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|
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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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|
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| load_dotenv()
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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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| user_input = st.text_area("Email/Text Input", "", height=200)
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|
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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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|
