Update app.py

app.py

@@ -1,168 +1,168 @@
-import os
-from langchain_huggingface import HuggingFaceEndpoint
-import streamlit as st
-from langchain_core.prompts import PromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-
-model_id="mistralai/Mistral-7B-Instruct-v0.3"
-
-def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.1):
-    """
-    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.
-
-    Returns:
-    - llm (HuggingFaceEndpoint): The language model for HuggingFace inference.
-    """
-    llm = HuggingFaceEndpoint(
-        repo_id=model_id,
-        max_new_tokens=max_new_tokens,
-        temperature=temperature,
-        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 "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
-    )
-
-    # 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.
-        model_id (str, optional): The ID of the HuggingFace model to use.
-        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(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:"
-        )
-    )
-    # 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 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:
-        
-        # 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,
-                )
+import os
+from langchain_huggingface import HuggingFaceEndpoint
+import streamlit as st
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+
+model_id="mistralai/Mistral-7B-v0.3"
+
+def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.1):
+    """
+    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.
+
+    Returns:
+    - llm (HuggingFaceEndpoint): The language model for HuggingFace inference.
+    """
+    llm = HuggingFaceEndpoint(
+        repo_id=model_id,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        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 "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
+    )
+
+    # 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.
+        model_id (str, optional): The ID of the HuggingFace model to use.
+        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(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:"
+        )
+    )
+    # 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 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:
+        
+        # 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)