142 lines
4.6 KiB
Python
142 lines
4.6 KiB
Python
import asyncio
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import aiohttp
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import time
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import re
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import pandas as pd
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import json
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from tqdm import tqdm
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class TopicRecreation:
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def __init__(self):
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self.instruction = f"""
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You will be given a tweet text.
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Your task is to write a phrase category for this tweet which tweet is related to it.
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this should be a combination of action + category :
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for example :
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انتقاد از سیاست ایران
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توهین به مقامات کشور
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حمایت از نظام جمهوری اسلامی
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جنگ اسراییل و قطر
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مسایل مربوط به موضوع هسته ای ایران
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مسایل مربوط به افغانستان
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The category should be in persian.
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# Roles
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- If it does not have specifc meaning then write "متفرقه"
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- Be specifc about the countries.
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- Do not be specifc about the people.
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- you can consider different categories and write an action + category or just simple category
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Just return the category, do not include any other text.
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"""
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async def run_llm(self, session, tweet):
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"""
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Run the LLM as reranker.
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Args:
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session: The session to use for the request.
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tweet: The tweet to rerank.
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Returns:
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The category of the tweet.
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"""
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headers = {"Content-Type": "application/json",}
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tweet = " ".join([m for m in tweet.split(" ") if "@" not in m])
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input_message = f"""{{"tweet": "{tweet}"}}"""
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messages = [{"role": "system", "content": self.instruction}, {"role": "user", "content": input_message}]
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payload = {
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"model": "google/gemma-3-27b-it",
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"messages": messages,
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"max_tokens": 500
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}
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# try:
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async with session.post("http://192.168.130.206:4001/v1/chat/completions", headers=headers, json=payload) as resp:
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resp.raise_for_status()
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response = await resp.json()
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out = response['choices'][0]['message']['content']
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return out
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# except Exception as e:
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# print(f"Error in llm as reranker: {e}")
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# return 0
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async def run_llm_async(self, tweets):
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"""
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Send all chunk requests concurrently.
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Args:
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tweets: The tweets to rerank.
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Returns:
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The categories of the tweets.
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"""
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async with aiohttp.ClientSession() as session:
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tasks = [self.run_llm(session, tweet) for tweet in tweets]
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scores_embed = await asyncio.gather(*tasks)
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return scores_embed
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def sanitize_for_excel(self, df):
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def _sanitize_for_excel(text):
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"""Remove zero-width and bidi control characters that can confuse Excel rendering."""
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if text is None:
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return ""
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s = str(text)
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# Characters to remove: ZWNJ, ZWJ, RLM, LRM, RLE, LRE, PDF, BOM, Tatweel
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remove_chars = [
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"\u200c", # ZWNJ
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"\u200d", # ZWJ
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"\u200e", # LRM
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"\u200f", # RLM
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"\u202a", # LRE
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"\u202b", # RLE
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"\u202c", # PDF
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"\u202d", # LRO
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"\u202e", # RLO
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"\ufeff", # BOM
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"\u0640", # Tatweel
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]
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for ch in remove_chars:
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s = s.replace(ch, "")
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# Normalize whitespace
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s = re.sub(r"\s+", " ", s).strip()
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return s
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df_copy = df.copy()
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for m in ["category"]:
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for i in range(len(df_copy[m])):
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df_copy.loc[i, m] = _sanitize_for_excel(df_copy.loc[i, m])
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return df_copy
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def start_process(self, input_path, output_path):
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df = pd.read_excel(input_path)
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df_copy = df.copy()
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tweets = df["tweet"].tolist()
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for i in tqdm(range(0, len(tweets), 1000)):
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start_time = time.time()
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result_list = asyncio.run(self.run_llm_async(tweets[i:i+1000]))
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end_time = time.time()
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print(f"Time taken for llm as reranker: {end_time - start_time} seconds")
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time.sleep(5)
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for j, result in enumerate(result_list):
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df_copy.at[i+j, "category"] = result
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df_copy = self.sanitize_for_excel(df_copy)
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df_copy.to_excel(output_path)
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if __name__ == "__main__":
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llm = TopicRecreation()
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llm.start_process("/home/firouzi/trend_grouping_new/tweet_topic.xlsx", "/home/firouzi/trend_grouping_new/tweet_topic_recreation.xlsx") |