Stable GUI

gui.py

@@ -17,9 +17,10 @@
 model_stop = os.environ.get('MODEL_STOP')
 
 # Initialization
-if "input" not in st.session_state:
+if "initialized" not in st.session_state:
     st.session_state.input = ""
     st.session_state.running = False
+    st.session_state.initialized = False
 
 st.set_page_config(page_title="CASALIOY")
 
@@ -49,10 +50,9 @@
 
 colored_header(label='', description='', color_name='blue-30')
 response_container = st.container()
-
-
 
 def generate_response(input=""):
+    print("Input:"+input)
     with response_container:
         col1, col2, col3 = st.columns(3)
         with col1:
@@ -68,24 +68,32 @@ def generate_response(input=""):
                 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']:
+        if 'generated' in st.session_state:
             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)
+            st.session_state.past.append(input)
             if st.session_state.running:
-                message(st.session_state.input, is_user=True)
+                message(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)
+                if st.session_state.initialized == False:
+                    st.session_state.initialized = True
+                    print("Initializing...")
+                    startLLM.initialize_qa_system()
+                else:
+                    print("Already initialized!")
+                response=startLLM.qa_system(st.session_state.input)
+                st.session_state.input = ""
+                answer, docs = response['result'], response['source_documents']
+                st.session_state.generated.append(answer)
+                message(answer)
                 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):
+        with form:
+            st.text_input("You: ", "", key="input", disabled=st.session_state.running)
+form = st.form(key="input-form", clear_on_submit=True)
+with form:
     st.form_submit_button('SUBMIT', on_click=generate_response(st.session_state.input), disabled=st.session_state.running)
 

startLLM.py

@@ -18,61 +18,57 @@
 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 
-    local_path = model_path
-
-    client = qdrant_client.QdrantClient(
-    path=persist_directory, prefer_grpc=True
-    )
-    qdrant = Qdrant(
-        client=client, collection_name="test", 
-        embeddings=llama
-    )
-
-    # Prepare the LLM chain 
-    callbacks = [StreamingStdOutCallbackHandler()]
-    match model_type:
-        case "LlamaCpp":
-            from langchain.llms import LlamaCpp
-            llm = LlamaCpp(model_path=local_path, n_ctx=model_n_ctx, temperature=model_temp, stop=model_stop, callbacks=callbacks, verbose=True)
-        case "GPT4All":
-            from langchain.llms import GPT4All
-            llm = GPT4All(model=local_path, n_ctx=model_n_ctx, callbacks=callbacks, verbose=True, backend='gptj')
-        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()
+        # Load stored vectorstore
+        llama = LlamaCppEmbeddings(model_path=llama_embeddings_model, n_ctx=model_n_ctx)
+        # Load ggml-formatted model 
+        local_path = model_path
+
+        client = qdrant_client.QdrantClient(
+        path=persist_directory, prefer_grpc=True
+        )
+        qdrant = Qdrant(
+            client=client, collection_name="test", 
+            embeddings=llama
+        )
+
+        # Prepare the LLM chain 
+        callbacks = [StreamingStdOutCallbackHandler()]
+        match model_type:
+            case "LlamaCpp":
+                from langchain.llms import LlamaCpp
+                llm = LlamaCpp(model_path=local_path, n_ctx=model_n_ctx, temperature=model_temp, stop=model_stop, callbacks=callbacks, verbose=True)
+            case "GPT4All":
+                from langchain.llms import GPT4All
+                llm = GPT4All(model=local_path, n_ctx=model_n_ctx, callbacks=callbacks, verbose=True, backend='gptj')
+            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)
+        qa_system = qa
+
+def main():
+    initialize_qa_system()
     # Interactive questions and answers
-    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']
+    while True:
+        query = 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)
-
-            if gui:
-                return answer
+        # 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 __name__ == "__main__":
     main()