Update app.py

app.py

@@ -6,7 +6,7 @@ from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 from langchain_community.document_loaders import YoutubeLoader, WebBaseLoader
 
-model_id="mistralai/Mistral-7B-Instruct-v0.3"
+model_id="unsloth/Llama-3.2-1B-Instruct"
 
 model = pipeline(
     "text-generation",
@@ -45,209 +45,209 @@ You are provided with a context and a user query. Your task is to answer the use
 """
 
 
-
-def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.1, top_k=50, top_p=0.9):
-    """
-    Returns a language model for HuggingFace inference.
-
-    Parameters:
-    - model_id (str): The ID of the HuggingFace model repository.
-    - max_new_tokens (int): The maximum number of new tokens to generate.
-    - temperature (float): The temperature for sampling from the model.
-    - top_k (int): The number of highest probability tokens to consider.
-    - top_p (float): The cumulative probability threshold for token selection.
-
-    Returns:
-    - llm (HuggingFaceEndpoint): The language model for HuggingFace inference.
-    """
-    llm = HuggingFaceEndpoint(
-        repo_id=model_id,
-        max_new_tokens=max_new_tokens,
-        temperature=temperature,
-        top_k=top_k,
-        top_p=top_p,
-        token=os.getenv("HF_TOKEN")
-    )
-    return llm
-
-# Configure the Streamlit app
-st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗")
-st.title("Personal HuggingFace ChatBot")
-st.markdown(f"*This is a simple chatbot that uses the HuggingFace transformers library to generate responses to your text input. It uses the {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 = 256
-
-if "top_k" not in st.session_state:
-    st.session_state.top_k = 1.0
-
-if "top_p" not in st.session_state:
-    st.session_state.top_p = 0.95
-
-if "temperature" not in st.session_state:
-    st.session_state.temperature = 1.0
-
-if "system_message" not in st.session_state:
-    st.session_state.system_message = "friendly AI conversing with a human user"
-
-if "starter_message" not in st.session_state:
-    st.session_state.starter_message = "Hello, there! How can I help you today?"
+# model_id="mistralai/Mistral-7B-Instruct-v0.3"
+# def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.1, top_k=50, top_p=0.9):
+#     """
+#     Returns a language model for HuggingFace inference.
+
+#     Parameters:
+#     - model_id (str): The ID of the HuggingFace model repository.
+#     - max_new_tokens (int): The maximum number of new tokens to generate.
+#     - temperature (float): The temperature for sampling from the model.
+#     - top_k (int): The number of highest probability tokens to consider.
+#     - top_p (float): The cumulative probability threshold for token selection.
+
+#     Returns:
+#     - llm (HuggingFaceEndpoint): The language model for HuggingFace inference.
+#     """
+#     llm = HuggingFaceEndpoint(
+#         repo_id=model_id,
+#         max_new_tokens=max_new_tokens,
+#         temperature=temperature,
+#         top_k=top_k,
+#         top_p=top_p,
+#         token=os.getenv("HF_TOKEN")
+#     )
+#     return llm
+
+# # Configure the Streamlit app
+# st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗")
+# st.title("Personal HuggingFace ChatBot")
+# st.markdown(f"*This is a simple chatbot that uses the HuggingFace transformers library to generate responses to your text input. It uses the {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 = 256
+
+# if "top_k" not in st.session_state:
+#     st.session_state.top_k = 1.0
+
+# if "top_p" not in st.session_state:
+#     st.session_state.top_p = 0.95
+
+# if "temperature" not in st.session_state:
+#     st.session_state.temperature = 1.0
+
+# if "system_message" not in st.session_state:
+#     st.session_state.system_message = "friendly AI conversing with a human user"
+
+# if "starter_message" not in st.session_state:
+#     st.session_state.starter_message = "Hello, there! How can I help you today?"
     
