Update app.py

app.py

@@ -11,17 +11,17 @@ a Llama 2 model with 70B parameters fine-tuned for chat instructions.
 # Initialize client
 
 
-with st.sidebar:
-    system_promptSide = st.text_input("Optional system prompt:")
-    temperatureSide = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
-    max_new_tokensSide = st.slider("Max new tokens", min_value=0.0, max_value=4096.0, value=4096.0, step=64.0)
-    ToppSide = st.slider("Top-p (nucleus sampling)", min_value=0.0, max_value=1.0, value=0.6, step=0.05)
-    RepetitionpenaltySide = st.slider("Repetition penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.05)
+# with st.sidebar:
+#     system_promptSide = st.text_input("Optional system prompt:")
+#     temperatureSide = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
+#     max_new_tokensSide = st.slider("Max new tokens", min_value=0.0, max_value=4096.0, value=4096.0, step=64.0)
+#     ToppSide = st.slider("Top-p (nucleus sampling)", min_value=0.0, max_value=1.0, value=0.6, step=0.05)
+#     RepetitionpenaltySide = st.slider("Repetition penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.05)
 
 
     
 # Prediction function
-def predict(message, system_prompt, temperature, max_new_tokens,Topp,Repetitionpenalty):
+def predict(message, system_prompt='', temperature=0.7, max_new_tokens=4096,Topp=0.5,Repetitionpenalty=1.2):
     with st.status("Starting client"):
         client = Client("https://ysharma-explore-llamav2-with-tgi.hf.space/")
         st.write("Requesting client")
@@ -59,7 +59,7 @@ if prompt := st.chat_input("Ask LLama-2-70b anything..."):
     # Add user message to chat history
     st.session_state.messages.append({"role": "human", "content": prompt})
 
-    response = predict(prompt,system_promptSide,temperatureSide,max_new_tokensSide,ToppSide,RepetitionpenaltySide)
+    response = predict(prompt)#,system_promptSide,temperatureSide,max_new_tokensSide,ToppSide,RepetitionpenaltySide)
     # Display assistant response in chat message container
     with st.chat_message("assistant", avatar='🦙'):
         st.markdown(response)