Create app.py

app.py

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+# Import necessary libraries
+import streamlit as st
+import openai
+import os
+
+# Set the title for the Streamlit app
+st.title("Simple Chatbot")
+
+# Load the OpenAI API key from Hugging Face's environment variables
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
+# Check if the API key is loaded
+if openai_api_key is None:
+    st.error("API key not found. Please set the OpenAI API key in the environment.")
+    st.stop()
+
+# Set the API key for OpenAI
+openai.api_key = openai_api_key
+
+# Define the template for the chatbot prompt
+prompt_template = """
+    You are a helpful Assistant who answers users' questions
+    based on your general knowledge. Keep your answers short 
+    and to the point.
+"""
+
+# Get the current prompt from the session state or set default
+if "prompt" not in st.session_state:
+    st.session_state["prompt"] = [{"role": "system", 
+                                   "content": prompt_template}]
+prompt = st.session_state["prompt"]
+
+# Display previous chat messages
+for message in prompt:
+    if message["role"] != "system":
+        with st.chat_message(message["role"]):
+            st.write(message["content"])
+
+# Get the user's question using Streamlit's chat input
+question = st.chat_input("Ask anything")
+
+# Handle the user's question
+if question:
+    # Add the user's question to the prompt and display it
+    prompt.append({"role": "user", "content": question})
+    with st.chat_message("user"):
+        st.write(question)
+
+    # Display an empty assistant message while waiting for response
+    with st.chat_message("assistant"):
+        botmsg = st.empty()
+
+    # Define a function to interact with OpenAI API
+    def chat_gpt(messages):
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=messages
+        )
+        return response.choices[0].message['content'].strip()
+
+    # Call the chat_gpt function
+    result = chat_gpt(prompt)
+
+    # Display the assistant's response
+    botmsg.write(result)
+
+    # Add the assistant's response to the prompt
+    prompt.append({"role": "assistant", "content": result})
+
+    # Store the updated prompt in the session state
+    st.session_state["prompt"] = prompt