GUI SUPPORT

gui.py

@@ -0,0 +1,91 @@
+import dotenv
+import streamlit as st
+from streamlit_chat import message
+from streamlit_extras.colored_header import colored_header
+from streamlit_extras.add_vertical_space import add_vertical_space
+import startLLM
+import os
+
+dotenv_file = dotenv.find_dotenv(".env")
+dotenv.load_dotenv()
+llama_embeddings_model = os.environ.get("LLAMA_EMBEDDINGS_MODEL")
+persist_directory = os.environ.get('PERSIST_DIRECTORY')
+model_type = os.environ.get('MODEL_TYPE')
+model_path = os.environ.get('MODEL_PATH')
+model_n_ctx = int(os.environ.get('MODEL_N_CTX'))
+model_temp = float(os.environ.get('MODEL_TEMP'))
+model_stop = os.environ.get('MODEL_STOP')
+
+# Initialization
+if "input" not in st.session_state:
+    st.session_state.input = ""
+    st.session_state.running = False
+
+st.set_page_config(page_title="CASALIOY")
+
+# Sidebar contents
+with st.sidebar:
+    st.title('CASALIOY')
+    st.markdown('''
+    ## About
+    This app is an LLM-powered chatbot built using:
+    - [Streamlit](https://streamlit.io/)
+    - [su77ungr/CASALIOY](https://github.com/alxspiker/CASALIOY) LLM Toolkit
+
+    💡 Note: No API key required!
+    Refreshing the page will restart gui.py with a fresh chat history.
+    CASALIOY will not remember previous questions as of yet.
+
+    GUI does not support live response yet, so you have to wait for the tokens to process.
+    ''')
+    add_vertical_space(5)
+    st.write('Made with ❤️ by [su77ungr/CASALIOY](https://github.com/alxspiker/CASALIOY)')
+
+if 'generated' not in st.session_state:
+    st.session_state['generated'] = ["I can help you answer questions about the documents you have ingested into the vector store."]
+
+if 'past' not in st.session_state:
+    st.session_state['past'] = ['Hi, what can you help me with!']
+
+colored_header(label='', description='', color_name='blue-30')
+response_container = st.container()
+
+
+
+def generate_response(input=""):
+    with response_container:
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            if st.number_input('Temperature', key="temp_input", value=float(model_temp), step=float(0.05), min_value=float(0), max_value=float(1)):
+                os.environ["MODEL_TEMP"] = str(st.session_state.temp_input)
+                dotenv.set_key(dotenv_file, "MODEL_TEMP", os.environ["MODEL_TEMP"])
+        with col2:
+            if st.number_input('Context', key="ctx_input", value=int(model_n_ctx), step=int(512), min_value=int(512), max_value=int(9000)):
+                os.environ["MODEL_N_CTX"] = str(st.session_state.ctx_input)
+                dotenv.set_key(dotenv_file, "MODEL_N_CTX", os.environ["MODEL_N_CTX"])
+        with col3:
+            if st.text_input('Stops', key="stops_input", value=str(model_stop)):
+                os.environ["MODEL_STOP"] = str(st.session_state.stops_input)
+                dotenv.set_key(dotenv_file, "MODEL_STOP", os.environ["MODEL_STOP"])
+        #with st.form("my_form", clear_on_submit=True):
+        if st.session_state['generated']:
+            for i in range(len(st.session_state['generated'])):
+                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user')
+                message(st.session_state["generated"][i], key=str(i))
+        if input.strip() != "":
+            st.session_state.running=True
+            st.session_state.past.append(st.session_state.input)
+            if st.session_state.running:
+                message(st.session_state.input, is_user=True)
+                message("Loading response. Please wait for me to finish before refreshing the page...", key="rmessage")
+                #startLLM.qdrant = None #Not sure why this fixes db error
+                response = startLLM.main(st.session_state.input, True)
+                st.session_state.generated.append(response)
+                message(response)
+                st.session_state.running = False
+        st.text_input("You: ", "", key="input", disabled=st.session_state.running)
+
+
+with st.form("my_form", clear_on_submit=True):
+    st.form_submit_button('SUBMIT', on_click=generate_response(st.session_state.input), disabled=st.session_state.running)
+

requirements.txt

@@ -4,5 +4,8 @@ qdrant-client==1.1.7
 llama-cpp-python==0.1.49
 pdfminer.six==20221105
 python-dotenv==1.0.0
+streamlit==1.22.0 
+streamlit-chat==0.0.2.2 
+streamlit-extras==0.2.7 
 pandoc==2.3
-unstructured==0.6.6 
+unstructured==0.6.6

startLLM.py

@@ -11,11 +11,13 @@
 persist_directory = os.environ.get('PERSIST_DIRECTORY')
 model_type = os.environ.get('MODEL_TYPE')
 model_path = os.environ.get('MODEL_PATH')
-model_n_ctx = os.environ.get('MODEL_N_CTX')
-model_temp = os.environ.get('MODEL_TEMP')
+model_n_ctx = int(os.environ.get('MODEL_N_CTX'))
+model_temp = float(os.environ.get('MODEL_TEMP'))
 model_stop = os.environ.get('MODEL_STOP').split(",")
 
-def main():
+qa_system=None
+
+def initialize_qa_system():
     # Load stored vectorstore
     llama = LlamaCppEmbeddings(model_path=llama_embeddings_model, n_ctx=model_n_ctx)
     # Load ggml-formatted model 
@@ -41,27 +43,36 @@ def main():
         case _default:
             print("Only LlamaCpp or GPT4All supported right now. Make sure you set up your .env correctly.")
     qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=qdrant.as_retriever(search_type="mmr"), return_source_documents=True)
+    return qa
 
+def main(prompt="", gui=False):
+    global qa_system
+    if qa_system is None:
+        qa_system = initialize_qa_system()
     # Interactive questions and answers
-    while True:
-        query = input("\nEnter a query: ")
-        if query == "exit":
-            break
-
-        # Get the answer from the chain
-        res = qa(query)    
-        answer, docs = res['result'], res['source_documents']
+    if (prompt.strip() != "" and gui) or gui==False:
+        while True:
+            query = prompt if gui else input("\nEnter a query: ")
+            if query == "exit":
+                break
+
+            # Get the answer from the chain
+            res = qa_system(query)    
+            answer, docs = res['result'], res['source_documents']
 
-        # Print the result
-        print("\n\n> Question:")
-        print(query)
-        print("\n> Answer:")
-        print(answer)
-
-        # Print the relevant sources used for the answer
-        for document in docs:
-            print("\n> " + document.metadata["source"] + ":")
-            print(document.page_content)
+            # Print the result
+            print("\n\n> Question:")
+            print(query)
+            print("\n> Answer:")
+            print(answer)
+
+            # Print the relevant sources used for the answer
+            for document in docs:
+                print("\n> " + document.metadata["source"] + ":")
+                print(document.page_content)
+
+            if gui:
+                return answer
 
 if __name__ == "__main__":
     main()