     
-# Sidebar for settings
-with st.sidebar:
-    st.header("System Settings")
-
-    # AI Settings
-    st.session_state.system_message = st.text_area(
-        "System Message", value="You are a friendly AI conversing with a human user."
-    )
-    st.session_state.starter_message = st.text_area(
-        'First AI Message', value="Hello, there! How can I help you today?"
-    )
-
-    # Model Settings
-    st.session_state.max_response_length = st.number_input(
-        "Max Response Length", value=128
-    )
-
-
-    selected_option = st.selectbox("Choose parametrs", ("Pricise", "Balanced", "Creative", "Custom"))
-
-    if selected_option == "Precise":
-        st.session_state.temperature = 0.1
-        st.session_state.top_k = 10.0
-        st.session_state.top_p = 0.8
-    elif selected_option == "Balanced":
-        st.session_state.temperature = 1.0
-        st.session_state.top_k = 100.0
-        st.session_state.top_p = 0.9
-    elif selected_option == "Creative":
-        st.session_state.temperature = 10.0
-        st.session_state.top_k = 1500.0
-        st.session_state.top_p = 0.99
-    elif selected_option == "Custom":
-        st.session_state.temperature = st.number_input(
-    "Temperature", value=st.session_state.temperature, min_value=0.0
-    )
-        st.session_state.top_k = st.number_input(
-        "Top sK", value=st.session_state.top_k, min_value=1.0, step=1.0
-    )
-        st.session_state.top_p = st.number_input(
-        "Top sP", value=st.session_state.top_p, min_value=0.0, max_value=1.0
-    )
+# # Sidebar for settings
+# with st.sidebar:
+#     st.header("System Settings")
+
+#     # AI Settings
+#     st.session_state.system_message = st.text_area(
+#         "System Message", value="You are a friendly AI conversing with a human user."
+#     )
+#     st.session_state.starter_message = st.text_area(
+#         'First AI Message', value="Hello, there! How can I help you today?"
+#     )
+
+#     # Model Settings
+#     st.session_state.max_response_length = st.number_input(
+#         "Max Response Length", value=128
+#     )
+
+
+#     selected_option = st.selectbox("Choose parametrs", ("Pricise", "Balanced", "Creative", "Custom"))
+
+#     if selected_option == "Precise":
+#         st.session_state.temperature = 0.1
+#         st.session_state.top_k = 10.0
+#         st.session_state.top_p = 0.8
+#     elif selected_option == "Balanced":
+#         st.session_state.temperature = 1.0
+#         st.session_state.top_k = 100.0
+#         st.session_state.top_p = 0.9
+#     elif selected_option == "Creative":
+#         st.session_state.temperature = 10.0
+#         st.session_state.top_k = 1500.0
+#         st.session_state.top_p = 0.99
+#     elif selected_option == "Custom":
+#         st.session_state.temperature = st.number_input(
+#     "Temperature", value=st.session_state.temperature, min_value=0.0
+#     )
+#         st.session_state.top_k = st.number_input(
+#         "Top sK", value=st.session_state.top_k, min_value=1.0, step=1.0
+#     )
+#         st.session_state.top_p = st.number_input(
+#         "Top sP", value=st.session_state.top_p, min_value=0.0, max_value=1.0
+#     )
     
-    # Avatar Selection
-    st.markdown("*Select Avatars:*")
-    col1, col2 = st.columns(2)
-    with col1:
-        st.session_state.avatars['assistant'] = st.selectbox(
-            "AI Avatar", options=["🤗", "💬", "🤖"], index=0
-        )
-    with col2:
-        st.session_state.avatars['user'] = st.selectbox(
-            "User Avatar", options=["👤", "👱‍♂️", "👨🏾", "👩", "👧🏾"], index=0
-        )
-    # Reset Chat History
-    reset_history = st.button("Reset Chat History")
+#     # Avatar Selection
+#     st.markdown("*Select Avatars:*")
+#     col1, col2 = st.columns(2)
+#     with col1:
+#         st.session_state.avatars['assistant'] = st.selectbox(
+#             "AI Avatar", options=["🤗", "💬", "🤖"], index=0
+#         )
+#     with col2:
+#         st.session_state.avatars['user'] = st.selectbox(
+#             "User Avatar", options=["👤", "👱‍♂️", "👨🏾", "👩", "👧🏾"], index=0
+#         )
+#     # Reset Chat History
+#     reset_history = st.button("Reset Chat History")
     
