Update app.py
Browse files
app.py
CHANGED
@@ -2,7 +2,6 @@ import streamlit as st
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import requests
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from transformers import pipeline
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import pandas as pd
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import matplotlib.pyplot as plt
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# Step 1: ๋ค์ด๋ฒ ๋ด์ค API ํธ์ถ ํจ์
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def fetch_naver_news(query, display=10, start=1, sort="date"):
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@@ -29,21 +28,26 @@ def fetch_naver_news(query, display=10, start=1, sort="date"):
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st.error(f"Error: {response.status_code}, {response.text}")
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return None
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# Step 2:
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def
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# Step 3:
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def generate_article_by_perspective(text, perspective="์ง๋ณด"):
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#
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if perspective == "์ง๋ณด":
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elif perspective == "๋ณด์":
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# Step 4: ์ ์น ์ฑํฅ ๋ถ๋ฅ ํจ์
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def classify_political_sentiment(text, classifier):
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@@ -65,7 +69,7 @@ def classify_political_sentiment(text, classifier):
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return "์ค๋ฆฝ", sentiment_score
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# Step 5: ๋ด์ค ๋ถ์ ๋ฐ ๊ฒฐ๊ณผ ์ถ๋ ฅ
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def analyze_news_political_orientation(news_items, classifier):
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results = {"์ง๋ณด": 0, "๋ณด์": 0, "์ค๋ฆฝ": 0}
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detailed_results = []
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@@ -77,18 +81,24 @@ def analyze_news_political_orientation(news_items, classifier):
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# ์ ์น ์ฑํฅ ๋ถ๋ฅ
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orientation, score = classify_political_sentiment(combined_text, classifier)
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results[orientation] += 1
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detailed_results.append({
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"์ ๋ชฉ": title,
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"์์ฝ": description,
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"์ฑํฅ": orientation,
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"์ ์": score,
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"๋งํฌ": item["link"],
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"์ง๋ณด์ ๊ธฐ์ฌ":
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"๋ณด์์ ๊ธฐ์ฌ":
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})
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return results, detailed_results
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# Step 6: Streamlit ์ฑ ์์
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st.title("์ ์น ์ฑํฅ ๋ถ์ ๋์๋ณด๋")
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st.markdown("### ๋ค์ด๋ฒ ๋ด์ค ๋ฐ์ดํฐ๋ฅผ ์ค์๊ฐ์ผ๋ก ์์งํ๊ณ ์ ์น ์ฑํฅ์ ๋ถ์ํฉ๋๋ค.")
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@@ -109,9 +119,10 @@ if st.button("๋ถ์ ์์"):
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# Hugging Face ๋ชจ๋ธ ๋ก๋
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classifier = load_huggingface_model()
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# ๋ด์ค ๋ฐ์ดํฐ ๋ถ์
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results, detailed_results = analyze_news_political_orientation(news_items, classifier)
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# ๋ถ์ ๊ฒฐ๊ณผ ์๊ฐํ
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st.subheader("๋ถ์ ๊ฒฐ๊ณผ ์์ฝ")
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import requests
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from transformers import pipeline
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import pandas as pd
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# Step 1: ๋ค์ด๋ฒ ๋ด์ค API ํธ์ถ ํจ์
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def fetch_naver_news(query, display=10, start=1, sort="date"):
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st.error(f"Error: {response.status_code}, {response.text}")
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return None
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# Step 2: GPT ๋ชจ๋ธ ๋ก๋ (์ง๋ณด์ , ๋ณด์์ ๊ธฐ์ฌ ์์ฑ)
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def load_gpt_model():
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# GPT ๋ชจ๋ธ ๋ก๋ (์ด ๋ชจ๋ธ์ ํ
์คํธ ์์ฑ์ ์ฌ์ฉ๋ฉ๋๋ค)
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gpt_model = pipeline("text-generation", model="gpt2") # Hugging Face์์ ์ฌ์ฉํ ์ ์๋ GPT ๋ชจ๋ธ
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return gpt_model
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# Step 3: ๊ธฐ์ฌ๋ฅผ ์ง๋ณด์ ๋ฐ ๋ณด์์ ๊ด์ ์์ ์์ฑํ๋ ํจ์
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def generate_article_by_perspective(text, perspective="์ง๋ณด"):
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# ์ง๋ณด์ ๊ด์ ์์ฑ
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if perspective == "์ง๋ณด":
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prompt = f"์ง๋ณด์ ๊ด์ ์์ ๋ค์ ๊ธฐ์ฌ๋ฅผ ์์ฑํด ์ฃผ์ธ์: {text}. ํ๊ฒฝ๊ณผ ์ฌํ์ ์ฑ
์์ ๊ฐ์กฐํ๋ฉฐ..."
