Update app.py

app.py

@@ -2,63 +2,64 @@ import streamlit as st
 from gradio_client import Client
 
 # Constants
-APP_TITLE = "Llama2 70B Chatbot"
-APP_DESCRIPTION = """
-This application demonstrates the Llama-2-70b chatbot model by Meta, 
-fine-tuned for chat instructions. You can interact with the model and ask questions.
+TITLE = "Llama2 70B Chatbot"
+DESCRIPTION = """
+This Space demonstrates model [Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) by Meta, 
+a Llama 2 model with 70B parameters fine-tuned for chat instructions. 
 """
 
 # Initialize client
-llama2_client = Client("https://ysharma-explore-llamav2-with-tgi.hf.space/")
+client = Client("https://ysharma-explore-llamav2-with-tgi.hf.space/")
+
 
 with st.sidebar:
-    system_prompt_input = st.text_input("Optional system prompt:")
-    temperature_slider = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
-    max_new_tokens_slider = st.slider("Max new tokens", min_value=0.0, max_value=4096.0, value=4096.0, step=64.0)
+    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 get_llama2_response(user_message, system_prompt, temperature, max_new_tokens, topp=0.6, repetition_penalty=1.2):
-    with st.status("Requesting Llama-2"):
-        st.write("Requesting API...")
-        response = llama2_client.predict(
-            user_message,
-            system_prompt,
-            temperature,
-            max_new_tokens,
-            topp,
-            repetition_penalty,
-            api_name="/chat"
+def predict(message, system_prompt, temperature, max_new_tokens,Topp,Repetitionpenalty):
+    with st.status("Requesting LLama-2"):
+        st.write("Requesting API")
+        response = client.predict(
+    			message,	# str in 'Message' Textbox component
+                system_prompt,	# str in 'Optional system prompt' Textbox component
+    			temperature,	# int | float (numeric value between 0.0 and 1.0)
+    			max_new_tokens,	# int | float (numeric value between 0 and 4096)
+    			Topp,	# int | float (numeric value between 0.0 and 1)
+    			Repetitionpenalty,	# int | float (numeric value between 1.0 and 2.0)
+    			api_name="/chat"
         )
         st.write("Done")
         return response
 
 # Streamlit UI
-st.title(APP_TITLE)
-st.write(APP_DESCRIPTION)
+st.title(TITLE)
+st.write(DESCRIPTION)
+
 
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
+if "messages" not in st.session_state:
+    st.session_state.messages = []
 
 # Display chat messages from history on app rerun
-for message in st.session_state.chat_history:
+for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
         
 # React to user input
-if user_input := st.chat_input("Ask Llama-2-70B anything..."):
+if prompt := st.chat_input("Ask LLama-2-70b anything..."):
     # Display user message in chat message container
-    st.chat_message("user", avatar="🧑‍💻").markdown(user_input)
+    st.chat_message("human",avatar = "🧑‍💻").markdown(prompt)
     # Add user message to chat history
-    st.session_state.chat_history.append({"role": "user", "content": user_input})
+    st.session_state.messages.append({"role": "human", "content": prompt})
 
-    response = get_llama2_response(
-        user_input,
-        system_prompt_input,
-        temperature_slider,
-        max_new_tokens_slider
-    )
+    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)
     # Add assistant response to chat history
-    st.session_state.chat_history.append({"role": "assistant", "content": response})
+    st.session_state.messages.append({"role": "assistant", "content": response})