/Handling
# #
def process_url(input_path, num_speakers=2, whisper_model="small.en", offset=0, api_name=None, api_key=None, vad_filter=False, download_video_flag=False,custom_prompt=None, demo_mode=True): def process_url(input_path, num_speakers=2, whisper_model="small.en", custom_prompt=None, offset=0, api_name=None, api_key=None, vad_filter=False, download_video_flag=False, demo_mode=False):
if demo_mode: if demo_mode:
api_name = "huggingface" api_name = "huggingface"
api_key = os.environ.get(HF_TOKEN) api_key = os.environ.get(HF_TOKEN)
@@ -646,115 +654,115 @@ def speech_to_text(audio_file_path, selected_source_lang='en', whisper_model='sm
# TODO: https://huggingface.co/pyannote/speaker-diarization-3.1 # TODO: https://huggingface.co/pyannote/speaker-diarization-3.1
# embedding_model = "pyannote/embedding", embedding_size=512 # embedding_model = "pyannote/embedding", embedding_size=512
# embedding_model = "speechbrain/spkrec-ecapa-voxceleb", embedding_size=192 # embedding_model = "speechbrain/spkrec-ecapa-voxceleb", embedding_size=192
def speaker_diarize(video_file_path, segments, embedding_model = "pyannote/embedding", embedding_size=512, num_speakers=0): # def speaker_diarize(video_file_path, segments, embedding_model = "pyannote/embedding", embedding_size=512, num_speakers=0):
""" # """
1. Generating speaker embeddings for each segments. # 1. Generating speaker embeddings for each segments.
2. Applying agglomerative clustering on the embeddings to identify the speaker for each segment. # 2. Applying agglomerative clustering on the embeddings to identify the speaker for each segment.
""" # """
try: # try:
from pyannote.audio import Audio # from pyannote.audio import Audio
from pyannote.core import Segment # from pyannote.core import Segment
from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding # from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
import numpy as np # import numpy as np
import pandas as pd # import pandas as pd
from sklearn.cluster import AgglomerativeClustering # from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import silhouette_score # from sklearn.metrics import silhouette_score
import tqdm # import tqdm
import wave # import wave
#
embedding_model = PretrainedSpeakerEmbedding( embedding_model, device=torch.device("cuda" if torch.cuda.is_available() else "cpu")) # embedding_model = PretrainedSpeakerEmbedding( embedding_model, device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
#
#
_,file_ending = os.path.splitext(f'{video_file_path}') # _,file_ending = os.path.splitext(f'{video_file_path}')
audio_file = video_file_path.replace(file_ending, ".wav") # audio_file = video_file_path.replace(file_ending, ".wav")
out_file = video_file_path.replace(file_ending, ".diarize.json") # out_file = video_file_path.replace(file_ending, ".diarize.json")
#
logging.debug("getting duration of audio file") # logging.debug("getting duration of audio file")
with contextlib.closing(wave.open(audio_file,'r')) as f: # with contextlib.closing(wave.open(audio_file,'r')) as f:
frames = f.getnframes() # frames = f.getnframes()
rate = f.getframerate() # rate = f.getframerate()
duration = frames / float(rate) # duration = frames / float(rate)
logging.debug("duration of audio file obtained") # logging.debug("duration of audio file obtained")
print(f"duration of audio file: {duration}") # print(f"duration of audio file: {duration}")
#
def segment_embedding(segment): # def segment_embedding(segment):
logging.debug("Creating embedding") # logging.debug("Creating embedding")
audio = Audio() # audio = Audio()
start = segment["start"] # start = segment["start"]
end = segment["end"] # end = segment["end"]
#
# Enforcing a minimum segment length # # Enforcing a minimum segment length
if end-start < 0.3: # if end-start < 0.3:
padding = 0.3-(end-start) # padding = 0.3-(end-start)
start -= padding/2 # start -= padding/2
end += padding/2 # end += padding/2
print('Padded segment because it was too short:',segment) # print('Padded segment because it was too short:',segment)
