Update app.py

app.py

@@ -3,109 +3,42 @@ from langchain_huggingface import HuggingFaceEndpoint
 import streamlit as st
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
+import requests
+from config import NASA_API_KEY  # Import the NASA API key from the configuration file
 
-model_id="mistralai/Mistral-7B-Instruct-v0.3"
+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")
+        token=os.getenv("HF_TOKEN")  # Hugging Face token from environment variable
     )
     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_nasa_apod():
+    """
+    Fetch the Astronomy Picture of the Day (APOD) from the NASA API.
+    """
+    url = f"https://api.nasa.gov/planetary/apod?api_key={NASA_API_KEY}"
+    response = requests.get(url)
+    if response.status_code == 200:
+        data = response.json()
+        return f"Title: {data['title']}\nExplanation: {data['explanation']}\nURL: {data['url']}"
+    else:
+        return "I couldn't fetch data from NASA right now. Please try again later."
 
 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.
+    if "NASA" in user_text or "space" in user_text:
+        nasa_response = get_nasa_apod()
+        chat_history.append({'role': 'user', 'content': user_text})
+        chat_history.append({'role': 'assistant', 'content': nasa_response})
+        return nasa_response, chat_history
 
-    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}"
@@ -114,55 +47,36 @@ def get_response(system_message, chat_history, user_text,
             "\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.")
+# Streamlit setup
+st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗")
+st.title("Personal Assistant")
+st.markdown(f"*This chatbot uses {model_id} and NASA's APIs to provide information and responses.*")
+
+# Initialize session state
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
     
-# Display chat messages
-with output_container:
-    # For every message in the history
+# Sidebar for settings
+if st.sidebar.button("Reset Chat"):
+    st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
+
+# Main chat interface
+user_input = st.chat_input(placeholder="Type your message here...")
+if user_input:
+    response, st.session_state.chat_history = get_response(
+        system_message="You are a helpful AI assistant.",
+        user_text=user_input,
+        chat_history=st.session_state.chat_history,
+        max_new_tokens=128
+    )
+    # Display messages
     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)
+        st.chat_message(message["role"]).write(message["content"])