Add application file

app.py

@@ -0,0 +1,76 @@
+import os
+import re
+import streamlit as st
+from dotenv import load_dotenv
+from langchain.agents.openai_assistant import OpenAIAssistantRunnable
+
+# Load environment variables
+load_dotenv()
+api_key = os.getenv("OPENAI_API_KEY")
+extractor_agent = os.getenv("ASSISTANT_ID_SOLUTION_SPECIFIER_A")
+
+# Create the assistant
+extractor_llm = OpenAIAssistantRunnable(
+    assistant_id=extractor_agent,
+    api_key=api_key,
+    as_agent=True
+)
+
+def remove_citation(text: str) -> str:
+    pattern = r"【\d+†\w+】"
+    return re.sub(pattern, "📚", text)
+
+# Initialize session state for messages and thread_id
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+if "thread_id" not in st.session_state:
+    st.session_state["thread_id"] = None
+
+st.title("Solution Specifier A")
+
+def predict(user_input: str) -> str:
+    """
+    This function calls our OpenAIAssistantRunnable to get a response.
+    If we don't have a thread_id yet, we create a new thread on the first call.
+    Otherwise, we continue the existing thread.
+    """
+    if st.session_state["thread_id"] is None:
+        response = extractor_llm.invoke({"content": user_input})
+        st.session_state["thread_id"] = response.thread_id
+    else:
+        response = extractor_llm.invoke(
+            {"content": user_input, "thread_id": st.session_state["thread_id"]}
+        )
+    output = response.return_values["output"]
+    return remove_citation(output)
+
+# Display any existing messages (from a previous run or refresh)
+for msg in st.session_state["messages"]:
+    if msg["role"] == "user":
+        with st.chat_message("user"):
+            st.write(msg["content"])
+    else:
+        with st.chat_message("assistant"):
+            st.write(msg["content"])
+
+# Create the chat input widget at the bottom of the page
+user_input = st.chat_input("Type your message here...")
+
+# When the user hits ENTER on st.chat_input
+if user_input:
+    # Add the user message to session state
+    st.session_state["messages"].append({"role": "user", "content": user_input})
+    
+    # Display the user's message
+    with st.chat_message("user"):
+        st.write(user_input)
+        
+    # Get the assistant's response
+    response_text = predict(user_input)
+    
+    # Add the assistant response to session state
+    st.session_state["messages"].append({"role": "assistant", "content": response_text})
+    
+    # Display the assistant's reply
+    with st.chat_message("assistant"):
+        st.write(response_text)