added support for more gpt types

app.py

@@ -16,29 +16,37 @@ DATA_AVAL: bool = schemer.count() > 0
 APP_NAME: str = "Groove-GPT"
 
 st.title(APP_NAME)
+st.header("What is Groovy-GPT?")
+st.write("Groovy-GPT is a RAG (Retrieval-Augmented Generation) model that uses ChromaDB to retrieve relevant documents and then uses OpenAI's models to generate a response.")
+st.write("The model is trained on the MIT Scheme textbook and a handful of Discrete Math and Paradigms related content that Professor Gertner posted")
 st.write("Data Avaliable: ", DATA_AVAL)
+
 user_question: str = st.text_area("Enter your groovy questions here")
 access_key: str = st.text_input("Enter your gpt key here", type="password")
+st.markdown("*For more information about how to get an access key, read [this article](https://platform.openai.com/api-keys).*", unsafe_allow_html=True)
+gpt_type: str = st.selectbox(label="Choose GPT Type", options=["gpt-3.5-turbo", "gpt-3.5-turbo-1106", "gpt-3.5-turbo-0125", "gpt-4-32k-0613", "gpt-4-0613", "gpt-4-0125-preview"], index=0)
+st.markdown("*For more information about GPT types, read [this article](https://platform.openai.com/docs/models).*", unsafe_allow_html=True)
 
 if st.button('Query Database') & (access_key != "") & (user_question != ""):
     openai_client = OpenAI(api_key=access_key)
 
-    st.header("Results")
-    # Perform the Chromadb query.
-    results = schemer.query(
-        query_texts=[user_question],
-        n_results=10,
-        include = ['documents']
-    )
-    documents = results["documents"]
-    response = openai_client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": "You are an expert in functional programming in Scheme, with great knowledge on programming paradigms."},
-            {"role": "user", "content": user_question},
-            {"role": "assistant", "content": str(documents)},
-        ]
-    )
+    with st.spinner('Loading...'):
+        st.header("Results")
+        # Perform the Chromadb query.
+        results = schemer.query(
+            query_texts=[user_question],
+            n_results=10,
+            include = ['documents']
+        )
+        documents = results["documents"]
+        response = openai_client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": "You are an expert in functional programming in Scheme, with great knowledge on programming paradigms."},
+                {"role": "user", "content": user_question},
+                {"role": "assistant", "content": str(documents)},
+            ]
+        )
 
     st.write(response.choices[0].message.content)