-# 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={}):
-    """
-    Generates a response from the chatbot model.
-
-    Args:
-        system_message (str): The system message for the conversation.
-        chat_history (list): The list of previous chat messages.
-        user_text (str): The user's input text.
-        eos_token_id (list, optional): The list of end-of-sentence token IDs.
-        max_new_tokens (int, optional): The maximum number of new tokens to generate.
-        get_llm_hf_kws (dict, optional): Additional keyword arguments for the get_llm_hf function.
-
-    Returns:
-        tuple: A tuple containing the generated response and the updated chat history.
-    """
-    # Set up the model
-    hf = get_llm_hf_inference(
-        model_id=model_id,
-        max_new_tokens=max_new_tokens,
-        temperature=st.session_state.temperature,
-        top_k=st.session_state.top_k,
-        top_p=st.session_state.top_p
-    )
-
-    # Create the prompt template
-    prompt = PromptTemplate.from_template(
-        (
-            "[INST] {system_message}"
-            "\nCurrent Conversation:\n{chat_history}\n\n"
-            "\nUser: {user_text}.\n [/INST]"
-            "\nAI:"
-        )
-    )
-    # Make the chain and bind the prompt
-    chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
-
-    # Generate the response
-    response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history))
-    response = response.split("AI:")[-1]
-
-    # Update the chat history
-    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="Enter your text here.")
+# # 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={}):
+#     """
+#     Generates a response from the chatbot model.
+
+#     Args:
+#         system_message (str): The system message for the conversation.
+#         chat_history (list): The list of previous chat messages.
+#         user_text (str): The user's input text.
+#         eos_token_id (list, optional): The list of end-of-sentence token IDs.
+#         max_new_tokens (int, optional): The maximum number of new tokens to generate.
+#         get_llm_hf_kws (dict, optional): Additional keyword arguments for the get_llm_hf function.
+
+#     Returns:
+#         tuple: A tuple containing the generated response and the updated chat history.
+#     """
+#     # Set up the model
+#     hf = get_llm_hf_inference(
+#         model_id=model_id,
+#         max_new_tokens=max_new_tokens,
+#         temperature=st.session_state.temperature,
+#         top_k=st.session_state.top_k,
+#         top_p=st.session_state.top_p
+#     )
+
+#     # Create the prompt template
+#     prompt = PromptTemplate.from_template(
+#         (
+#             "[INST] {system_message}"
+#             "\nCurrent Conversation:\n{chat_history}\n\n"
+#             "\nUser: {user_text}.\n [/INST]"
+#             "\nAI:"
+#         )
+#     )
+#     # Make the chain and bind the prompt
+#     chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
+
+#     # Generate the response
+#     response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history))
+#     response = response.split("AI:")[-1]
+
+#     # Update the chat history
+#     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="Enter your text here.")
     
-# Display chat messages
-with output_container:
-    # For every message in the history
-    for message in st.session_state.chat_history:
-        # Skip the system message
-        if message['role'] == 'system':
-            continue
+# # Display chat messages
+# with output_container:
+#     # For every message in the history
+#     for message in st.session_state.chat_history:
+#         # Skip the system message
+#         if message['role'] == 'system':
+#             continue
             
-        # Display the chat message using the correct avatar
-        with st.chat_message(message['role'], 
-                             avatar=st.session_state['avatars'][message['role']]):
-            st.markdown(message['content'])
+#         # Display the chat message using the correct avatar
+#         with st.chat_message(message['role'], 
+#                              avatar=st.session_state['avatars'][message['role']]):
+#             st.markdown(message['content'])
             
- # When the user enter new text:
-    if st.session_state.user_text:
+#  # When the user enter new text:
+#     if st.session_state.user_text:
         
-        # Display the user's new message immediately
-        with st.chat_message("user", 
-                             avatar=st.session_state.avatars['user']):
-            st.markdown(st.session_state.user_text)
+#         # Display the user's new message immediately
+#         with st.chat_message("user", 
+#                              avatar=st.session_state.avatars['user']):
+#             st.markdown(st.session_state.user_text)
             
-        # Display a spinner status bar while waiting for the response
-        with st.chat_message("assistant", 
-                             avatar=st.session_state.avatars['assistant']):
-
-            with st.spinner("Thinking..."):
-                # Call the Inference API with the system_prompt, user text, and history
-                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)
+#         # Display a spinner status bar while waiting for the response
+#         with st.chat_message("assistant", 
+#                              avatar=st.session_state.avatars['assistant']):
+
+#             with st.spinner("Thinking..."):
+#                 # Call the Inference API with the system_prompt, user text, and history
+#                 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)