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Create app.py
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app.py
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import traceback
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import streamlit as st
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from langchain_openai import ChatOpenAI
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from langchain_anthropic import ChatAnthropic
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from langchain_google_genai import ChatGoogleGenerativeAI
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###### dotenv を利用する場合 ######
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try:
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from dotenv import load_dotenv
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load_dotenv()
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except ImportError:
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import warnings
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warnings.warn("dotenv not found. Please make sure to set your environment variables manually.", ImportWarning)
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################################################
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PROMPT = """
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You are an AI assistant specializing in anger management. Your task is to provide thoughtful, practical, and effective advice to help the user manage their anger based on:
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- Who the user is angry at: {who}
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- The specific situation that caused the anger: {content}
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1. Acknowledge the user's feelings to validate their emotions.
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2. Provide practical steps or techniques to manage and reduce their anger.
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3. Encourage the user to address the underlying issue in a constructive manner.
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4. Offer additional resources or tips for similar situations in the future.
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"""
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def init_page():
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st.set_page_config(
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page_title="Anger Management - AI Agent Navi",
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page_icon="🧘"
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)
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st.header("Anger Management - AI Agent Navi🧘")
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def select_model(temperature=0):
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models = ("GPT-4o","GPT-4o-mini", "Claude 3.5 Sonnet", "Gemini 1.5 Pro")
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model_choice = st.radio("Choose a model:", models)
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if model_choice == "GPT-4o":
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return ChatOpenAI(temperature=temperature, model_name="gpt-4o")
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elif model_choice == "GPT-4o-mini":
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return ChatOpenAI(temperature=temperature, model_name="gpt-4o-mini")
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elif model_choice == "Claude 3.5 Sonnet":
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return ChatAnthropic(temperature=temperature, model_name="claude-3-5-sonnet-20240620")
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elif model_choice == "Gemini 1.5 Pro":
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return ChatGoogleGenerativeAI(temperature=temperature, model="gemini-1.5-pro-latest")
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def init_chain():
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llm = select_model()
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prompt = ChatPromptTemplate.from_messages([
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("user", PROMPT),
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])
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output_parser = StrOutputParser()
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chain = prompt | llm | output_parser
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return chain
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def main():
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init_page()
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chain = init_chain()
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if chain:
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who = st.text_input("Who are you angry at?", key="who")
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content = st.text_area("What specific situation caused the anger?", key="content")
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if st.button("Submit"):
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result = chain.stream({"who": who, "content": content})
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st.write(result)
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if __name__ == '__main__':
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main()
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# Style adjustments (optional, remove if not needed)
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st.markdown(
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"""
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<style>
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/* Custom style adjustments */
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.st-emotion-cache-iiif1v { display: none !important; }
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</style>
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""",
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unsafe_allow_html=True,
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)
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