mirror of
https://github.com/jlengrand/tldw.git
synced 2026-03-10 08:51:17 +00:00
Breaking app.py but fixing summarizer.py ....
This commit is contained in:
Robert
426
HF/app.py
426
HF/app.py
@@ -1,14 +1,22 @@
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#!/usr/bin/env python3
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import gradio as gr
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import argparse, configparser, datetime, json, logging, os, platform, requests, shutil, subprocess, sys, time, unicodedata
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import argparse
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import configparser
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import json
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import logging
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import os
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import platform
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import requests
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import shutil
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import subprocess
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import sys
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import time
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import unicodedata
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import zipfile
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from datetime import datetime
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import contextlib
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import ffmpeg
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import gradio as gr
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import torch
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import yt_dlp
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#######
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# Function Sections
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#
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@@ -349,7 +357,7 @@ def process_local_file(file_path):
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# Video Download/Handling
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#
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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):
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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):
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if demo_mode:
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api_name = "huggingface"
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api_key = os.environ.get(HF_TOKEN)
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@@ -646,115 +654,115 @@ def speech_to_text(audio_file_path, selected_source_lang='en', whisper_model='sm
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# TODO: https://huggingface.co/pyannote/speaker-diarization-3.1
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# embedding_model = "pyannote/embedding", embedding_size=512
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# embedding_model = "speechbrain/spkrec-ecapa-voxceleb", embedding_size=192
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def speaker_diarize(video_file_path, segments, embedding_model = "pyannote/embedding", embedding_size=512, num_speakers=0):
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"""
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1. Generating speaker embeddings for each segments.
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2. Applying agglomerative clustering on the embeddings to identify the speaker for each segment.
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"""
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try:
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from pyannote.audio import Audio
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from pyannote.core import Segment
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from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
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import numpy as np
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import pandas as pd
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from sklearn.cluster import AgglomerativeClustering
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from sklearn.metrics import silhouette_score
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import tqdm
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import wave
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embedding_model = PretrainedSpeakerEmbedding( embedding_model, device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
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_,file_ending = os.path.splitext(f'{video_file_path}')
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audio_file = video_file_path.replace(file_ending, ".wav")
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out_file = video_file_path.replace(file_ending, ".diarize.json")
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logging.debug("getting duration of audio file")
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with contextlib.closing(wave.open(audio_file,'r')) as f:
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frames = f.getnframes()
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rate = f.getframerate()
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duration = frames / float(rate)
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logging.debug("duration of audio file obtained")
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print(f"duration of audio file: {duration}")
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def segment_embedding(segment):
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logging.debug("Creating embedding")
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audio = Audio()
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start = segment["start"]
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end = segment["end"]
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# Enforcing a minimum segment length
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if end-start < 0.3:
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padding = 0.3-(end-start)
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start -= padding/2
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end += padding/2
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print('Padded segment because it was too short:',segment)
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# Whisper overshoots the end timestamp in the last segment
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end = min(duration, end)
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# clip audio and embed
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clip = Segment(start, end)
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waveform, sample_rate = audio.crop(audio_file, clip)
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return embedding_model(waveform[None])
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embeddings = np.zeros(shape=(len(segments), embedding_size))
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for i, segment in enumerate(tqdm.tqdm(segments)):
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embeddings[i] = segment_embedding(segment)
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embeddings = np.nan_to_num(embeddings)
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print(f'Embedding shape: {embeddings.shape}')
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if num_speakers == 0:
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# Find the best number of speakers
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score_num_speakers = {}
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for num_speakers in range(2, 10+1):
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clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
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score = silhouette_score(embeddings, clustering.labels_, metric='euclidean')
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score_num_speakers[num_speakers] = score
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best_num_speaker = max(score_num_speakers, key=lambda x:score_num_speakers[x])
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print(f"The best number of speakers: {best_num_speaker} with {score_num_speakers[best_num_speaker]} score")
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else:
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best_num_speaker = num_speakers
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# Assign speaker label
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clustering = AgglomerativeClustering(best_num_speaker).fit(embeddings)
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labels = clustering.labels_
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for i in range(len(segments)):
