Create main.py

Author: petermartens98

Diff for: AppV1/main.py

@@ -0,0 +1,63 @@
+import os
+from dotenv import load_dotenv
+import streamlit as st
+import openai
+from langchain.llms import OpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+
+def init_ses_states():
+    if 'chatHistory' not in st.session_state: 
+        st.session_state['chatHistory'] = []
+
+
+def sidebar():
+    global language, scenario, temperature
+    languages = ['Python',
+                'JavaScript',
+                'GoLang',
+                'C',
+                'C++',
+                'C#'
+                ]
+    scenarios = ['Code Correction',
+                'Snippet Completion',
+                ]
+    with st.sidebar:
+        with st.expander(label="Settings", expanded=True):
+            language = st.selectbox(label="Language", options=languages)
+            scenario = st.selectbox(label="Scenario", options=scenarios)
+            temperature = st.slider(label="Temperature", min_value=0.0, max_value=1.0,value=0.5)
+
+
+def main():
+    st.set_page_config(page_title="GPT-4 Coding Asssistant", page_icon="computer")
+    init_ses_states()
+    sidebar()
+    st.title("GPT-4 Coding Assistant")
+    st.caption("Powered by OpenAI, LangChain, Streamlit")
+    template = PromptTemplate(
+        input_variables=['input','language','scenario'],
+        template='''
+        You are an AI Coding Assistant specializing in the "{language}" programming language.
+        \nThe user has specified the mode to "{scenario}"
+        \nUSER {language} CODE INPUT: 
+        \n"{input}"
+        '''
+    )
+    memory = ConversationBufferMemory(input_key="input", memory_key="chat_history")
+    llm = OpenAI(temperature=temperature, model_name="gpt-4")
+    llm_chain = LLMChain(llm=llm, prompt=template, memory=memory)
+    user_input = st.text_area(label=f"Input {language} Code", height=400)
+    if (st.button('Submit') and user_input):
+        with st.spinner('Generating Response...'):   
+            response = llm_chain.run(input=user_input, language=language, scenario=scenario)
+            st.write(response)
+
+
+if __name__ == '__main__':
+    load_dotenv()
+    main()
+