NonToxicGlazeAdvisor_Chat_with_Docs_Groq_Edition_1 - app.py - 27-03-2024, 09:33 CET

app.py

@@ -1,4 +1,4 @@
-# NonToxicGlazeAdvisor_Chat_with_Docs_Groq_Edition_1 - app.py - 26-03-2024
+# NonToxicGlazeAdvisor_Chat_with_Docs_Groq_Edition_1 - app.py - 27-03-2024
 
 # STREAMLIT:
 # https://www.datacamp.com/tutorial/streamlit:
@@ -260,11 +260,59 @@ document_chain = create_stuff_documents_chain(llm, prompt)
 retriever = st.session_state.vector.as_retriever()
 retrieval_chain = create_retrieval_chain(retriever, document_chain)
 
-# prompt = st.text_input("Input your prompt here") #, key=key)
-prompt = st.text_input("Stel hieronder Uw vraag:") #, key=key)
+## prompt = st.text_input("Input your prompt here") #, key=key)
+#prompt = st.text_input("Stel hieronder Uw vraag:") #, key=key)
+#
+## If the user hits enter
+#if prompt:
+#    # Then pass the prompt to the LLM
+#    start = time.process_time()
+#    response = retrieval_chain.invoke({"input": prompt})
+#    # print(f"Response time: {time.process_time() - start}")
+#    st.write(f"Response time: {time.process_time() - start} seconds")
+#
+#    st.write(response["answer"])
+#
+#    # With a streamlit expander
+#    with st.expander("Document Similarity Search"):
+#        # Find the relevant chunks
+#        for i, doc in enumerate(response["context"]):
+#            # print(doc)
+#            # st.write(f"Source Document # {i+1} : {doc.metadata['source'].split('/')[-1]}")
+#            st.write(doc)
+#            st.write(f"Source Document # {i+1} : {doc.metadata['source'].split('/')[-1]}")
+#            
+#            
+#            st.write(doc.page_content)
+#            st.write("--------------------------------")
+#
+#st.write("---------------------------------")
+
+
+# ZIE:
+# https://raw.githubusercontent.com/streamlit/llm-examples/main/Chatbot.py
+# from openai import OpenAI
+# import streamlit as st
+
+with st.sidebar:
+    openai_api_key = st.text_input("OpenAI API Key", key="chatbot_api_key", type="password")
+    "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
+    "[View the source code](https://github.com/streamlit/llm-examples/blob/main/Chatbot.py)"
+    "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
+
+st.title("💬 Chatbot")
+st.caption("🚀 A streamlit chatbot powered by mixtral-8x7b-32768 Groq LLM (VERY FAST !)")
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you?"}]
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+if prompt := st.chat_input():
+    #if not openai_api_key:
+    #    st.info("Please add your OpenAI API key to continue.")
+    #    st.stop()
 
-# If the user hits enter
-if prompt:
     # Then pass the prompt to the LLM
     start = time.process_time()
     response = retrieval_chain.invoke({"input": prompt})
@@ -273,6 +321,16 @@ if prompt:
 
     st.write(response["answer"])
 
+
+    #client = OpenAI(api_key=openai_api_key)
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    st.chat_message("user").write(prompt)
+    # response = client.chat.completions.create(model="gpt-3.5-turbo", messages=st.session_state.messages)
+    # msg = response.choices[0].message.content
+    msg = response["answer"]
+    st.session_state.messages.append({"role": "assistant", "content": msg})
+    st.chat_message("assistant").write(msg)
+
     # With a streamlit expander
     with st.expander("Document Similarity Search"):
         # Find the relevant chunks
@@ -290,12 +348,6 @@ st.write("---------------------------------")
 
 
 
-
-
-
-
-
-
 #i=0
 #while True:
 #