Update app.py

app.py

@@ -1,9 +1,10 @@
 import os
+import subprocess
 import re
 import random
 import requests
 import streamlit as st
-import spacy  # for additional NLP processing
+import spacy  # For additional NLP processing
 from langchain_huggingface import HuggingFaceEndpoint
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
@@ -12,8 +13,17 @@ from transformers import pipeline
 # Must be the first Streamlit command!
 st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
 
-# --- Appearance Section (optional) ---
-# (You can adjust CSS or appearance settings here if needed)
+# --- 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")
+
+# Load the spaCy model (used for extracting context)
+nlp_spacy = load_spacy_model()
 
 # --- Initialize Session State Variables ---
 if "chat_history" not in st.session_state:
@@ -23,17 +33,37 @@ if "response_ready" not in st.session_state:
 if "follow_up" not in st.session_state:
     st.session_state.follow_up = ""
 
-# --- Load spaCy Model for Additional NLP ---
-nlp_spacy = spacy.load("en_core_web_sm")
-
-def extract_context(text):
-    """
-    Extract key entities from the text using spaCy to provide extra context.
-    Returns a comma-separated string of entities (if any).
-    """
-    doc = nlp_spacy(text)
-    entities = [ent.text for ent in doc.ents]
-    return ", ".join(entities) if entities else ""
+# --- Appearance (Optional) ---
+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)
 
 # --- Set Up Model & API Functions ---
 model_id = "mistralai/Mistral-7B-Instruct-v0.3"
@@ -70,6 +100,15 @@ def predict_action(user_text):
         return "nasa_info"
     return "general_query"
 
+def extract_context(text):
+    """
+    Uses spaCy to extract named entities for additional context.
+    Returns a comma-separated string of entities, if any.
+    """
+    doc = nlp_spacy(text)
+    entities = [ent.text for ent in doc.ents]
+    return ", ".join(entities) if entities else ""
+
 def generate_follow_up(user_text):
     """
     Generates two variant follow-up questions and randomly selects one.
@@ -90,20 +129,17 @@ def generate_follow_up(user_text):
 
 def get_response(system_message, chat_history, user_text, max_new_tokens=256):
     """
-    Generates HAL's response with a detailed explanation and a follow-up question.
-    Uses sentiment analysis and extracts additional context from the user's text via spaCy.
+    Generates HAL's detailed, in-depth response with a follow-up question.
+    Incorporates sentiment analysis and additional NLP context extraction.
     """
     sentiment = analyze_sentiment(user_text)
     action = predict_action(user_text)
     
-    # Extract additional context using spaCy
+    # Extract additional context (e.g., named entities) using spaCy.
     context_info = extract_context(user_text)
-    if context_info:
-        context_clause = f" The key topics here are: {context_info}."
-    else:
-        context_clause = ""
+    context_clause = f" The key topics here are: {context_info}." if context_info else ""
     
-    # Extract style instruction if present.
+    # Extract style instructions if provided.
     style_instruction = ""
     lower_text = user_text.lower()
     if "in the voice of" in lower_text or "speaking as" in lower_text:
@@ -134,7 +170,7 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
         (
             "[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 covers the topic thoroughly." 
+            "AI: Please provide a detailed, in-depth answer in a friendly, conversational tone that thoroughly covers the topic."
             + style_clause + context_clause +
             "\nHAL:"
         )
@@ -167,7 +203,6 @@ if st.sidebar.button("Reset Chat"):
     st.session_state.follow_up = ""
     st.experimental_rerun()
 
-# Render the chat history.
 st.markdown("<div class='container'>", unsafe_allow_html=True)
 for message in st.session_state.chat_history:
     if message["role"] == "user":