changed layout

.gitignore

@@ -1 +1,2 @@
-env/
+env/
+__pycache__/

app.py

@@ -1,114 +1,71 @@
 import streamlit as st
-import chromadb
-from chromadb.utils import embedding_functions
-from sentence_transformers import SentenceTransformer
 from openai import OpenAI
+from params import params
+from database import get_client
 
-# CONSTANTS
-client = chromadb.PersistentClient(path="./chromadb_linux/")
-MODEL_NAME: str = "mixedbread-ai/mxbai-embed-large-v1"  # ~ 0.5 gb
-COLLECTION_NAME: str = "scheme"
-EMBEDDING_FUNC = embedding_functions.SentenceTransformerEmbeddingFunction(
-    model_name=MODEL_NAME
-)
-schemer = client.get_collection(
-    name=COLLECTION_NAME,
-    embedding_function=EMBEDDING_FUNC,
-)
-DATA_AVAL: bool = schemer.count() > 0
+CLIENT = get_client()
 APP_NAME: str = "Groove-GPT"
 history = []
+st.set_page_config(layout="wide")
 
 # INFO
 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 Troeger posted"
-)
-st.write("Data Avaliable: ", DATA_AVAL)
 
-# INPUTS
-user_question: str = st.text_area("Enter your groovy questions here")
-
-remember_chat_history = st.toggle("Remember This Chat's History")
-
-temperature = st.slider(
-    label="Creativity of Model", min_value=0.0, max_value=2.0, value=0.8
-)
-st.markdown("*High creativity will make it go crazy - keep it low*")
-
-num_samples = st.slider(
-    label="Amount of References to Give to Model", min_value=10, max_value=100, value=10
-)
-st.markdown(
-    "*High amount will make it slow and expensive (and may not be relevant) - keep it low*"
-)
-
-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). Make sure it has money in it ☠️*",
-    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,
-)
-
-st.divider()
-
-# ON BUTTON CLICK
-if st.button("Start Scheming") & (access_key != "") & (user_question != ""):
-    openai_client = OpenAI(api_key=access_key)
-
-    with st.spinner("Loading..."):
-        # Perform the Chromadb query.
-        results = schemer.query(
-            query_texts=[user_question], n_results=num_samples, 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 R5RS, with great knowledge on programming paradigms. You wish to teach the user everything you know about programming paradigms in R5RS - so you explain everything thoroughly. Surround Latex equations in dollar signs as such Inline equation: $equation$ & Display equation: $$equation$$. You will focus your examples to work exclusively in interative and recursive apporaches",
-                },
-                {"role": "user", "content": user_question},
-                {"role": "assistant", "content": str(documents)},
-                {"role": "user", "content": f"Conversation History: {history}"},
-            ],
-            temperature=temperature,
-            stream=True,
-        )
-
-    # history.append({user_question : response.choices[0].message.content} if remember_chat_history else {})
-
-    st.header("The Mega Schemer Says ...")
-
-    text_placeholder = st.empty()
-
-    content = ""
-    for i, chunk in enumerate(response):
-        if chunk.choices[0].delta.content is not None:
-            # Append the chunk content to the string
-            content += chunk.choices[0].delta.content
-
-            text_placeholder.markdown(content)
-else:
-    st.write("Please provide an input and (valid) API key")
+l_col, r_col = st.columns((3, 1))
+
+# param column
+with r_col:
+    (
+        submit_button,
+        remember_chat_history,
+        temperature,
+        num_samples,
+        access_key,
+        gpt_type,
+    ) = params()
+
+# input & response
+with l_col:
+    user_question: str = st.text_input("Enter your groovy questions here")
+
+    # ON BUTTON CLICK
+    if submit_button & (access_key != "") & (user_question != ""):
+        openai_client = OpenAI(api_key=access_key)
+
+        with st.spinner("Loading..."):
+            # Perform the Chromadb query.
+            results = CLIENT.query(
+                query_texts=[user_question],
+                n_results=num_samples,
+                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 R5RS, with great knowledge on programming paradigms. You wish to teach the user everything you know about programming paradigms in R5RS - so you explain everything thoroughly. Surround Latex equations in dollar signs as such Inline equation: $equation$ & Display equation: $$equation$$.",
+                    },
+                    {"role": "user", "content": user_question},
+                    {"role": "assistant", "content": str(documents)},
+                    {"role": "user", "content": f"Conversation History: {history}"},
+                ],
+                temperature=temperature,
+                stream=True,
+            )
+
+        st.header("The Super Duper Schemer Says ...")
+        text_placeholder = st.empty()
+
+        content = ""
+        for i, chunk in enumerate(response):
+            if chunk.choices[0].delta.content is not None:
+                # Append the chunk content to the string
+                content += chunk.choices[0].delta.content
+
+                text_placeholder.markdown(content)
+
+        history.append({user_question: content} if remember_chat_history else {})
+    else:
+        st.write("Please provide an input and (valid) API key")

database.py

@@ -0,0 +1,16 @@
+import chromadb
+from chromadb.utils import embedding_functions
+
+def get_client():
+    # CONSTANTS
+    client = chromadb.PersistentClient(path="./chromadb_linux/")
+    MODEL_NAME: str = "mixedbread-ai/mxbai-embed-large-v1"  # ~ 0.5 gb
+    COLLECTION_NAME: str = "scheme"
+    EMBEDDING_FUNC = embedding_functions.SentenceTransformerEmbeddingFunction(
+        model_name=MODEL_NAME
+    )
+    schemer = client.get_collection(
+        name=COLLECTION_NAME,
+        embedding_function=EMBEDDING_FUNC,
+    )
+    return schemer

params.py

@@ -0,0 +1,67 @@
+import streamlit as st
+
+
+def params():
+    submit_button = st.button("Start Scheming")
+    
+    # API Key
+    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). Make sure it has money in it ☠️*",
+        unsafe_allow_html=True,
+    )
+
+    # Chat History
+    remember_chat_history = st.toggle("Remember This Chat's History")
+
+    # Temperature
+    temperature = st.slider(
+        label="Creativity of Model", min_value=0.0, max_value=2.0, value=0.8
+    )
+    st.markdown("*High creativity will make it go crazy - keep it low*")
+
+    # Number of Samples
+    num_samples = st.slider(
+        label="Amount of References to Give to Model",
+        min_value=1,
+        max_value=20,
+        value=10,
+    )
+    st.markdown(
+        "*High amount will make it slow and expensive (and may not be relevant) - keep it low*"
+    )
+
+    # GPT Type
+    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-4-0125-preview",
+            "gpt-4-turbo",
+        ],
+        index=0,
+    )
+    st.markdown(
+        "*For more information about GPT types, read [this article](https://platform.openai.com/docs/models).*",
+        unsafe_allow_html=True,
+    )
+
+    st.subheader("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 Troeger posted"
+    )
+
+    return (
+        submit_button,
+        remember_chat_history,
+        temperature,
+        num_samples,
+        access_key,
+        gpt_type,
+    )