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import streamlit as st
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from langchain.llms import Ollama
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from langchain.prompts import PromptTemplate
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from langchain.chains import LLMChain
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from textblob import TextBlob
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from dotenv import load_dotenv
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load_dotenv()
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def load_model():
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model = Ollama(model='llama3.1:latest', temperature=0.2)
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return model
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def summarize_prompt():
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return PromptTemplate(
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input_variables=["email"],
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template=(
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"""
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1. Berdasarkan teks berikut, buat ringkasan singkat tentang tindakan utama yang dilakukan oleh user, ambil no resi pada text dalam format berikut:
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[Aksi User]
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2. Berikan juga analisis sentimen percakapan user dengan salah satu label:
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- Positif
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- Negatif
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- Netral
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Teks:
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{email}
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Ringkasan:
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Aksi User: [Deskripsikan tindakan utama user berdasarkan teks]
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Sentimen: [Tentukan sentimen berdasarkan nada dan konteks teks]
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"""
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)
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)
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def simple_sentiment_analysis(text):
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blob = TextBlob(text)
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sentiment = blob.sentiment.polarity
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if sentiment > 0:
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return "Positif"
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elif sentiment < 0:
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return "Negatif"
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else:
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return "Netral"
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model = load_model()
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prompt = summarize_prompt()
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llm_chain = LLMChain(llm=model, prompt=prompt)
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def analyze_email(email):
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result = llm_chain.run(email=email)
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sentiment = simple_sentiment_analysis(email)
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return result + f"\nSentimen Percakapan: {sentiment}"
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st.title("Sentiment Analysis and User Action Summarizer")
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st.write("Enter an email or text below to analyze the user's action and sentiment:")
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user_input = st.text_area("Email/Text Input", "", height=200)
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if st.button("Analyze"):
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if user_input:
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analysis_result = analyze_email(user_input)
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st.subheader("Analysis Result:")
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st.text(analysis_result)
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else:
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st.warning("Please enter some text to analyze.")
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