Update app.py

app.py

@@ -1,7 +1,7 @@
 import os
-import subprocess
 import re
 import random
+import subprocess
 import requests
 import streamlit as st
 import spacy  # For additional NLP processing
@@ -22,7 +22,6 @@ def load_spacy_model():
         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 ---
@@ -33,38 +32,6 @@ if "response_ready" not in st.session_state:
 if "follow_up" not in st.session_state:
     st.session_state.follow_up = ""
 
-# --- Appearance (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"
 sentiment_analyzer = pipeline(
@@ -102,18 +69,13 @@ def predict_action(user_text):
 
 def extract_context(text):
     """
-    Uses spaCy to extract named entities for additional context.
-    Returns a comma-separated string of entities, if any.
+    Extract key entities using spaCy for additional context.
     """
     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.
-    Cleans up extraneous quotation marks.
-    """
     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?' "
@@ -128,18 +90,14 @@ 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 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 (e.g., named entities) using spaCy.
+    # 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 provided.
+    # 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:
@@ -165,22 +123,27 @@ 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.
     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."
+            "AI: Please provide a detailed, in-depth answer in a friendly, conversational tone that thoroughly covers the topic." 
             + style_clause + context_clause +
             "\nHAL:"
         )
     )
     
+    # Debug: Output the prompt being sent (for troubleshooting)
+    st.write("DEBUG: Prompt sent to language 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')
     response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=filtered_history))
     response = response.split("HAL:")[-1].strip()
     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})
     
@@ -191,6 +154,9 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
     follow_up = generate_follow_up(user_text)
     chat_history.append({'role': 'assistant', 'content': follow_up})
     
+    # Debug: Output generated follow-up for troubleshooting.
+    st.write("DEBUG: Generated follow-up question:", follow_up)
+    
     return response, follow_up, chat_history, None
 
 # --- Chat UI ---