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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 | |
NASA_API_KEY = os.getenv("NASA_API_KEY") | |
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"<div class='assistant-msg'><strong>HAL:</strong> {response}</div>", 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"<div class='assistant-msg'><strong>HAL:</strong> {response}</div>", unsafe_allow_html=True) |