#
# Whisper overshoots the end timestamp in the last segment # # Whisper overshoots the end timestamp in the last segment
end = min(duration, end) # end = min(duration, end)
# clip audio and embed # # clip audio and embed
clip = Segment(start, end) # clip = Segment(start, end)
waveform, sample_rate = audio.crop(audio_file, clip) # waveform, sample_rate = audio.crop(audio_file, clip)
return embedding_model(waveform[None]) # return embedding_model(waveform[None])
#
embeddings = np.zeros(shape=(len(segments), embedding_size)) # embeddings = np.zeros(shape=(len(segments), embedding_size))
for i, segment in enumerate(tqdm.tqdm(segments)): # for i, segment in enumerate(tqdm.tqdm(segments)):
embeddings[i] = segment_embedding(segment) # embeddings[i] = segment_embedding(segment)
embeddings = np.nan_to_num(embeddings) # embeddings = np.nan_to_num(embeddings)
print(f'Embedding shape: {embeddings.shape}') # print(f'Embedding shape: {embeddings.shape}')
#
if num_speakers == 0: # if num_speakers == 0:
# Find the best number of speakers # # Find the best number of speakers
score_num_speakers = {} # score_num_speakers = {}
#
for num_speakers in range(2, 10+1): # for num_speakers in range(2, 10+1):
clustering = AgglomerativeClustering(num_speakers).fit(embeddings) # clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
score = silhouette_score(embeddings, clustering.labels_, metric='euclidean') # score = silhouette_score(embeddings, clustering.labels_, metric='euclidean')
score_num_speakers[num_speakers] = score # score_num_speakers[num_speakers] = score
best_num_speaker = max(score_num_speakers, key=lambda x:score_num_speakers[x]) # best_num_speaker = max(score_num_speakers, key=lambda x:score_num_speakers[x])
print(f"The best number of speakers: {best_num_speaker} with {score_num_speakers[best_num_speaker]} score") # print(f"The best number of speakers: {best_num_speaker} with {score_num_speakers[best_num_speaker]} score")
else: # else:
best_num_speaker = num_speakers # best_num_speaker = num_speakers
#
# Assign speaker label # # Assign speaker label
clustering = AgglomerativeClustering(best_num_speaker).fit(embeddings) # clustering = AgglomerativeClustering(best_num_speaker).fit(embeddings)
labels = clustering.labels_ # labels = clustering.labels_
for i in range(len(segments)): # for i in range(len(segments)):
segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1) # segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
#
with open(out_file,'w') as f: # with open(out_file,'w') as f:
f.write(json.dumps(segments, indent=2)) # f.write(json.dumps(segments, indent=2))
#
# Make CSV output # # Make CSV output
def convert_time(secs): # def convert_time(secs):
return datetime.timedelta(seconds=round(secs)) # return datetime.timedelta(seconds=round(secs))
#
objects = { # objects = {
'Start' : [], # 'Start' : [],
'End': [], # 'End': [],
'Speaker': [], # 'Speaker': [],
'Text': [] # 'Text': []
} # }
text = '' # text = ''
for (i, segment) in enumerate(segments): # for (i, segment) in enumerate(segments):
if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]: # if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
objects['Start'].append(str(convert_time(segment["start"]))) # objects['Start'].append(str(convert_time(segment["start"])))
objects['Speaker'].append(segment["speaker"]) # objects['Speaker'].append(segment["speaker"])
if i != 0: # if i != 0:
objects['End'].append(str(convert_time(segments[i - 1]["end"]))) # objects['End'].append(str(convert_time(segments[i - 1]["end"])))
objects['Text'].append(text) # objects['Text'].append(text)
text = '' # text = ''
text += segment["text"] + ' ' # text += segment["text"] + ' '
objects['End'].append(str(convert_time(segments[i - 1]["end"]))) # objects['End'].append(str(convert_time(segments[i - 1]["end"])))
objects['Text'].append(text) # objects['Text'].append(text)
#
save_path = video_file_path.replace(file_ending, ".csv") # save_path = video_file_path.replace(file_ending, ".csv")
df_results = pd.DataFrame(objects) # df_results = pd.DataFrame(objects)
df_results.to_csv(save_path) # df_results.to_csv(save_path)
return df_results, save_path # return df_results, save_path
#
except Exception as e: # except Exception as e:
