Update app.py

app.py

@@ -1,7 +1,6 @@
 import os
 import re
 import random
-import subprocess
 import requests
 import streamlit as st
 import spacy  # for additional NLP processing
@@ -18,6 +17,8 @@ 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.")
+        import subprocess
         subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"], check=True)
         return spacy.load("en_core_web_sm")
 
@@ -31,38 +32,6 @@ if "response_ready" not in st.session_state:
 if "follow_up" not in st.session_state:
     st.session_state.follow_up = ""
 
-# --- Appearance CSS ---
-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"
 sentiment_analyzer = pipeline(
@@ -99,7 +68,9 @@ def predict_action(user_text):
     return "general_query"
 
 def extract_context(text):
-    """Extract key entities using spaCy."""
+    """
+    Uses spaCy to extract named entities for additional context.
+    """
     doc = nlp_spacy(text)
     entities = [ent.text for ent in doc.ents]
     return ", ".join(entities) if entities else ""
@@ -121,17 +92,18 @@ def generate_follow_up(user_text):
         cleaned = ["Would you like to explore this topic further?"]
     return random.choice(cleaned)
 
-def get_response(system_message, chat_history, user_text, max_new_tokens=1024):
+def get_response(system_message, chat_history, user_text, max_new_tokens=512):
     """
-    Generates HAL's detailed, in-depth answer and a follow-up question.
-    Incorporates sentiment analysis, additional NLP context, and style instructions.
+    Generates HAL's detailed answer and a follow-up question.
     """
     sentiment = analyze_sentiment(user_text)
     action = predict_action(user_text)
     
+    # Extract extra context
     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.
     style_instruction = ""
     lower_text = user_text.lower()
     if "in the voice of" in lower_text or "speaking as" in lower_text:
@@ -140,8 +112,6 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=1024):
             style_instruction = match.group(2).strip().capitalize()
             style_instruction = f" Please respond in the voice of {style_instruction}."
     
-    language_clause = " Answer exclusively in English."
-    
     if action == "nasa_info":
         nasa_url, nasa_title, nasa_explanation = get_nasa_apod()
         response = f"**{nasa_title}**\n\n{nasa_explanation}"
@@ -165,13 +135,20 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=1024):
             "[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 + language_clause +
+            + style_clause + context_clause +
             "\nHAL:"
         )
     )
     
+    # Debug: print the prompt for troubleshooting
+    st.write("DEBUG: Prompt sent to model:")
+    st.write(prompt.format(system_message=system_message, chat_history=filtered_history, user_text=user_text))
+    
     chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
     raw_output = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=filtered_history))
+    st.write("DEBUG: Raw model output:")
+    st.write(raw_output)
+    
     response = raw_output.split("HAL:")[-1].strip()
     if not response:
         response = "Certainly, here is an in-depth explanation: [Fallback explanation]."
@@ -186,6 +163,8 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=1024):
     follow_up = generate_follow_up(user_text)
     chat_history.append({'role': 'assistant', 'content': follow_up})
     
+    st.write("DEBUG: Generated follow-up question:", follow_up)
+    
     return response, follow_up, chat_history, None
 
 # --- Chat UI ---