initial set up

app.py

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+import os
+from langchain_huggingface import HuggingFaceEndpoint
+import streamlit as st
+from transformers import pipeline
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+
+# Chat parameters
+first_ia_message = "Hello, there! How can I help you today?"
+system_message = "You are a friendly AI conversing with a human user."
+text_placeholder = "Enter your text here."
+text_waiting_ai_response = "Thinking..."
+max_response_length = 256
+reset_button_label = "Reset Chat History"
+
+# Models and Pipeline
+model_id="mistralai/Mistral-7B-Instruct-v0.3"
+translation_model_id = "Helsinki-NLP/opus-mt-tc-big-en-pt"
+
+translation_pipeline = pipeline(
+    "translation_en_to_pt",
+    model=translation_model_id,
+    token=os.getenv("HF_TOKEN")
+)
+
+def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.1):
+    llm = HuggingFaceEndpoint(
+        repo_id=model_id,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        token = os.getenv("HF_TOKEN")
+    )
+    return llm
+
+
+def translate_to_portuguese(text):
+    translation = translation_pipeline(text)
+    return translation[0]['translation_text']
+
+# Configure the Streamlit app
+st.set_page_config(page_title="Personal ChatBot", page_icon="🤗")
+st.title("Personal ChatBot")
+st.markdown(f"* A simple chatbot with {model_id} and {translation_model_id}.*")
+
+# Initialize session state for avatars
+if "avatars" not in st.session_state:
+    st.session_state.avatars = {'user': None, 'assistant': None}
+
+# Initialize session state for user text input
+if 'user_text' not in st.session_state:
+    st.session_state.user_text = None
+
+# Initialize session state for model parameters
+if "max_response_length" not in st.session_state:
+    st.session_state.max_response_length = max_response_length
+
+# Sidebar for settings
+with st.sidebar:
+    st.header("System Settings")
+
+    # AI Settings
+    st.session_state.system_message = st.text_area(
+        "System Message", value=system_message
+    )
+    st.session_state.starter_message = st.text_area(
+        'First AI Message', value=first_ia_message
+    )
+    # Model Settings
+    st.session_state.max_response_length = st.number_input(
+        "Max Response Length", value=max_response_length
+    )
+    # Reset Chat History
+    reset_history = st.button(reset_button_label)
+    
+# Initialize or reset chat history
+if "chat_history" not in st.session_state or reset_history:
+    st.session_state.chat_history = [{"role": "assistant", "content": st.session_state.starter_message}]
+
+def get_response(system_message, chat_history, user_text, 
+                 eos_token_id=['User'], max_new_tokens=256, get_llm_hf_kws={}):
+    # Set up model with token and temperature
+    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)
+
+    # Create the prompt template
+    prompt = PromptTemplate.from_template(
+        (
+            "[INST] {system_message}"
+            "\nCurrent Conversation:\n{chat_history}\n\n"
+            "\nUser: {user_text}.\n [/INST]"
+            "\nAI:"
+        )
+    )
+
+    # Response template
+    chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
+
+    response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history))
+    response = response.split("AI:")[-1]
+    response = translate_to_portuguese(response)
+    chat_history.append({'role': 'user', 'content': user_text})
+    chat_history.append({'role': 'assistant', 'content': response})
+    return response, chat_history
+
+# Chat interface
+chat_interface = st.container(border=True)
+with chat_interface:
+    output_container = st.container()
+    st.session_state.user_text = st.chat_input(placeholder=text_placeholder)
+    
+# Display chat messages
+with output_container:
+    for message in st.session_state.chat_history:
+        if message['role'] == 'system':
+            continue            
+        with st.chat_message(message['role'], 
+                             avatar=st.session_state['avatars'][message['role']]):
+            st.markdown(message['content'])
+            
+ # User new text:
+    if st.session_state.user_text:
+        with st.chat_message("user", 
+                             avatar=st.session_state.avatars['user']):
+            st.markdown(st.session_state.user_text)
+        with st.chat_message("assistant", 
+                             avatar=st.session_state.avatars['assistant']):
+
+            with st.spinner(text_waiting_ai_response):
+                response, st.session_state.chat_history = get_response(
+                    system_message=st.session_state.system_message, 
+                    user_text=st.session_state.user_text,
+                    chat_history=st.session_state.chat_history,
+                    max_new_tokens=st.session_state.max_response_length,
+                )
+                st.markdown(response)

requirements.txt

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+huggingface_hub==0.25.2
+transformers
+torch
+gradio
+accelerate
+bitsandbytes
+