raise RuntimeError("Error Running inference with local model", e) # raise RuntimeError("Error Running inference with local model", e)
# #
# #
#################################################################################################################################### ####################################################################################################################################
@@ -777,12 +785,12 @@ def extract_text_from_segments(segments):
def summarize_with_openai(api_key, file_path, model): def summarize_with_openai(api_key, file_path, model, custom_prompt):
try: try:
logging.debug("openai: Loading json data for summarization") logging.debug("openai: Loading json data for summarization")
with open(file_path, 'r') as file: with open(file_path, 'r') as file:
segments = json.load(file) segments = json.load(file)
logging.debug("openai: Extracting text from the segments") logging.debug("openai: Extracting text from the segments")
text = extract_text_from_segments(segments) text = extract_text_from_segments(segments)
@@ -790,9 +798,14 @@ def summarize_with_openai(api_key, file_path, model):
'Authorization': f'Bearer {api_key}', 'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json' 'Content-Type': 'application/json'
} }
# headers = {
# 'Authorization': f'Bearer {api_key}',
# 'Content-Type': 'application/json'
# }
logging.debug(f"openai: API Key is: {api_key}")
logging.debug("openai: Preparing data + prompt for submittal") logging.debug("openai: Preparing data + prompt for submittal")
openai_prompt = f"{text} \n\n\n\n{prompt_text}" openai_prompt = f"{text} \n\n\n\n{custom_prompt}"
data = { data = {
"model": model, "model": model,
"messages": [ "messages": [
@@ -810,7 +823,7 @@ def summarize_with_openai(api_key, file_path, model):
} }
logging.debug("openai: Posting request") logging.debug("openai: Posting request")
response = requests.post('https://api.openai.com/v1/chat/completions', headers=headers, json=data) response = requests.post('https://api.openai.com/v1/chat/completions', headers=headers, json=data)
if response.status_code == 200: if response.status_code == 200:
summary = response.json()['choices'][0]['message']['content'].strip() summary = response.json()['choices'][0]['message']['content'].strip()
logging.debug("openai: Summarization successful") logging.debug("openai: Summarization successful")
@@ -826,13 +839,12 @@ def summarize_with_openai(api_key, file_path, model):
return None return None
def summarize_with_claude(api_key, file_path, model, custom_prompt):
def summarize_with_claude(api_key, file_path, model):
try: try:
logging.debug("anthropic: Loading JSON data") logging.debug("anthropic: Loading JSON data")
with open(file_path, 'r') as file: with open(file_path, 'r') as file:
segments = json.load(file) segments = json.load(file)
logging.debug("anthropic: Extracting text from the segments file") logging.debug("anthropic: Extracting text from the segments file")
text = extract_text_from_segments(segments) text = extract_text_from_segments(segments)
@@ -841,16 +853,17 @@ def summarize_with_claude(api_key, file_path, model):
'anthropic-version': '2023-06-01', 'anthropic-version': '2023-06-01',
'Content-Type': 'application/json' 'Content-Type': 'application/json'
} }
logging.debug("anthropic: Prepping data + prompt for submittal") anthropic_prompt = custom_prompt
logging.debug("anthropic: Prompt is {anthropic_prompt}")
user_message = { user_message = {
"role": "user", "role": "user",
"content": f"{text} \n\n\n\n{prompt_text}" "content": f"{text} \n\n\n\n{anthropic_prompt}"
} }
data = { data = {
"model": model, "model": model,
"max_tokens": 4096, # max _possible_ tokens to return "max_tokens": 4096, # max _possible_ tokens to return
"messages": [user_message], "messages": [user_message],
"stop_sequences": ["\n\nHuman:"], "stop_sequences": ["\n\nHuman:"],
"temperature": 0.7, "temperature": 0.7,
@@ -862,17 +875,17 @@ def summarize_with_claude(api_key, file_path, model):
"stream": False, "stream": False,
"system": "You are a professional summarizer." "system": "You are a professional summarizer."
