Update app.py

app.py

@@ -1,200 +1,83 @@
 import os
-import requests
-import streamlit as st
 from langchain_huggingface import HuggingFaceEndpoint
+import streamlit as st
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
-from transformers import pipeline
-
-# Use environment variables for keys
-HF_TOKEN = os.getenv("HF_TOKEN")
-if HF_TOKEN is None:
-    raise ValueError("HF_TOKEN environment variable not set. Please set it in your Hugging Face Space settings.")
-
-NASA_API_KEY = os.getenv("NASA_API_KEY")
-if NASA_API_KEY is None:
-    raise ValueError("NASA_API_KEY environment variable not set. Please set it in your Hugging Face Space settings.")
-
-# Set up Streamlit UI
-st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
-
-# --- Initialize Session State Variables ---
-if "chat_history" not in st.session_state:
-    # Initial greeting stored in chat history
-    st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
-
-if "response_ready" not in st.session_state:
-    st.session_state.response_ready = False  # Tracks whether HAL has responded
-
-if "follow_up" not in st.session_state:
-    st.session_state.follow_up = ""  # Stores follow-up question
+import requests
+from config import NASA_API_KEY  # Import the NASA API key from the configuration file
 
-# --- Set Up Model & API Functions ---
 model_id = "mistralai/Mistral-7B-Instruct-v0.3"
 
-# Initialize sentiment analysis pipeline with explicit model specification
-sentiment_analyzer = pipeline(
-    "sentiment-analysis",
-    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english",
-    revision="714eb0f"
-)
-
-def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.7):
-    # Specify task="text-generation" so that the endpoint uses the right model function.
-    return HuggingFaceEndpoint(
+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=HF_TOKEN,
-        task="text-generation"
+        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 data.get("url", ""), data.get("title", ""), data.get("explanation", "")
+        return f"Title: {data['title']}\nExplanation: {data['explanation']}\nURL: {data['url']}"
     else:
-        return "", "NASA Data Unavailable", "I couldn't fetch data from NASA right now. Please try again later."
+        return "I couldn't fetch data from NASA right now. Please try again later."
 
-def analyze_sentiment(user_text):
-    result = sentiment_analyzer(user_text)[0]
-    return result['label']
-
-def predict_action(user_text):
+def get_response(system_message, chat_history, user_text, 
+                 eos_token_id=['User'], max_new_tokens=256, get_llm_hf_kws={}):
     if "NASA" in user_text or "space" in user_text:
-        return "nasa_info"
-    return "general_query"
-
-def generate_follow_up(user_text):
-    """
-    Generates a concise and conversational follow-up question related to the user's input.
-    """
-    prompt_text = (
-        f"Given the user's question: '{user_text}', generate a SHORT and SIMPLE follow-up question. "
-        "Make it conversational and friendly. Example: 'Would you like to learn more about the six types of quarks?' "
-        "Do NOT provide long explanations—just ask a friendly follow-up question."
-    )
-    hf = get_llm_hf_inference(max_new_tokens=32, temperature=0.7)
-    return hf.invoke(input=prompt_text).strip()
-
-def get_response(system_message, chat_history, user_text, max_new_tokens=256):
-    """
-    Generates HAL's response in a friendly, conversational manner.
-    The prompt instructs the model to ignore previous greetings and focus on the new user question.
-    """
-    sentiment = analyze_sentiment(user_text)
-    action = predict_action(user_text)
-
-    if action == "nasa_info":
-        nasa_url, nasa_title, nasa_explanation = get_nasa_apod()
-        response = f"**{nasa_title}**\n\n{nasa_explanation}"
+        nasa_response = get_nasa_apod()
         chat_history.append({'role': 'user', 'content': user_text})
-        chat_history.append({'role': 'assistant', 'content': response})
-        
-        follow_up = generate_follow_up(user_text)
-        chat_history.append({'role': 'assistant', 'content': follow_up})
-        return response, follow_up, chat_history, nasa_url
+        chat_history.append({'role': 'assistant', 'content': nasa_response})
+        return nasa_response, chat_history
 
-    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.9)
+    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)
 
-    # Updated prompt: Instruct the model not to repeat previous greetings.
     prompt = PromptTemplate.from_template(
         (
-            "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\n"
-            "User: {user_text}.\n [/INST]\n"
-            "AI: Please answer the user's question without repeating any previous greetings. "
-            "Keep your response friendly and conversational, starting with a phrase like "
-            "'Certainly!', 'Of course!', or 'Great question!'.\nHAL:"
+            "[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("HAL:")[-1].strip()
+    response = response.split("AI:")[-1]
 
     chat_history.append({'role': 'user', 'content': user_text})
     chat_history.append({'role': 'assistant', 'content': response})
+    return response, chat_history
 
-    if sentiment == "NEGATIVE":
-        response = "I'm here to help. Let me know what I can do for you. 😊"
+# 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.*")
 
-    follow_up = generate_follow_up(user_text)
-    chat_history.append({'role': 'assistant', 'content': follow_up})
-
-    return response, follow_up, chat_history, None
-
-# --- Chat UI ---
-st.title("🚀 HAL - Your NASA AI Assistant")
-st.markdown("🌌 *Ask me about space, NASA, and beyond!*")
-
-# Sidebar: Reset Chat
+# 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?"}]
-    st.session_state.response_ready = False
-    st.session_state.follow_up = ""
-    st.experimental_rerun()
-
-# Custom Chat Styling
-st.markdown("""
-    <style>
-    .user-msg {
-        background-color: #0078D7;
-        color: white;
-        padding: 10px;
-        border-radius: 10px;
-        margin-bottom: 5px;
-        width: fit-content;
-        max-width: 80%;
-    }
-    .assistant-msg {
-        background-color: #333333;
-        color: white;
-        padding: 10px;
-        border-radius: 10px;
-        margin-bottom: 5px;
-        width: fit-content;
-        max-width: 80%;
-    }
-    .container {
-        display: flex;
-        flex-direction: column;
-        align-items: flex-start;
-    }
-    @media (max-width: 600px) {
-        .user-msg, .assistant-msg { font-size: 16px; max-width: 100%; }
-    }
-    </style>
-""", unsafe_allow_html=True)
-
-# Chat History Display
-st.markdown("<div class='container'>", unsafe_allow_html=True)
-for message in st.session_state.chat_history:
-    if message["role"] == "user":
-        st.markdown(f"<div class='user-msg'><strong>You:</strong> {message['content']}</div>", unsafe_allow_html=True)
-    else:
-        st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {message['content']}</div>", unsafe_allow_html=True)
-st.markdown("</div>", unsafe_allow_html=True)
-
-# --- Single Input Box for Both Initial and Follow-Up Messages ---
-user_input = st.chat_input("Type your message here...")  # Only ONE chat_input()
 
+# Main chat interface
+user_input = st.chat_input(placeholder="Type your message here...")
 if user_input:
-    response, follow_up, st.session_state.chat_history, image_url = get_response(
+    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
+        chat_history=st.session_state.chat_history,
+        max_new_tokens=128
     )
-
-    st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {response}</div>", unsafe_allow_html=True)
-
-    if image_url:
-        st.image(image_url, caption="NASA Image of the Day")
-
-    st.session_state.follow_up = follow_up
-    st.session_state.response_ready = True
-
-if st.session_state.response_ready and st.session_state.follow_up:
-    st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {st.session_state.follow_up}</div>", unsafe_allow_html=True)
-    st.session_state.response_ready = False
+    # Display messages
+    for message in st.session_state.chat_history:
+        st.chat_message(message["role"]).write(message["content"])