Create app.py

app.py

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+import traceback
+import streamlit as st
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_openai import ChatOpenAI
+from langchain_anthropic import ChatAnthropic
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+###### dotenv を利用する場合 ######
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+except ImportError:
+    import warnings
+    warnings.warn("dotenv not found. Please make sure to set your environment variables manually.", ImportWarning)
+################################################
+
+
+PROMPT = """
+You are an AI language model that helps users generate email replies. Given the context of an email conversation, you will create a well-structured, appropriate response based on the provided inputs. The response should match the specified tone and length.
+
+Input:
+1. Sender: The person sending the email (e.g., boss, client, etc.)
+2. Email Subject: The subject of the email (e.g., About scheduling a meeting)
+3. Email Message: The content of the sender's email (e.g., I would like to adjust the time for tomorrow's meeting, are you available in the afternoon?)
+4. What you want to say: The desired response (e.g., I am available after 2 PM.)
+5. Length: The desired length of the response (e.g., Within 100 characters)
+
+Output:
+Generate a reply that addresses the sender's message, incorporates the user's desired response, and maintains a professional tone.
+
+Examples:
+ Sender: Client
+   Email Subject: About scheduling a meeting
+   Email Message: I would like to adjust the time for tomorrow's meeting, are you available in the afternoon?
+   What you want to say: I am available after 2 PM.
+   Length: 100 characters
+   Generated Reply: Dear [Client's Name], Thank you for reaching out. I am available after 2 PM tomorrow for the meeting. Please let me know if this time works for you. Best regards, [Your Name]
+
+Please generate a reply based on the provided inputs.
+---
+- Sender: {sender},
+- Email Subject : {subject},
+- Content of the recipient's email:{message},
+- What you want to say:{reply},
+---
+"""
+
+def init_page():
+    st.set_page_config(
+        page_title="Email Reply AI Agent",
+        page_icon="✉️"
+    )
+    st.header("Email Reply AI Agent ✉️")
+
+
+def select_model(temperature=0):
+    models = ("GPT-4o","GPT-4o-mini", "Claude 3.5 Sonnet", "Gemini 1.5 Pro")
+    model_choice = st.radio("Choose a model:", models)
+    if model_choice == "GPT-4o":
+        return ChatOpenAI(temperature=temperature, model_name="gpt-4o")
+    elif model_choice == "GPT-4o-mini":
+        return ChatOpenAI(temperature=temperature, model_name="gpt-4o-mini")
+    elif model_choice == "Claude 3.5 Sonnet":
+        return ChatAnthropic(temperature=temperature, model_name="claude-3-5-sonnet-20240620")
+    elif model_choice == "Gemini 1.5 Pro":
+        return ChatGoogleGenerativeAI(temperature=temperature, model="gemini-1.5-pro-latest")
+
+def init_chain():
+    llm = select_model()
+    prompt = ChatPromptTemplate.from_messages([
+        ("user", PROMPT),
+    ])
+    output_parser = StrOutputParser()
+    chain = prompt | llm | output_parser
+    return chain
+
+def main():
+    init_page()
+    chain = init_chain()
+    if chain:
+        sender = st.selectbox("Sender",("Co-worker", "Boss", "Client", "Friend"),key="sender")
+        subject = st.text_input("Email Subject (e.g., About scheduling a meeting)", key="subject")
+        message = st.text_area("Content of the recipient's email: (e.g., I would like to adjust the time for tomorrow's meeting, are you available in the afternoon?)", key="message")
+        reply = st.text_input("What you want to say: (e.g., I am available after 2 PM.)", key="reply")
+        if st.button("返信を生成する"):
+            result = chain.stream({"sender": sender, "subject": subject, "message": message, "reply": reply})
+            st.write(result)   
+      
+
+if __name__ == '__main__':
+    main()
+
+# Style adjustments (optional, remove if not needed)
+st.markdown(
+"""
+<style>
+/* Custom style adjustments */
+.st-emotion-cache-iiif1v { display: none !important; }
+</style>
+""",
+    unsafe_allow_html=True,
+)