} }
logging.debug("anthropic: Posting request to API") logging.debug("anthropic: Posting request to API")
response = requests.post('https://api.anthropic.com/v1/messages', headers=headers, json=data) response = requests.post('https://api.anthropic.com/v1/messages', headers=headers, json=data)
# Check if the status code indicates success # Check if the status code indicates success
if response.status_code == 200: if response.status_code == 200:
logging.debug("anthropic: Post submittal successful") logging.debug("anthropic: Post submittal successful")
response_data = response.json() response_data = response.json()
try: try:
summary = response_data['content'][0]['text'].strip() summary = response_data['content'][0]['text'].strip()
logging.debug("anthropic: Summarization succesful") logging.debug("anthropic: Summarization successful")
print("Summary processed successfully.") print("Summary processed successfully.")
return summary return summary
except (IndexError, KeyError) as e: except (IndexError, KeyError) as e:
@@ -894,9 +907,8 @@ def summarize_with_claude(api_key, file_path, model):
return None return None
# Summarize with Cohere # Summarize with Cohere
def summarize_with_cohere(api_key, file_path, model): def summarize_with_cohere(api_key, file_path, model, custom_prompt):
try: try:
logging.basicConfig(level=logging.DEBUG) logging.basicConfig(level=logging.DEBUG)
logging.debug("cohere: Loading JSON data") logging.debug("cohere: Loading JSON data")
@@ -912,7 +924,9 @@ def summarize_with_cohere(api_key, file_path, model):
'Authorization': f'Bearer {api_key}' 'Authorization': f'Bearer {api_key}'
} }
cohere_prompt = f"{text} \n\n\n\n{prompt_text}" cohere_prompt = f"{text} \n\n\n\n{custom_prompt}"
logging.debug("cohere: Prompt being sent is {cohere_prompt}")
data = { data = {
"chat_history": [ "chat_history": [
{"role": "USER", "message": cohere_prompt} {"role": "USER", "message": cohere_prompt}
@@ -938,7 +952,7 @@ def summarize_with_cohere(api_key, file_path, model):
logging.error("Expected data not found in API response.") logging.error("Expected data not found in API response.")
return "Expected data not found in API response." return "Expected data not found in API response."
else: else:
logging.error(f"cohere: API request failed with status code {response.status_code}: {resposne.text}") logging.error(f"cohere: API request failed with status code {response.status_code}: {response.text}")
print(f"Failed to process summary, status code {response.status_code}: {response.text}") print(f"Failed to process summary, status code {response.status_code}: {response.text}")
return f"cohere: API request failed: {response.text}" return f"cohere: API request failed: {response.text}"
@@ -947,9 +961,8 @@ def summarize_with_cohere(api_key, file_path, model):
return f"cohere: Error occurred while processing summary with Cohere: {str(e)}" return f"cohere: Error occurred while processing summary with Cohere: {str(e)}"
# https://console.groq.com/docs/quickstart # https://console.groq.com/docs/quickstart
def summarize_with_groq(api_key, file_path, model): def summarize_with_groq(api_key, file_path, model, custom_prompt):
try: try:
logging.debug("groq: Loading JSON data") logging.debug("groq: Loading JSON data")
with open(file_path, 'r') as file: with open(file_path, 'r') as file:
@@ -963,7 +976,9 @@ def summarize_with_groq(api_key, file_path, model):
'Content-Type': 'application/json' 'Content-Type': 'application/json'
} }
groq_prompt = f"{text} \n\n\n\n{prompt_text}" groq_prompt = f"{text} \n\n\n\n{custom_prompt}"
logging.debug("groq: Prompt being sent is {groq_prompt}")
data = { data = {
"messages": [ "messages": [
{ {
@@ -1003,7 +1018,7 @@ def summarize_with_groq(api_key, file_path, model):
