Spaces:
Sleeping
Sleeping
File size: 11,124 Bytes
fa9a583 ed28876 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 |
# Article_Summarization_Lib.py
#########################################
# Article Summarization Library
# This library is used to handle summarization of articles.
#
####
#
####################
# Function List
#
# 1.
#
####################
#
# Import necessary libraries
import datetime
from datetime import datetime
import gradio as gr
import json
import os
import logging
import requests
# 3rd-Party Imports
from tqdm import tqdm
from App_Function_Libraries.Utils import sanitize_filename
# Local Imports
from Article_Extractor_Lib import scrape_article
from Local_Summarization_Lib import summarize_with_llama, summarize_with_oobabooga, summarize_with_tabbyapi, \
summarize_with_vllm, summarize_with_kobold, save_summary_to_file, summarize_with_local_llm
from Summarization_General_Lib import summarize_with_openai, summarize_with_anthropic, summarize_with_cohere, \
summarize_with_groq, summarize_with_openrouter, summarize_with_deepseek, summarize_with_huggingface, \
summarize_with_mistral
from App_Function_Libraries.DB_Manager import ingest_article_to_db
#
#######################################################################################################################
# Function Definitions
#
def scrape_and_summarize_multiple(urls, custom_prompt_arg, api_name, api_key, keywords, custom_article_titles, system_message=None):
urls = [url.strip() for url in urls.split('\n') if url.strip()]
custom_titles = custom_article_titles.split('\n') if custom_article_titles else []
results = []
errors = []
# Create a progress bar
progress = gr.Progress()
for i, url in tqdm(enumerate(urls), total=len(urls), desc="Processing URLs"):
custom_title = custom_titles[i] if i < len(custom_titles) else None
try:
result = scrape_and_summarize(url, custom_prompt_arg, api_name, api_key, keywords, custom_title, system_message)
results.append(f"Results for URL {i + 1}:\n{result}")
except Exception as e:
error_message = f"Error processing URL {i + 1} ({url}): {str(e)}"
errors.append(error_message)
results.append(f"Failed to process URL {i + 1}: {url}")
# Update progress
progress((i + 1) / len(urls), desc=f"Processed {i + 1}/{len(urls)} URLs")
# Combine results and errors
combined_output = "\n".join(results)
if errors:
combined_output += "\n\nErrors encountered:\n" + "\n".join(errors)
return combined_output
def scrape_and_summarize(url, custom_prompt_arg, api_name, api_key, keywords, custom_article_title, system_message=None):
try:
# Step 1: Scrape the article
article_data = scrape_article(url)
print(f"Scraped Article Data: {article_data}") # Debugging statement
if not article_data:
return "Failed to scrape the article."
# Use the custom title if provided, otherwise use the scraped title
title = custom_article_title.strip() if custom_article_title else article_data.get('title', 'Untitled')
author = article_data.get('author', 'Unknown')
content = article_data.get('content', '')
ingestion_date = datetime.now().strftime('%Y-%m-%d')
print(f"Title: {title}, Author: {author}, Content Length: {len(content)}") # Debugging statement
# Custom system prompt for the article
system_message = system_message or "Act as a professional summarizer and summarize this article."
# Custom prompt for the article
article_custom_prompt = custom_prompt_arg or "Act as a professional summarizer and summarize this article."
# Step 2: Summarize the article
summary = None
if api_name:
logging.debug(f"Article_Summarizer: Summarization being performed by {api_name}")
# Sanitize filename for saving the JSON file
sanitized_title = sanitize_filename(title)
json_file_path = os.path.join("Results", f"{sanitized_title}_segments.json")
with open(json_file_path, 'w') as json_file:
json.dump([{'text': content}], json_file, indent=2)