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segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
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with open(out_file,'w') as f:
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f.write(json.dumps(segments, indent=2))
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# Make CSV output
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def convert_time(secs):
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return datetime.timedelta(seconds=round(secs))
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objects = {
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'Start' : [],
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'End': [],
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'Speaker': [],
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'Text': []
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}
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text = ''
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for (i, segment) in enumerate(segments):
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if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
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objects['Start'].append(str(convert_time(segment["start"])))
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objects['Speaker'].append(segment["speaker"])
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if i != 0:
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objects['End'].append(str(convert_time(segments[i - 1]["end"])))
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objects['Text'].append(text)
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text = ''
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text += segment["text"] + ' '
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objects['End'].append(str(convert_time(segments[i - 1]["end"])))
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objects['Text'].append(text)
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save_path = video_file_path.replace(file_ending, ".csv")
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df_results = pd.DataFrame(objects)
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df_results.to_csv(save_path)
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return df_results, save_path
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except Exception as e:
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raise RuntimeError("Error Running inference with local model", e)
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# def speaker_diarize(video_file_path, segments, embedding_model = "pyannote/embedding", embedding_size=512, num_speakers=0):
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# """
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# 1. Generating speaker embeddings for each segments.
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# 2. Applying agglomerative clustering on the embeddings to identify the speaker for each segment.
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# """
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# try:
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# from pyannote.audio import Audio
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# from pyannote.core import Segment
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# from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
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# import numpy as np
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# import pandas as pd
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# from sklearn.cluster import AgglomerativeClustering
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# from sklearn.metrics import silhouette_score
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# import tqdm
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# import wave
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#
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# embedding_model = PretrainedSpeakerEmbedding( embedding_model, device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
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#
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#
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# _,file_ending = os.path.splitext(f'{video_file_path}')
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# audio_file = video_file_path.replace(file_ending, ".wav")
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# out_file = video_file_path.replace(file_ending, ".diarize.json")
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#
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# logging.debug("getting duration of audio file")
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# with contextlib.closing(wave.open(audio_file,'r')) as f:
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# frames = f.getnframes()
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# rate = f.getframerate()
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# duration = frames / float(rate)
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# logging.debug("duration of audio file obtained")
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# print(f"duration of audio file: {duration}")
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#
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# def segment_embedding(segment):
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# logging.debug("Creating embedding")
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# audio = Audio()
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# start = segment["start"]
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# end = segment["end"]
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#
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# # Enforcing a minimum segment length
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# if end-start < 0.3:
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# padding = 0.3-(end-start)
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# start -= padding/2
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# end += padding/2
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# print('Padded segment because it was too short:',segment)
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#
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# # Whisper overshoots the end timestamp in the last segment
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# end = min(duration, end)
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# # clip audio and embed
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# clip = Segment(start, end)
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# waveform, sample_rate = audio.crop(audio_file, clip)
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# return embedding_model(waveform[None])
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#
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# embeddings = np.zeros(shape=(len(segments), embedding_size))
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# for i, segment in enumerate(tqdm.tqdm(segments)):
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# embeddings[i] = segment_embedding(segment)
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# embeddings = np.nan_to_num(embeddings)
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# print(f'Embedding shape: {embeddings.shape}')
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#
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# if num_speakers == 0:
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# # Find the best number of speakers
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# score_num_speakers = {}
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#
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# for num_speakers in range(2, 10+1):
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# clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
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# score = silhouette_score(embeddings, clustering.labels_, metric='euclidean')
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# score_num_speakers[num_speakers] = score
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# best_num_speaker = max(score_num_speakers, key=lambda x:score_num_speakers[x])
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# print(f"The best number of speakers: {best_num_speaker} with {score_num_speakers[best_num_speaker]} score")
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# else:
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# best_num_speaker = num_speakers
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#
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# # Assign speaker label
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# clustering = AgglomerativeClustering(best_num_speaker).fit(embeddings)
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# labels = clustering.labels_