# #
# Local Summarization # Local Summarization
def summarize_with_llama(api_url, file_path, token): def summarize_with_llama(api_url, file_path, token, custom_prompt):
try: try:
logging.debug("llama: Loading JSON data") logging.debug("llama: Loading JSON data")
with open(file_path, 'r') as file: with open(file_path, 'r') as file:
@@ -1016,17 +1031,17 @@ def summarize_with_llama(api_url, file_path, token):
'accept': 'application/json', 'accept': 'application/json',
'content-type': 'application/json', 'content-type': 'application/json',
} }
if len(token)>5: if len(token) > 5:
headers['Authorization'] = f'Bearer {token}' headers['Authorization'] = f'Bearer {token}'
llama_prompt = f"{text} \n\n\n\n{custom_prompt}"
logging.debug("llama: Prompt being sent is {llama_prompt}")
llama_prompt = f"{text} \n\n\n\n{prompt_text}"
logging.debug(f"llama: Complete prompt is: {llama_prompt}")
data = { data = {
"prompt": llama_prompt "prompt": llama_prompt
} }
#logging.debug(f"llama: Submitting request to API endpoint {llama_prompt}") logging.debug("llama: Submitting request to API endpoint")
print("llama: Submitting request to API endpoint") print("llama: Submitting request to API endpoint")
response = requests.post(api_url, headers=headers, json=data) response = requests.post(api_url, headers=headers, json=data)
response_data = response.json() response_data = response.json()
@@ -1048,9 +1063,8 @@ def summarize_with_llama(api_url, file_path, token):
return f"llama: Error occurred while processing summary with llama: {str(e)}" return f"llama: Error occurred while processing summary with llama: {str(e)}"
# https://lite.koboldai.net/koboldcpp_api#/api%2Fv1/post_api_v1_generate # https://lite.koboldai.net/koboldcpp_api#/api%2Fv1/post_api_v1_generate
def summarize_with_kobold(api_url, file_path): def summarize_with_kobold(api_url, file_path, custom_prompt):
try: try:
logging.debug("kobold: Loading JSON data") logging.debug("kobold: Loading JSON data")
with open(file_path, 'r') as file: with open(file_path, 'r') as file:
@@ -1063,9 +1077,11 @@ def summarize_with_kobold(api_url, file_path):
'accept': 'application/json', 'accept': 'application/json',
'content-type': 'application/json', 'content-type': 'application/json',
} }
kobold_prompt = f"{text} \n\n\n\n{custom_prompt}"
logging.debug("kobold: Prompt being sent is {kobold_prompt}")
# FIXME # FIXME
kobold_prompt = f"{text} \n\n\n\n{prompt_text}"
logging.debug(kobold_prompt)
# Values literally c/p from the api docs.... # Values literally c/p from the api docs....
data = { data = {
"max_context_length": 8096, "max_context_length": 8096,
@@ -1097,9 +1113,8 @@ def summarize_with_kobold(api_url, file_path):
return f"kobold: Error occurred while processing summary with kobold: {str(e)}" return f"kobold: Error occurred while processing summary with kobold: {str(e)}"
# https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API # https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API
def summarize_with_oobabooga(api_url, file_path): def summarize_with_oobabooga(api_url, file_path, custom_prompt):
try: try:
logging.debug("ooba: Loading JSON data") logging.debug("ooba: Loading JSON data")
with open(file_path, 'r') as file: with open(file_path, 'r') as file:
@@ -1114,14 +1129,15 @@ def summarize_with_oobabooga(api_url, file_path):
'content-type': 'application/json', 'content-type': 'application/json',
} }
#prompt_text = "I like to eat cake and bake cakes. I am a baker. I work in a french bakery baking cakes. It is a fun job. I have been baking cakes for ten years. I also bake lots of other baked goods, but cakes are my favorite." # prompt_text = "I like to eat cake and bake cakes. I am a baker. I work in a French bakery baking cakes. It is a fun job. I have been baking cakes for ten years. I also bake lots of other baked goods, but cakes are my favorite."
