Create app.py

app.py

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+import streamlit as st
+from llama_cpp import Llama
+import warnings
+warnings.filterwarnings(action='ignore')
+import datetime
+import random
+import string
+from time import sleep
+import tiktoken
+
+# for counting the tokens in the prompt and in the result
+#context_count = len(encoding.encode(yourtext))
+encoding = tiktoken.get_encoding("r50k_base") 
+
+nCTX = 8192
+sTOPS = ['<eos>']
+modelname = "Gemma2-2B-it"
+# Set the webpage title
+st.set_page_config(
+    page_title=f"Your LocalGPT ✨ with {modelname}",
+    page_icon="🌟",
+    layout="wide")
+
+if "hf_model" not in st.session_state:
+    st.session_state.hf_model = "Gemma2-2B-it"
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "repeat" not in st.session_state:
+    st.session_state.repeat = 1.35
+
+if "temperature" not in st.session_state:
+    st.session_state.temperature = 0.1
+
+if "maxlength" not in st.session_state:
+    st.session_state.maxlength = 500
+
+if "speed" not in st.session_state:
+    st.session_state.speed = 0.0
+
+if "numOfTurns" not in st.session_state:
+    st.session_state.numOfTurns = 0
+
+if "maxTurns" not in st.session_state:
+    st.session_state.maxTurns = 5  #must be odd number, greater than equal to 5
+
+def writehistory(filename,text):
+    with open(filename, 'a', encoding='utf-8') as f:
+        f.write(text)
+        f.write('\n')
+    f.close()
+
+def genRANstring(n):
+    """
+    n = int number of char to randomize
+    """
+    N = n
+    res = ''.join(random.choices(string.ascii_uppercase +
+                                string.digits, k=N))
+    return res
+
+@st.cache_resource 
+def create_chat():   
+# Set HF API token  and HF repo
+    from llama_cpp import Llama
+    client = Llama.from_pretrained((
+                repo_id="bartowski/gemma-2-2b-it-GGUF",
+                filename="gemma-2-2b-it-Q5_K_M.gguf",
+                temperature=0.24,
+                n_ctx=nCTX,
+                max_tokens=600,
+                repeat_penalty=1.176,
+                stop=sTOPS,
+                verbose=False,
+                )
+    print('loading gemma-2-2b-it-Q5_K_M.gguf with LlamaCPP...')
+    return client
+
+
+# create THE SESSIoN STATES
+if "logfilename" not in st.session_state:
+## Logger file
+    logfile = f'{genRANstring(5)}_log.txt'
+    st.session_state.logfilename = logfile
+    #Write in the history the first 2 sessions
+    writehistory(st.session_state.logfilename,f'{str(datetime.datetime.now())}\n\nYour own LocalGPT with 🌀 {modelname}\n---\n🧠🫡: You are a helpful assistant.')    
+    writehistory(st.session_state.logfilename,f'🌀: How may I help you today?')
+
+
+#AVATARS
+av_us = 'https://github.com/fabiomatricardi/Gemma2-2b-it-chatbot/raw/main/images/user.png'  # './man.png'  #"🦖"  #A single emoji, e.g. "🧑‍💻", "🤖", "🦖". Shortcodes are not supported.
+av_ass = 'https://github.com/fabiomatricardi/Gemma2-2b-it-chatbot/raw/main/images/assistant2.png'   #'./robot.png'
+
+### START STREAMLIT UI
+# Create a header element
+st.image('https://github.com/fabiomatricardi/Gemma2-2b-it-chatbot/raw/main/images/Gemma-2-Banner.original.jpg',use_column_width=True)
+mytitle = f'> *🌟 {modelname} with {nCTX} tokens Context window* - Turn based Chat available with max capacity of :orange[**{st.session_state.maxTurns} messages**].'