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# for i in range(len(segments)):
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# segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
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#
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# with open(out_file,'w') as f:
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# f.write(json.dumps(segments, indent=2))
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#
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# # Make CSV output
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# def convert_time(secs):
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# return datetime.timedelta(seconds=round(secs))
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#
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# objects = {
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# 'Start' : [],
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# 'End': [],
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# 'Speaker': [],
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# 'Text': []
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# }
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# text = ''
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# for (i, segment) in enumerate(segments):
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# if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
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# objects['Start'].append(str(convert_time(segment["start"])))
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# objects['Speaker'].append(segment["speaker"])
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# if i != 0:
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# objects['End'].append(str(convert_time(segments[i - 1]["end"])))
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# objects['Text'].append(text)
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# text = ''
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# text += segment["text"] + ' '
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# objects['End'].append(str(convert_time(segments[i - 1]["end"])))
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# objects['Text'].append(text)
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#
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# save_path = video_file_path.replace(file_ending, ".csv")
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# df_results = pd.DataFrame(objects)
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# df_results.to_csv(save_path)
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# return df_results, save_path
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#
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# except Exception as e:
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# raise RuntimeError("Error Running inference with local model", e)
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#
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#
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####################################################################################################################################
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@@ -777,12 +785,12 @@ def extract_text_from_segments(segments):
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def summarize_with_openai(api_key, file_path, model):
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def summarize_with_openai(api_key, file_path, model, custom_prompt):
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try:
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logging.debug("openai: Loading json data for summarization")
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with open(file_path, 'r') as file:
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segments = json.load(file)
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logging.debug("openai: Extracting text from the segments")
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text = extract_text_from_segments(segments)
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@@ -790,9 +798,14 @@ def summarize_with_openai(api_key, file_path, model):
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'Authorization': f'Bearer {api_key}',
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'Content-Type': 'application/json'
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}
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# headers = {
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# 'Authorization': f'Bearer {api_key}',
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# 'Content-Type': 'application/json'
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# }
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logging.debug(f"openai: API Key is: {api_key}")
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logging.debug("openai: Preparing data + prompt for submittal")
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openai_prompt = f"{text} \n\n\n\n{prompt_text}"
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openai_prompt = f"{text} \n\n\n\n{custom_prompt}"
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data = {
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"model": model,
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"messages": [
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@@ -810,7 +823,7 @@ def summarize_with_openai(api_key, file_path, model):
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}
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logging.debug("openai: Posting request")
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response = requests.post('https://api.openai.com/v1/chat/completions', headers=headers, json=data)
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if response.status_code == 200:
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summary = response.json()['choices'][0]['message']['content'].strip()
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logging.debug("openai: Summarization successful")
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@@ -826,13 +839,12 @@ def summarize_with_openai(api_key, file_path, model):
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return None
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def summarize_with_claude(api_key, file_path, model):
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def summarize_with_claude(api_key, file_path, model, custom_prompt):
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try:
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logging.debug("anthropic: Loading JSON data")
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with open(file_path, 'r') as file:
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segments = json.load(file)
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logging.debug("anthropic: Extracting text from the segments file")
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text = extract_text_from_segments(segments)
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@@ -841,16 +853,17 @@ def summarize_with_claude(api_key, file_path, model):
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'anthropic-version': '2023-06-01',
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'Content-Type': 'application/json'
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}
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logging.debug("anthropic: Prepping data + prompt for submittal")
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anthropic_prompt = custom_prompt
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logging.debug("anthropic: Prompt is {anthropic_prompt}")
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user_message = {
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"role": "user",
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"content": f"{text} \n\n\n\n{prompt_text}"
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"content": f"{text} \n\n\n\n{anthropic_prompt}"
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}
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data = {
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"model": model,
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"max_tokens": 4096, # max _possible_ tokens to return
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"max_tokens": 4096, # max _possible_ tokens to return
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"messages": [user_message],
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"stop_sequences": ["\n\nHuman:"],
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"temperature": 0.7,
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@@ -862,17 +875,17 @@ def summarize_with_claude(api_key, file_path, model):
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"stream": False,
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"system": "You are a professional summarizer."