#prompt_text += f"\n\n{text}" # Uncomment this line if you want to include the text variable # prompt_text += f"\n\n{text}" # Uncomment this line if you want to include the text variable
ooba_prompt = f"{text}\n\n\n\n{prompt_text}" ooba_prompt = "{text}\n\n\n\n{custom_prompt}"
logging.debug("ooba: Prompt being sent is {ooba_prompt}")
data = { data = {
"mode": "chat", "mode": "chat",
"character": "Example", "character": "Example",
"messages": [{"role": "user", "content": prompt_text}] "messages": [{"role": "user", "content": ooba_prompt}]
} }
logging.debug("ooba: Submitting request to API endpoint") logging.debug("ooba: Submitting request to API endpoint")
@@ -1350,7 +1366,7 @@ def main(input_path, api_name=None, api_key=None, num_speakers=2, whisper_model=
logging.debug("MAIN: Video downloaded successfully") logging.debug("MAIN: Video downloaded successfully")
logging.debug("MAIN: Converting video file to WAV...") logging.debug("MAIN: Converting video file to WAV...")
audio_file = convert_to_wav(video_path, offset) audio_file = convert_to_wav(video_path, offset)
logging.debug("MAIN: Audio file converted succesfully") logging.debug("MAIN: Audio file converted successfully")
else: else:
if os.path.exists(path): if os.path.exists(path):
logging.debug("MAIN: Local file path detected") logging.debug("MAIN: Local file path detected")
@@ -1370,85 +1386,69 @@ def main(input_path, api_name=None, api_key=None, num_speakers=2, whisper_model=
results.append(transcription_result) results.append(transcription_result)
logging.info(f"Transcription complete: {audio_file}") logging.info(f"Transcription complete: {audio_file}")
if path.startswith('http'):
# Delete the downloaded video file
os.remove(video_path)
logging.info(f"Deleted downloaded video file: {video_path}")
# Perform summarization based on the specified API # Perform summarization based on the specified API
if api_name and api_key: if api_name and api_key:
logging.debug(f"MAIN: Summarization being performed by {api_name}") logging.debug(f"MAIN: Summarization being performed by {api_name}")
json_file_path = audio_file.replace('.wav', '.segments.json') json_file_path = audio_file.replace('.wav', '.segments.json')
if api_name.lower() == 'openai': if api_name.lower() == 'openai':
api_key = openai_api_key
try: try:
logging.debug(f"MAIN: trying to summarize with openAI") logging.debug(f"MAIN: trying to summarize with openAI")
api_key = openai_api_key summary = summarize_with_openai(api_key, json_file_path, openai_model, custom_prompt)
logging.debug(f"OpenAI: OpenAI API Key: {api_key}")
summary = summarize_with_openai(api_key, json_file_path, openai_model)
except requests.exceptions.ConnectionError: except requests.exceptions.ConnectionError:
r.status_code = "Connection: " requests.status_code = "Connection: "
elif api_name.lower() == 'anthropic': elif api_name.lower() == "anthropic":
api_key = anthropic_api_key
try: try:
logging.debug("MAIN: Trying to summarize with anthropic") logging.debug(f"MAIN: Trying to summarize with anthropic")
api_key = anthropic_api_key summary = summarize_with_claude(api_key, json_file_path, anthropic_model, custom_prompt)
logging.debug(f"Anthropic: Anthropic API Key: {api_key}")
summary = summarize_with_claude(api_key, json_file_path, anthropic_model)
except requests.exceptions.ConnectionError: except requests.exceptions.ConnectionError:
r.status_code = "Connection: " requests.status_code = "Connection: "
elif api_name.lower() == 'cohere': elif api_name.lower() == "cohere":
api_key = cohere_api_key
try: try:
logging.debug("Main: Trying to summarize with cohere") logging.debug(f"MAIN: Trying to summarize with cohere")
api_key = cohere_api_key summary = summarize_with_cohere(api_key, json_file_path, cohere_model, custom_prompt)
