import os
import requests
import streamlit as st
from langchain_huggingface import HuggingFaceEndpoint
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from transformers import pipeline # for Sentiment Analysis
from config import NASA_API_KEY # Import the NASA API key from the configuration file
model_id = "mistralai/Mistral-7B-Instruct-v0.3"
# Initialize sentiment analysis pipeline
sentiment_analyzer = pipeline("sentiment-analysis")
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") # Hugging Face token from environment variable
)
return llm
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 analyze_sentiment(user_text):
"""
Analyzes the sentiment of the user's input to adjust responses.
"""
result = sentiment_analyzer(user_text)[0]
sentiment = result['label']
return sentiment
def predict_action(user_text):
"""
Predicts actions based on user input (e.g., fetch space info or general knowledge).
"""
if "NASA" in user_text or "space" in user_text:
return "nasa_info"
if "weather" in user_text:
return "weather_info"
return "general_query"
def generate_follow_up(user_text):
"""
Generates a relevant follow-up question based on the user's input.
"""
prompt_text = (
f"Given the user's message: '{user_text}', ask one natural follow-up question "
"that suggests a related topic or offers user the opportunity to go in a new direction."
)
hf = get_llm_hf_inference(max_new_tokens=64, temperature=0.7)
chat = hf.invoke(input=prompt_text)
return chat.strip()
def get_response(system_message, chat_history, user_text,
eos_token_id=['User'], max_new_tokens=256, get_llm_hf_kws={}):
sentiment = analyze_sentiment(user_text)
action = predict_action(user_text)
if action == "nasa_info":
nasa_response = get_nasa_apod()
chat_history.append({'role': 'user', 'content': user_text})
chat_history.append({'role': 'assistant', 'content': nasa_response})
follow_up = generate_follow_up(user_text)
chat_history.append({'role': 'assistant', 'content': follow_up})
return f"{nasa_response}\n\n{follow_up}", chat_history
hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)
prompt = PromptTemplate.from_template(
(
"[INST] {system_message}"
"\nCurrent Conversation:\n{chat_history}\n\n"
"\nUser: {user_text}.\n [/INST]"
"\nAI:"
)
)
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]
chat_history.append({'role': 'user', 'content': user_text})
chat_history.append({'role': 'assistant', 'content': response})
# Modify response based on sentiment analysis (e.g., offer help for negative sentiments)
if sentiment == "NEGATIVE":
response += "\nI'm sorry to hear that. How can I assist you further?"
follow_up = generate_follow_up(user_text)
chat_history.append({'role': 'assistant', 'content': follow_up})
return f"{response}\n\n{follow_up}", chat_history
# Streamlit setup
st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗")
st.title("NASA 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?"}]
# 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:
st.chat_message(message["role"]).write(message["content"])
if st.button("Send"):
if user_input:
response, follow_up, st.session_state.chat_history, image_url = get_response(
system_message="You are a helpful AI assistant.",
user_text=user_input,
chat_history=st.session_state.chat_history
)
# Display response
st.markdown(f"HAL: {response}
", unsafe_allow_html=True)
# Display NASA image if available
if image_url:
st.image(image_url, caption="NASA Image of the Day")
# Follow-up question suggestions
follow_up_options = [follow_up, "Explain differently", "Give me an example"]
selected_option = st.radio("What would you like to do next?", follow_up_options)
if st.button("Continue"):
if selected_option:
response, _, st.session_state.chat_history, _ = get_response(
system_message="You are a helpful AI assistant.",
user_text=selected_option,
chat_history=st.session_state.chat_history
)
st.markdown(f"HAL: {response}
", unsafe_allow_html=True)