testing chat interface

app.py

@@ -1,4 +1,46 @@
 import streamlit as st
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+hf_hub_download(repo_id="LLukas22/gpt4all-lora-quantized-ggjt", filename="ggjt-model.bin", local_dir=".")
+llm = Llama(model_path="./ggjt-model.bin")
+
+ins = '''### Instruction:
+{}
+### Response:
+'''
+
+fixed_instruction = "You are a healthcare bot designed to give advice for the prevention and treatment of various illnesses."
+
+def respond(message):
+    full_instruction = fixed_instruction + " " + message
+    formatted_instruction = ins.format(full_instruction)
+    bot_message = llm(formatted_instruction, stop=['### Instruction:', '### End'])
+    bot_message = bot_message['choices'][0]['text']
+    return bot_message
+
+st.title("Healthcare Bot")
+
+# Initialize 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.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# React to user input
+if prompt := st.chat_input("What is your question?"):
+    # Display user message in chat message container
+    st.chat_message("user").markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    response = respond(prompt)
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        st.markdown(response)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response})
+)