+st.markdown(mytitle, unsafe_allow_html=True)
+#st.markdown('> Local Chat ')
+#st.markdown('---')
+
+# CREATE THE SIDEBAR
+with st.sidebar:
+    st.image('https://github.com/fabiomatricardi/Gemma2-2b-it-chatbot/raw/main/images/banner.png', use_column_width=True)
+    st.session_state.temperature = st.slider('Temperature:', min_value=0.0, max_value=1.0, value=0.65, step=0.01)
+    st.session_state.maxlength = st.slider('Length reply:', min_value=150, max_value=2000, 
+                                           value=550, step=50)
+    st.session_state.repeat = st.slider('Repeat Penalty:', min_value=0.0, max_value=2.0, value=1.176, step=0.02)
+    st.session_state.turns = st.toggle('Turn based', value=False, help='Activate Conversational Turn Chat with History', 
+                                       disabled=False, label_visibility="visible")
+    st.markdown(f"*Number of Max Turns*: {st.session_state.maxTurns}")
+    actualTurns = st.markdown(f"*Chat History Lenght*: :green[Good]")
+    statspeed = st.markdown(f'💫 speed: {st.session_state.speed}  t/s')
+    btnClear = st.button("Clear History",type="primary", use_container_width=True)
+    st.markdown(f"**Logfile**: {st.session_state.logfilename}")
+
+llm = create_chat()
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    if message["role"] == "user":
+        with st.chat_message(message["role"],avatar=av_us):
+            st.markdown(message["content"])
+    else:
+        with st.chat_message(message["role"],avatar=av_ass):
+            st.markdown(message["content"])
+# Accept user input
+if myprompt := st.chat_input("What is an AI model?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": myprompt})
+    st.session_state.numOfTurns = len(st.session_state.messages)
+    # Display user message in chat message container
+    with st.chat_message("user", avatar=av_us):
+        st.markdown(myprompt)
+        usertext = f"user: {myprompt}"
+        writehistory(st.session_state.logfilename,usertext)
+        # Display assistant response in chat message container
+    with st.chat_message("assistant",avatar=av_ass):
+        message_placeholder = st.empty()
+        with st.spinner("Thinking..."):
+            start = datetime.datetime.now()
+            response = ''
+            conv_messages = []
+            if st.session_state.turns:
+                if st.session_state.numOfTurns > st.session_state.maxTurns:
+                    conv_messages = st.session_state.messages[-st.session_state.maxTurns:]
+                    actualTurns.markdown(f"*Chat History Lenght*: :red[Trimmed]")
+                else:    
+                    conv_messages = st.session_state.messages
+            else:
+                conv_messages.append(st.session_state.messages[-1])
+            full_response = ""
+            for chunk in llm.create_chat_completion(
+                messages=conv_messages,
+                temperature=st.session_state.temperature,
+                repeat_penalty= st.session_state.repeat,
+                stop=sTOPS,
+                max_tokens=st.session_state.maxlength,              
+                stream=True,):
+                try:
+                    if chunk["choices"][0]["delta"]["content"]:
+                        full_response += chunk["choices"][0]["delta"]["content"]
+                        message_placeholder.markdown(full_response + "🟡")
+                        delta = datetime.datetime.now() -start       
+                        totalseconds = delta.total_seconds()
+                        prompttokens = len(encoding.encode(myprompt))
+                        assistanttokens = len(encoding.encode(full_response))
+                        totaltokens = prompttokens + assistanttokens  
+                        st.session_state.speed = totaltokens/totalseconds 
+                        statspeed.markdown(f'💫 speed: {st.session_state.speed:.2f}  t/s')                                               
+                except:
+                    pass                 
+
+            delta = datetime.datetime.now() - start
+            totalseconds = delta.total_seconds()
+            prompttokens = len(encoding.encode(myprompt))
+            assistanttokens = len(encoding.encode(full_response))
+            totaltokens = prompttokens + assistanttokens
+            st.session_state.speed = totaltokens/totalseconds
+            statspeed.markdown(f'💫 speed: {st.session_state.speed:.3f}  t/s')
+            toregister = full_response + f"""
+```
+🧾 prompt tokens: {prompttokens}
+📈 generated tokens: {assistanttokens}
+⏳ generation time: {delta}
+💫 speed: {st.session_state.speed}  t/s
+```"""    
+            message_placeholder.markdown(toregister)
+            asstext = f"assistant: {toregister}"
+            writehistory(st.session_state.logfilename,asstext)       
+        st.session_state.messages.append({"role": "assistant", "content": toregister})
+        st.session_state.numOfTurns = len(st.session_state.messages)
+