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}
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logging.debug("anthropic: Posting request to API")
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response = requests.post('https://api.anthropic.com/v1/messages', headers=headers, json=data)
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# Check if the status code indicates success
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if response.status_code == 200:
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logging.debug("anthropic: Post submittal successful")
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response_data = response.json()
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try:
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summary = response_data['content'][0]['text'].strip()
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logging.debug("anthropic: Summarization succesful")
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logging.debug("anthropic: Summarization successful")
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print("Summary processed successfully.")
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return summary
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except (IndexError, KeyError) as e:
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@@ -894,9 +907,8 @@ def summarize_with_claude(api_key, file_path, model):
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return None
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||||
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||||
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# Summarize with Cohere
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def summarize_with_cohere(api_key, file_path, model):
|
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def summarize_with_cohere(api_key, file_path, model, custom_prompt):
|
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try:
|
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logging.basicConfig(level=logging.DEBUG)
|
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logging.debug("cohere: Loading JSON data")
|
||||
@@ -912,7 +924,9 @@ def summarize_with_cohere(api_key, file_path, model):
|
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'Authorization': f'Bearer {api_key}'
|
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}
|
||||
|
||||
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}")
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||||
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||||
data = {
|
||||
"chat_history": [
|
||||
{"role": "USER", "message": cohere_prompt}
|
||||
@@ -938,7 +952,7 @@ def summarize_with_cohere(api_key, file_path, model):
|
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logging.error("Expected data not found in API response.")
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return "Expected data not found in API response."
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else:
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logging.error(f"cohere: API request failed with status code {response.status_code}: {resposne.text}")
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logging.error(f"cohere: API request failed with status code {response.status_code}: {response.text}")
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print(f"Failed to process summary, status code {response.status_code}: {response.text}")
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return f"cohere: API request failed: {response.text}"
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||||
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@@ -947,9 +961,8 @@ def summarize_with_cohere(api_key, file_path, model):
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return f"cohere: Error occurred while processing summary with Cohere: {str(e)}"
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||||
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||||
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||||
# https://console.groq.com/docs/quickstart
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def summarize_with_groq(api_key, file_path, model):
|
||||
def summarize_with_groq(api_key, file_path, model, custom_prompt):
|
||||
try:
|
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logging.debug("groq: Loading JSON data")
|
||||
with open(file_path, 'r') as file:
|
||||
@@ -963,7 +976,9 @@ def summarize_with_groq(api_key, file_path, model):
|
||||
'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 = {
|
||||
"messages": [
|
||||
{
|
||||
@@ -1003,7 +1018,7 @@ def summarize_with_groq(api_key, file_path, model):
|
||||
#
|
||||
# Local Summarization
|
||||
|
||||
def summarize_with_llama(api_url, file_path, token):
|
||||
def summarize_with_llama(api_url, file_path, token, custom_prompt):
|
||||
try:
|
||||
logging.debug("llama: Loading JSON data")
|
||||
with open(file_path, 'r') as file:
|
||||
@@ -1016,17 +1031,17 @@ def summarize_with_llama(api_url, file_path, token):
|
||||
'accept': 'application/json',
|
||||
'content-type': 'application/json',
|
||||
}
|
||||
if len(token)>5:
|
||||
if len(token) > 5:
|
||||
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 = {
|
||||
"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")
|
||||
response = requests.post(api_url, headers=headers, json=data)
|
||||
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)}"
|
||||
|
||||
|
||||
|
||||
# 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:
|
||||
logging.debug("kobold: Loading JSON data")
|
||||
with open(file_path, 'r') as file:
|
||||
@@ -1063,9 +1077,11 @@ def summarize_with_kobold(api_url, file_path):
|
||||
'accept': '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
|
||||
kobold_prompt = f"{text} \n\n\n\n{prompt_text}"
|
||||
logging.debug(kobold_prompt)