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# ๋ณด์์ ๊ด์ ์์ฑ
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elif perspective == "๋ณด์":
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prompt = f"๋ณด์์ ๊ด์ ์์ ๋ค์ ๊ธฐ์ฌ๋ฅผ ์์ฑํด ์ฃผ์ธ์: {text}. ๊ฒฝ์ ์ฑ์ฅ๊ณผ ๊ตญ๊ฐ ์๋ณด๋ฅผ ๊ฐ์กฐํ๋ฉฐ..."
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else:
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prompt = text
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# GPT ๋ชจ๋ธ์ ์ฌ์ฉํ์ฌ ํ
์คํธ ์์ฑ
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generated_article = gpt_model(prompt, max_length=512, num_return_sequences=1)[0]['generated_text']
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return generated_article
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# Step 4: ์ ์น ์ฑํฅ ๋ถ๋ฅ ํจ์
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def classify_political_sentiment(text, classifier):
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return "์ค๋ฆฝ", sentiment_score
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# Step 5: ๋ด์ค ๋ถ์ ๋ฐ ๊ฒฐ๊ณผ ์ถ๋ ฅ
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def analyze_news_political_orientation(news_items, classifier, gpt_model):
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results = {"์ง๋ณด": 0, "๋ณด์": 0, "์ค๋ฆฝ": 0}
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detailed_results = []
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# ์ ์น ์ฑํฅ ๋ถ๋ฅ
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orientation, score = classify_political_sentiment(combined_text, classifier)
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results[orientation] += 1
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# ์ง๋ณด์ /๋ณด์์ ๊ธฐ์ฌ ์์ฑ
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progressive_article = generate_article_by_perspective(combined_text, "์ง๋ณด")
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conservative_article = generate_article_by_perspective(combined_text, "๋ณด์")
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detailed_results.append({
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"์ ๋ชฉ": title,
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"์์ฝ": description,
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"์ฑํฅ": orientation,
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"์ ์": score,
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"๋งํฌ": item["link"],
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"์ง๋ณด์ ๊ธฐ์ฌ": progressive_article,
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"๋ณด์์ ๊ธฐ์ฌ": conservative_article
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})
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return results, detailed_results
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# Step 6: Streamlit ์ฑ ์์
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st.title("์ ์น ์ฑํฅ ๋ถ์ ๋์๋ณด๋")
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st.markdown("### ๋ค์ด๋ฒ ๋ด์ค ๋ฐ์ดํฐ๋ฅผ ์ค์๊ฐ์ผ๋ก ์์งํ๊ณ ์ ์น ์ฑํฅ์ ๋ถ์ํฉ๋๋ค.")
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# Hugging Face ๋ชจ๋ธ ๋ก๋
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classifier = load_huggingface_model()
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gpt_model = load_gpt_model()
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# ๋ด์ค ๋ฐ์ดํฐ ๋ถ์
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results, detailed_results = analyze_news_political_orientation(news_items, classifier, gpt_model)
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# ๋ถ์ ๊ฒฐ๊ณผ ์๊ฐํ
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st.subheader("๋ถ์ ๊ฒฐ๊ณผ ์์ฝ")
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