Create app.py

app.py

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+import streamlit as st
+import os
+import shelve
+from g4f.client import Client
+
+
+st.title("Streamlit Chatbot Interface")
+
+USER_AVATAR = "👤"
+BOT_AVATAR = "🤖"
+client = Client()
+
+# Ensure openai_model is initialized in session state
+if "openai_model" not in st.session_state:
+    st.session_state["openai_model"] = "gpt-3.5-turbo"
+
+
+# Load chat history from shelve file
+def load_chat_history():
+    with shelve.open("chat_history") as db:
+        return db.get("messages", [])
+
+
+# Save chat history to shelve file
+def save_chat_history(messages):
+    with shelve.open("chat_history") as db:
+        db["messages"] = messages
+
+
+# Initialize or load chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = load_chat_history()
+
+# Sidebar with a button to delete chat history
+with st.sidebar:
+    if st.button("Delete Chat History"):
+        st.session_state.messages = []
+        save_chat_history([])
+
+# Display chat messages
+for message in st.session_state.messages:
+    avatar = USER_AVATAR if message["role"] == "user" else BOT_AVATAR
+    with st.chat_message(message["role"], avatar=avatar):
+        st.markdown(message["content"])
+
+# Main chat interface
+if prompt := st.chat_input("How can I help?"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user", avatar=USER_AVATAR):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant", avatar=BOT_AVATAR):
+        message_placeholder = st.empty()
+        full_response = ""
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=st.session_state["messages"],
+        )
+        full_response = response.choices[0].message.content
+        message_placeholder.markdown(full_response)
+    st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+# Save chat history after each interaction
+save_chat_history(st.session_state.messages)