done

app.py

@@ -0,0 +1,71 @@
+import streamlit as st
+import os
+from ctransformers import AutoModelForCausalLM
+
+# App title
+st.set_page_config(page_title="🦙💬 Llama 2 Chatbot")
+
+@st.cache_resource()
+def ChatModel(temperature, top_p):
+    return AutoModelForCausalLM.from_pretrained(
+        'ggml-llama-2-7b-chat-q4_0.bin', 
+        model_type='llama',
+        temperature=temperature, 
+        top_p = top_p)
+
+# Replicate Credentials
+with st.sidebar:
+    st.title('🦙💬 Llama 2 Chatbot')
+
+    # Refactored from <https://github.com/a16z-infra/llama2-chatbot>
+    st.subheader('Models and parameters')
+    
+    temperature = st.sidebar.slider('temperature', min_value=0.01, max_value=2.0, value=0.1, step=0.01)
+    top_p = st.sidebar.slider('top_p', min_value=0.01, max_value=1.0, value=0.9, step=0.01)
+    # max_length = st.sidebar.slider('max_length', min_value=64, max_value=4096, value=512, step=8)
+    chat_model =ChatModel(temperature, top_p)
+    # st.markdown('📖 Learn how to build this app in this [blog](#link-to-blog)!')
+
+# Store LLM generated responses
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+
+# Display or clear chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+def clear_chat_history():
+    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
+
+# Function for generating LLaMA2 response
+def generate_llama2_response(prompt_input):
+    string_dialogue = "You are a helpful assistant. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'."
+    for dict_message in st.session_state.messages:
+        if dict_message["role"] == "user":
+            string_dialogue += "User: " + dict_message["content"] + "\\n\\n"
+        else:
+            string_dialogue += "Assistant: " + dict_message["content"] + "\\n\\n"
+    output = chat_model(f"prompt {string_dialogue} {prompt_input} Assistant: ")
+    return output
+
+# User-provided prompt
+if prompt := st.chat_input():
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.write(prompt)
+
+# Generate a new response if last message is not from assistant
+if st.session_state.messages[-1]["role"] != "assistant":
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            response = generate_llama2_response(prompt)
+            placeholder = st.empty()
+            full_response = ''
+            for item in response:
+                full_response += item
+                placeholder.markdown(full_response)
+            placeholder.markdown(full_response)
+    message = {"role": "assistant", "content": full_response}
+    st.session_state.messages.append(message)

requirements.txt

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+ctransformers
+streamlit