interactive app

app.py

@@ -24,10 +24,11 @@ for file in files:
     docs.append(loader.load()[0])
 
 # Config
-chunk_size = 500 # Defines the chunks in amount of tokens in which the files are split. Also defines the amount of tokens that are feeded into the context. 
-chunk_overlap = 100
-temperature = 0.4
-model = "gpt-3.5-turbo"
+with st.sidebar:
+    chunk_size = st.number_input("Insert chunk size", value=500, step=100, placeholder=500) # Defines the chunks in amount of tokens in which the files are split. Also defines the amount of tokens that are feeded into the context. 
+    chunk_overlap = st.number_input("Insert chunk-overlap", value=100, step=10, placeholder=100)
+    temperature = st.number_input("Insert temperature", value=0.0, min_value=0.0, step=0.2, max_value=1.0, placeholder=0.0)
+    model = st.selectbox("Model name", ["gpt-3.5-turbo"])
 
 
 prompt_template ="""
@@ -71,17 +72,56 @@ rag_chain = (
     | StrOutputParser()
 )
 
+
 st.title("🐔 Volker-Chat")
 
+def click_button(btn):
+    st.session_state.clicked = True
+    st.session_state['btn'] = btn
+
+def predefined_question(btn):
+    if btn == "btn_1":
+        prompt = "Erläutere die Säule 'Lernen' aus der Volker-App."
+    elif btn == "btn_2":
+        prompt = "Erläutere die Säule 'Tracken' aus der Volker-App."
+    elif btn == "btn_3":
+        prompt = "Erläutere die Säule 'Handeln' aus der Volker-App."
+    return prompt
+
+c = st.container()
+c.write("Beispielfragen")
+col1, col2, col3 = c.columns(3)
+col1.button("Was ist 'Lernen'?", on_click=click_button, args=['btn_1'])
+col2.button("Was ist 'Tracken'?", on_click=click_button, args=['btn_2'])
+col3.button("Was ist 'Handeln'?", on_click=click_button, args=['btn_3'])
+
+if 'clicked' not in st.session_state:
+    st.session_state.clicked = False
+
 if "messages" not in st.session_state:
     st.session_state["messages"] = [{"role": "assistant", "content": "Ahoi! Ich bin Volker. Wie kann ich dir helfen?"}]
 
 for msg in st.session_state.messages:
     st.chat_message(msg["role"]).write(msg["content"])
 
+if st.session_state.clicked:
+    prompt = predefined_question(st.session_state['btn'])
+    st.chat_message("user").write(prompt)
+    with get_openai_callback() as cb:
+        response = rag_chain.invoke(prompt)
+        st.chat_message("assistant").write(response)
+        with st.sidebar:
+            sidebar_c = st.container()
+            sidebar_c.success(cb)
+    st.session_state.clicked = False
+
 if prompt := st.chat_input():
     st.chat_message("user").write(prompt)
     with get_openai_callback() as cb:
         response = rag_chain.invoke(prompt)
         st.chat_message("assistant").write(response)
-        st.chat_message("assistant").write(str(cb))
+        with st.sidebar:
+            sidebar_c = st.container()
+            sidebar_c.success(cb)
+    st.session_state.clicked = False
+