|
||||
# Values literally c/p from the api docs....
|
||||
data = {
|
||||
"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)}"
|
||||
|
||||
|
||||
|
||||
# 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:
|
||||
logging.debug("ooba: Loading JSON data")
|
||||
with open(file_path, 'r') as file:
|
||||
@@ -1114,14 +1129,15 @@ def summarize_with_oobabooga(api_url, file_path):
|
||||
'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 += 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}"
|
||||
# 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
|
||||
ooba_prompt = "{text}\n\n\n\n{custom_prompt}"
|
||||
logging.debug("ooba: Prompt being sent is {ooba_prompt}")
|
||||
|
||||
data = {
|
||||
data = {
|
||||
"mode": "chat",
|
||||
"character": "Example",
|
||||
"messages": [{"role": "user", "content": prompt_text}]
|
||||
"messages": [{"role": "user", "content": ooba_prompt}]
|
||||
}
|
||||
|
||||
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: Converting video file to WAV...")
|
||||
audio_file = convert_to_wav(video_path, offset)
|
||||
logging.debug("MAIN: Audio file converted succesfully")
|
||||
logging.debug("MAIN: Audio file converted successfully")
|
||||
else:
|
||||
if os.path.exists(path):
|
||||
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)
|
||||
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
|
||||
if api_name and api_key:
|
||||
logging.debug(f"MAIN: Summarization being performed by {api_name}")
|
||||
json_file_path = audio_file.replace('.wav', '.segments.json')
|
||||
if api_name.lower() == 'openai':
|
||||
api_key = openai_api_key
|
||||
try:
|
||||
logging.debug(f"MAIN: trying to summarize with openAI")
|
||||
api_key = openai_api_key
|
||||
logging.debug(f"OpenAI: OpenAI API Key: {api_key}")
|
||||
summary = summarize_with_openai(api_key, json_file_path, openai_model)
|
||||
logging.debug(f"MAIN: trying to summarize with openAI")
|
||||
summary = summarize_with_openai(api_key, json_file_path, openai_model, custom_prompt)
|
||||
except requests.exceptions.ConnectionError:
|
||||
r.status_code = "Connection: "
|
||||
elif api_name.lower() == 'anthropic':
|
||||
requests.status_code = "Connection: "
|
||||
elif api_name.lower() == "anthropic":
|
||||
api_key = anthropic_api_key
|
||||
try:
|
||||
logging.debug("MAIN: Trying to summarize with anthropic")
|
||||
api_key = anthropic_api_key
|
||||
logging.debug(f"Anthropic: Anthropic API Key: {api_key}")
|
||||
summary = summarize_with_claude(api_key, json_file_path, anthropic_model)
|
||||
logging.debug(f"MAIN: Trying to summarize with anthropic")
|
||||
summary = summarize_with_claude(api_key, json_file_path, anthropic_model, custom_prompt)
|
||||
except requests.exceptions.ConnectionError:
|
||||
r.status_code = "Connection: "
|
||||
elif api_name.lower() == 'cohere':
|
||||
requests.status_code = "Connection: "
|
||||
elif api_name.lower() == "cohere":
|
||||
api_key = cohere_api_key
|
||||
try:
|
||||
logging.debug("Main: Trying to summarize with cohere")
|
||||
api_key = cohere_api_key
|
||||
logging.debug(f"Cohere: Cohere API Key: {api_key}")
|
||||
summary = summarize_with_cohere(api_key, json_file_path, cohere_model)
|
||||
logging.debug(f"MAIN: Trying to summarize with cohere")
|
||||
summary = summarize_with_cohere(api_key, json_file_path, cohere_model, custom_prompt)
|
||||
