Update app.py

app.py

@@ -1,28 +1,24 @@
 import os
 import re
 import random
-import subprocess
 import requests
 import streamlit as st
-import spacy  # For additional NLP processing
 from langchain_huggingface import HuggingFaceEndpoint
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 from transformers import pipeline
 
-# Must be the first Streamlit command!
-st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
+# 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.")
 
-# --- Helper to load spaCy model with fallback ---
-def load_spacy_model():
-    try:
-        return spacy.load("en_core_web_sm")
-    except OSError:
-        st.warning("Downloading spaCy model en_core_web_sm... This may take a moment.")
-        subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"], check=True)
-        return spacy.load("en_core_web_sm")
+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.")
 
-nlp_spacy = load_spacy_model()
+# 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:
@@ -45,12 +41,12 @@ def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.7)
         repo_id=model_id,
         max_new_tokens=max_new_tokens,
         temperature=temperature,
-        token=os.getenv("HF_TOKEN"),
+        token=HF_TOKEN,
         task="text-generation"
     )
 
 def get_nasa_apod():
-    url = f"https://api.nasa.gov/planetary/apod?api_key={os.getenv('NASA_API_KEY')}"
+    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()
@@ -63,23 +59,19 @@ def analyze_sentiment(user_text):
     return result['label']
 
 def predict_action(user_text):
-    if "nasa" in user_text.lower() or "space" in user_text.lower():
+    if "NASA" in user_text or "space" in user_text:
         return "nasa_info"
     return "general_query"
 
-def extract_context(text):
+def generate_follow_up(user_text):
     """
-    Extract key entities using spaCy for additional context.
+    Generates two variant follow-up questions and randomly selects one.
+    It also cleans up any unwanted quotation marks or extra meta commentary.
     """
-    doc = nlp_spacy(text)
-    entities = [ent.text for ent in doc.ents]
-    return ", ".join(entities) if entities else ""
-
-def generate_follow_up(user_text):
     prompt_text = (
         f"Based on the user's question: '{user_text}', generate two concise, friendly follow-up questions "
         "that invite further discussion. For example, one might be 'Would you like to know more about the six types of quarks?' "
-        "and another 'Would you like to explore another aspect of quantum physics?'. Do not include extra commentary."
+        "and another might be 'Would you like to explore another aspect of quantum physics?' Do not include extra commentary."
     )
     hf = get_llm_hf_inference(max_new_tokens=80, temperature=0.9)
     output = hf.invoke(input=prompt_text).strip()
@@ -90,14 +82,15 @@ def generate_follow_up(user_text):
     return random.choice(cleaned)
 
 def get_response(system_message, chat_history, user_text, max_new_tokens=256):
+    """
+    Generates HAL's answer with depth and a follow-up question.
+    The prompt instructs the model to provide a detailed explanation and then generate a follow-up.
+    If the answer comes back empty, a fallback answer is used.
+    """
     sentiment = analyze_sentiment(user_text)
     action = predict_action(user_text)
     
-    # Extract extra context from user's text.
-    context_info = extract_context(user_text)
-    context_clause = f" The key topics here are: {context_info}." if context_info else ""
-    
-    # Extract style instructions if present.
+    # Extract style instruction if present
     style_instruction = ""
     lower_text = user_text.lower()
     if "in the voice of" in lower_text or "speaking as" in lower_text:
@@ -123,17 +116,39 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
         filtered_history += f"{message['role']}: {message['content']}\n"
     
     style_clause = style_instruction if style_instruction else ""
-    # Construct prompt with additional context.
+    
+    # Instruct the model to generate a detailed, in-depth answer.
     prompt = PromptTemplate.from_template(
         (
             "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\n"
             "User: {user_text}.\n [/INST]\n"
-            "AI: Please provide a detailed, in-depth answer in a friendly, conversational tone that thoroughly covers the topic." 
-            + style_clause + context_clause +
+            "AI: Please provide a detailed explanation in depth. "
+            "Ensure your response covers the topic thoroughly and is written in a friendly, conversational style, "
+            "starting with a phrase like 'Certainly!', 'Of course!', or 'Great question!'." + style_clause +
             "\nHAL:"
         )
     )
     
+    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=filtered_history))
+    # Remove any extra markers if present.
+    response = response.split("HAL:")[-1].strip()
+    
+    # Fallback in case the generated answer is empty
+    if not response:
+        response = "Certainly, here is an in-depth explanation: [Fallback explanation]."
+    
+    chat_history.append({'role': 'user', 'content': user_text})
+    chat_history.append({'role': 'assistant', 'content': response})
+    
+    if sentiment == "NEGATIVE" and not user_text.strip().endswith("?"):
+        response = "I'm sorry you're feeling this way. I'm here to help. What can I do to assist you further?"
+        chat_history[-1]['content'] = response
+    
+    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")
@@ -145,13 +160,36 @@ if st.sidebar.button("Reset Chat"):
     st.session_state.follow_up = ""
     st.experimental_rerun()
 
-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)
+st.markdown("""
+    <style>
+    .user-msg {
+        background-color: #696969;
+        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)
 
 user_input = st.chat_input("Type your message here...")
 
@@ -165,3 +203,11 @@ if user_input:
         st.image(image_url, caption="NASA Image of the Day")
     st.session_state.follow_up = follow_up
     st.session_state.response_ready = True
+
+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)