logging.debug(f"Cohere: Cohere API Key: {api_key}")
summary = summarize_with_cohere(api_key, json_file_path, cohere_model)
except requests.exceptions.ConnectionError: except requests.exceptions.ConnectionError:
r.status_code = "Connection: " requests.status_code = "Connection: "
elif api_name.lower() == 'groq': elif api_name.lower() == "groq":
api_key = groq_api_key
try: try:
logging.debug("Main: Trying to summarize with Groq") logging.debug(f"MAIN: Trying to summarize with Groq")
api_key = groq_api_key summary = summarize_with_groq(api_key, json_file_path, groq_model, custom_prompt)
logging.debug(f"Groq: Groq API Key: {api_key}")
summary = summarize_with_groq(api_key, json_file_path, groq_model)
except requests.exceptions.ConnectionError: except requests.exceptions.ConnectionError:
r.status_code = "Connection: " requests.status_code = "Connection: "
elif api_name.lower() == 'llama': elif api_name.lower() == "llama":
token = llama_api_key
llama_ip = llama_api_IP
try: try:
logging.debug("Main: Trying to summarize with Llama.cpp") logging.debug(f"MAIN: Trying to summarize with Llama.cpp")
token = llama_api_key summary = summarize_with_llama(llama_ip, json_file_path, token, custom_prompt)
logging.debug(f"Llama.cpp: Llama.cpp API Key: {api_key}")
llama_ip = llama_api_IP
logging.debug(f"Llama.cpp: Llama.cpp API IP:Port : {llama_ip}")
summary = summarize_with_llama(llama_ip, json_file_path, token)
except requests.exceptions.ConnectionError: except requests.exceptions.ConnectionError:
r.status_code = "Connection: " requests.status_code = "Connection: "
elif api_name.lower() == 'kobold': elif api_name.lower() == "kobold":
token = kobold_api_key
kobold_ip = kobold_api_IP
try: try:
logging.debug("Main: Trying to summarize with kobold.cpp") logging.debug(f"MAIN: Trying to summarize with kobold.cpp")
token = kobold_api_key summary = summarize_with_kobold(kobold_ip, json_file_path, custom_prompt)
logging.debug(f"kobold.cpp: Kobold.cpp API Key: {api_key}")
kobold_ip = kobold_api_IP
logging.debug(f"kobold.cpp: Kobold.cpp API IP:Port : {kobold_api_IP}")
summary = summarize_with_kobold(kobold_ip, json_file_path)
except requests.exceptions.ConnectionError: except requests.exceptions.ConnectionError:
r.status_code = "Connection: " requests.status_code = "Connection: "
elif api_name.lower() == 'ooba': elif api_name.lower() == "ooba":
token = ooba_api_key
ooba_ip = ooba_api_IP
try: try:
logging.debug("Main: Trying to summarize with oobabooga") logging.debug(f"MAIN: Trying to summarize with oobabooga")
token = ooba_api_key summary = summarize_with_oobabooga(ooba_ip, json_file_path, custom_prompt)
logging.debug(f"oobabooga: ooba API Key: {api_key}")
ooba_ip = ooba_api_IP
logging.debug(f"oobabooga: ooba API IP:Port : {ooba_ip}")
summary = summarize_with_oobabooga(ooba_ip, json_file_path)
except requests.exceptions.ConnectionError: except requests.exceptions.ConnectionError:
r.status_code = "Connection: " requests.status_code = "Connection: "
if api_name.lower() == 'huggingface': elif api_name.lower() == "huggingface":
api_key = huggingface_api_key
try: try:
logging.debug("MAIN: Trying to summarize with huggingface") logging.debug(f"MAIN: Trying to summarize with huggingface")
api_key = huggingface_api_key summarize_with_huggingface(api_key, json_file_path, custom_prompt)
logging.debug(f"huggingface: huggingface API Key: {api_key}")
summarize_with_huggingface(api_key, json_file_path)
except requests.exceptions.ConnectionError: except requests.exceptions.ConnectionError:
r.status_code = "Connection: " requests.status_code = "Connection: "
else: else:
logging.warning(f"Unsupported API: {api_name}") logging.warning(f"Unsupported API: {api_name}")

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summarize.py
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