except requests.exceptions.ConnectionError:
|
||||
r.status_code = "Connection: "
|
||||
elif api_name.lower() == 'groq':
|
||||
requests.status_code = "Connection: "
|
||||
elif api_name.lower() == "groq":
|
||||
api_key = groq_api_key
|
||||
try:
|
||||
logging.debug("Main: Trying to summarize with Groq")
|
||||
api_key = groq_api_key
|
||||
logging.debug(f"Groq: Groq API Key: {api_key}")
|
||||
summary = summarize_with_groq(api_key, json_file_path, groq_model)
|
||||
logging.debug(f"MAIN: Trying to summarize with Groq")
|
||||
summary = summarize_with_groq(api_key, json_file_path, groq_model, custom_prompt)
|
||||
except requests.exceptions.ConnectionError:
|
||||
r.status_code = "Connection: "
|
||||
elif api_name.lower() == 'llama':
|
||||
requests.status_code = "Connection: "
|
||||
elif api_name.lower() == "llama":
|
||||
token = llama_api_key
|
||||
llama_ip = llama_api_IP
|
||||
try:
|
||||
logging.debug("Main: Trying to summarize with Llama.cpp")
|
||||
token = llama_api_key
|
||||
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)
|
||||
logging.debug(f"MAIN: Trying to summarize with Llama.cpp")
|
||||
summary = summarize_with_llama(llama_ip, json_file_path, token, custom_prompt)
|
||||
except requests.exceptions.ConnectionError:
|
||||
r.status_code = "Connection: "
|
||||
elif api_name.lower() == 'kobold':
|
||||
requests.status_code = "Connection: "
|
||||
elif api_name.lower() == "kobold":
|
||||
token = kobold_api_key
|
||||
kobold_ip = kobold_api_IP
|
||||
try:
|
||||
logging.debug("Main: Trying to summarize with kobold.cpp")
|
||||
token = kobold_api_key
|
||||
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)
|
||||
logging.debug(f"MAIN: Trying to summarize with kobold.cpp")
|
||||
summary = summarize_with_kobold(kobold_ip, json_file_path, custom_prompt)
|
||||
except requests.exceptions.ConnectionError:
|
||||
r.status_code = "Connection: "
|
||||
elif api_name.lower() == 'ooba':
|
||||
requests.status_code = "Connection: "
|
||||
elif api_name.lower() == "ooba":
|
||||
token = ooba_api_key
|
||||
ooba_ip = ooba_api_IP
|
||||
try:
|
||||
logging.debug("Main: Trying to summarize with oobabooga")
|
||||
token = ooba_api_key
|
||||
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)
|
||||
logging.debug(f"MAIN: Trying to summarize with oobabooga")
|
||||
summary = summarize_with_oobabooga(ooba_ip, json_file_path, custom_prompt)
|
||||
except requests.exceptions.ConnectionError:
|
||||
r.status_code = "Connection: "
|
||||
if api_name.lower() == 'huggingface':
|
||||
requests.status_code = "Connection: "
|
||||
elif api_name.lower() == "huggingface":
|
||||
api_key = huggingface_api_key
|
||||
try:
|
||||
logging.debug("MAIN: Trying to summarize with huggingface")
|
||||
api_key = huggingface_api_key
|
||||
logging.debug(f"huggingface: huggingface API Key: {api_key}")
|
||||
summarize_with_huggingface(api_key, json_file_path)
|
||||
logging.debug(f"MAIN: Trying to summarize with huggingface")
|
||||
summarize_with_huggingface(api_key, json_file_path, custom_prompt)
|
||||
except requests.exceptions.ConnectionError:
|
||||
r.status_code = "Connection: "
|
||||
requests.status_code = "Connection: "
|
||||
|
||||
else:
|
||||
logging.warning(f"Unsupported API: {api_name}")
|
||||
|
||||
517
summarize.py
517
summarize.py
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user