Upload app.py
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app.py
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import os
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import pandas as pd
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import numpy as np
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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import gradio as gr
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from google import generativeai as genai
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# API ํค๋ฅผ Hugging Face Spaces์ Repository secrets์์ ๊ฐ์ ธ์ต๋๋ค.
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API_KEY = os.getenv("GOOGLE_API_KEY")
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if API_KEY:
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genai.configure(api_key=API_KEY)
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print("API ํค๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์ค์ ๋์์ต๋๋ค.")
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else:
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raise ValueError("API ํค๊ฐ ์ค์ ๋์ง ์์์ต๋๋ค. Hugging Face Spaces์ Repository secrets์ 'GOOGLE_API_KEY'๋ฅผ ์ค์ ํด์ฃผ์ธ์.")
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# ๋ฐ์ดํฐ์
๋ก๋
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df = pd.read_csv('https://raw.githubusercontent.com/kairess/mental-health-chatbot/master/wellness_dataset_original.csv')
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df = df.drop(columns=['Unnamed: 3'], errors='ignore')
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df = df.dropna(subset=['์ ์ ', '์ฑ๋ด'])
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# ์๋ฒ ๋ฉ ๋ชจ๋ธ ๋ก๋
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model = SentenceTransformer('jhgan/ko-sbert-nli')
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# ๋ฐ์ดํฐ์
์๋ฒ ๋ฉ ๋ฏธ๋ฆฌ ๊ณ์ฐ
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print("๋ฐ์ดํฐ์
์๋ฒ ๋ฉ์ ๋ฏธ๋ฆฌ ๊ณ์ฐ ์ค์
๋๋ค. ์ด ๊ณผ์ ์ ์๊ฐ์ด ์์๋ฉ๋๋ค...")
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df['embedding'] = df['์ ์ '].apply(lambda x: model.encode(x))
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print("์๋ฒ ๋ฉ ๊ณ์ฐ์ด ์๋ฃ๋์์ต๋๋ค! ์ด์ ์ฑ๋ด ์๋ต์ด ํจ์ฌ ๋นจ๋ผ์ง๋๋ค.")
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# Gemini API ํธ์ถ ํจ์
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def call_gemini_api(question):
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try:
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llm_model = genai.GenerativeModel('gemini-pro')
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response = llm_model.generate_content(question)
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return response.text
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except Exception as e:
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print(f"API ํธ์ถ ์ค ์ค๋ฅ ๋ฐ์: {e}")
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return f"์ฃ์กํฉ๋๋ค. API ํธ์ถ ์ค ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค: {e}"
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# ์ ์ฌ๋ ์๊ณ๊ฐ
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COSINE_SIMILARITY_THRESHOLD = 0.8
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# ์ฑ๋ด ํต์ฌ ๋ก์ง
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def chatbot(user_question):
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try:
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user_embedding = model.encode(user_question)
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similarities = df['embedding'].apply(lambda x: cosine_similarity([user_embedding], [x])[0][0])
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best_match_index = similarities.idxmax()
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best_score = similarities.loc[best_match_index]
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best_match_row = df.loc[best_match_index]
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if best_score >= COSINE_SIMILARITY_THRESHOLD:
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answer = best_match_row['์ฑ๋ด']
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print(f"์ ์ฌ๋ ๊ธฐ๋ฐ ๋ต๋ณ. ์ ์: {best_score}")
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return answer
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else:
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print(f"์ ์ฌ๋ ์๊ณ๊ฐ({COSINE_SIMILARITY_THRESHOLD}) ๋ฏธ๋ง. Gemini ๋ชจ๋ธ์ ํธ์ถํฉ๋๋ค. ์ ์: {best_score}")
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return call_gemini_api(user_question)
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except Exception as e:
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print(f"์ฑ๋ด ์คํ ์ค ์ค๋ฅ ๋ฐ์: {e}")
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return f"์ฃ์กํฉ๋๋ค. ์ฑ๋ด ์คํ ์ค ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค: {e}"
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# Gradio ์ธํฐํ์ด์ค ์ ์
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demo = gr.Interface(
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fn=chatbot,
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inputs=gr.Textbox(lines=2, placeholder="์ง๋ฌธ์ ์
๋ ฅํด ์ฃผ์ธ์...", label="์ง๋ฌธ", elem_id="user_question_input"),
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outputs=gr.Textbox(lines=5, label="์ฑ๋ด ๋ต๋ณ"),
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title="๋๋ ์๋ด ์ฑ๋ด",
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description="5๋ถ ๋์ ๋ํํ์ฌ ์ฃผ์๊ณ ๋ค์์ ๋งํฌ๋ฅผ ํด๋ฆญํ์ฌ ๊ผญ ์ค๋ฌธ์กฐ์ฌ์ ์ฐธ์ฌํด์ฃผ์ธ์! https://forms.gle/eWtyejQaQntKbbxG8"
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)
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# Hugging Face Spaces ํ๊ฒฝ์ ๋ง์ถฐ server_name๊ณผ server_port๋ฅผ ๋ช
์ํฉ๋๋ค.
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demo.launch(server_name="0.0.0.0", server_port=7860, share=False)
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