# FIXME - Swap out this if/else to use the dedicated function....
try:
if api_name.lower() == 'openai':
# def summarize_with_openai(api_key, input_data, custom_prompt_arg)
summary = summarize_with_openai(api_key, json_file_path, article_custom_prompt, system_message)
elif api_name.lower() == "anthropic":
# def summarize_with_anthropic(api_key, input_data, model, custom_prompt_arg, max_retries=3, retry_delay=5):
summary = summarize_with_anthropic(api_key, json_file_path, article_custom_prompt, system_message)
elif api_name.lower() == "cohere":
# def summarize_with_cohere(api_key, input_data, model, custom_prompt_arg)
summary = summarize_with_cohere(api_key, json_file_path, article_custom_prompt, system_message)
elif api_name.lower() == "groq":
logging.debug(f"MAIN: Trying to summarize with groq")
# def summarize_with_groq(api_key, input_data, model, custom_prompt_arg):
summary = summarize_with_groq(api_key, json_file_path, article_custom_prompt, system_message)
elif api_name.lower() == "openrouter":
logging.debug(f"MAIN: Trying to summarize with OpenRouter")
# def summarize_with_openrouter(api_key, input_data, custom_prompt_arg):
summary = summarize_with_openrouter(api_key, json_file_path, article_custom_prompt, system_message)
elif api_name.lower() == "deepseek":
logging.debug(f"MAIN: Trying to summarize with DeepSeek")
# def summarize_with_deepseek(api_key, input_data, custom_prompt_arg):
summary = summarize_with_deepseek(api_key, json_file_path, article_custom_prompt, system_message)
elif api_name.lower() == "mistral":
summary = summarize_with_mistral(api_key, json_file_path, article_custom_prompt, system_message)
elif api_name.lower() == "llama.cpp":
logging.debug(f"MAIN: Trying to summarize with Llama.cpp")
# def summarize_with_llama(api_url, file_path, token, custom_prompt)
summary = summarize_with_llama(json_file_path, article_custom_prompt, system_message)
elif api_name.lower() == "kobold":
logging.debug(f"MAIN: Trying to summarize with Kobold.cpp")
# def summarize_with_kobold(input_data, kobold_api_token, custom_prompt_input, api_url):
summary = summarize_with_kobold(json_file_path, api_key, article_custom_prompt, system_message)
elif api_name.lower() == "ooba":
# def summarize_with_oobabooga(input_data, api_key, custom_prompt, api_url):
summary = summarize_with_oobabooga(json_file_path, api_key, article_custom_prompt, system_message)
elif api_name.lower() == "tabbyapi":
# def summarize_with_tabbyapi(input_data, tabby_model, custom_prompt_input, api_key=None, api_IP):
summary = summarize_with_tabbyapi(json_file_path, article_custom_prompt, system_message)
elif api_name.lower() == "vllm":
logging.debug(f"MAIN: Trying to summarize with VLLM")
# def summarize_with_vllm(api_key, input_data, custom_prompt_input):
summary = summarize_with_vllm(json_file_path, article_custom_prompt, system_message)
elif api_name.lower() == "local-llm":
logging.debug(f"MAIN: Trying to summarize with Local LLM")
summary = summarize_with_local_llm(json_file_path, article_custom_prompt, system_message)
elif api_name.lower() == "huggingface":
logging.debug(f"MAIN: Trying to summarize with huggingface")
# def summarize_with_huggingface(api_key, input_data, custom_prompt_arg):
summarize_with_huggingface(api_key, json_file_path, article_custom_prompt, system_message)
# Add additional API handlers here...
except requests.exceptions.ConnectionError as e:
logging.error(f"Connection error while trying to summarize with {api_name}: {str(e)}")
if summary:
logging.info(f"Article_Summarizer: Summary generated using {api_name} API")
save_summary_to_file(summary, json_file_path)
else:
summary = "Summary not available"
logging.warning(f"Failed to generate summary using {api_name} API")
else:
summary = "Article Summarization: No API provided for summarization."
print(f"Summary: {summary}") # Debugging statement
# Step 3: Ingest the article into the database
ingestion_result = ingest_article_to_db(url, title, author, content, keywords, summary, ingestion_date,
article_custom_prompt)
return f"Title: {title}\nAuthor: {author}\nIngestion Result: {ingestion_result}\n\nSummary: {summary}\n\nArticle Contents: {content}"
except Exception as e:
logging.error(f"Error processing URL {url}: {str(e)}")
return f"Failed to process URL {url}: {str(e)}"
def ingest_unstructured_text(text, custom_prompt, api_name, api_key, keywords, custom_article_title, system_message=None):
title = custom_article_title.strip() if custom_article_title else "Unstructured Text"
author = "Unknown"
ingestion_date = datetime.now().strftime('%Y-%m-%d')
# Summarize the unstructured text
if api_name:
json_file_path = f"Results/{title.replace(' ', '_')}_segments.json"
with open(json_file_path, 'w') as json_file:
json.dump([{'text': text}], json_file, indent=2)
if api_name.lower() == 'openai':
summary = summarize_with_openai(api_key, json_file_path, custom_prompt, system_message)
# Add other APIs as needed
else:
summary = "Unsupported API."
else:
summary = "No API provided for summarization."
# Ingest the unstructured text into the database
ingestion_result = ingest_article_to_db('Unstructured Text', title, author, text, keywords, summary, ingestion_date,
custom_prompt)
return f"Title: {title}\nSummary: {summary}\nIngestion Result: {ingestion_result}"
#
#
####################################################################################################################### |