Update app.py

app.py

@@ -1,10 +1,8 @@
 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
@@ -19,7 +17,7 @@ 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.")
 
-# Must be the first Streamlit command!
+# Set up Streamlit UI
 st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
 
 # --- Initialize Session State Variables ---
@@ -38,27 +36,17 @@ sentiment_analyzer = pipeline(
     revision="714eb0f"
 )
 
-# --- Helper to load spaCy model with fallback ---
-def load_spacy_model():
-    try:
-        return spacy.load("en_core_web_sm")
-    except OSError:
-        subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"], check=True)
-        return spacy.load("en_core_web_sm")
-
-nlp_spacy = load_spacy_model()
-
 def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.7):
     return HuggingFaceEndpoint(
         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()
@@ -66,23 +54,6 @@ def get_nasa_apod():
     else:
         return "", "NASA Data Unavailable", "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):
-    if "nasa" in user_text.lower() or "space" in user_text.lower():
-        return "nasa_info"
-    return "general_query"
-
-def extract_context(text):
-    """
-    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 ""
-
 def is_apod_query(user_text):
     """
     Checks if the user's question contains keywords indicating they are asking for
@@ -91,15 +62,25 @@ def is_apod_query(user_text):
     keywords = ["apod", "image", "picture", "photo", "astronomy picture"]
     return any(keyword in user_text.lower() for keyword in keywords)
 
+def analyze_sentiment(user_text):
+    result = sentiment_analyzer(user_text)[0]
+    return result['label']
+
+def predict_action(user_text):
+    if "NASA" in user_text or "space" in user_text:
+        return "nasa_info"
+    return "general_query"
+
 def generate_follow_up(user_text):
     """
     Generates two variant follow-up questions and randomly selects one.
+    It also cleans up any unwanted quotation marks or extra meta commentary.
     """
     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. "
-        "Answer exclusively in English."
+        "and another might be 'Would you like to explore another aspect of quantum physics?' Do not include extra commentary ."
+        "Answer exclusively in English, and do not include extra commentary."
     )
     hf = get_llm_hf_inference(max_new_tokens=80, temperature=0.9)
     output = hf.invoke(input=prompt_text).strip()
@@ -109,16 +90,25 @@ 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 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)
     
-    # If the user's NASA-related query is specifically an APOD query, handle it specially.
-    if action == "nasa_info" and is_apod_query(user_text):
+    # 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:
+        match = re.search(r"(in the voice of|speaking as)(.*)", lower_text)
+        if match:
+            style_instruction = match.group(2).strip().capitalize()
+            style_instruction = f" Please respond in the voice of {style_instruction}."
+    
+    if action == "nasa_info":
         nasa_url, nasa_title, nasa_explanation = get_nasa_apod()
         response = f"**{nasa_title}**\n\n{nasa_explanation}"
         chat_history.append({'role': 'user', 'content': user_text})
@@ -126,8 +116,7 @@ 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})
         return response, follow_up, chat_history, nasa_url
-
-    # Otherwise, treat NASA-related queries as general queries.
+    
     hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.9)
     filtered_history = ""
     for message in chat_history:
@@ -135,34 +124,27 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=1024):
             continue
         filtered_history += f"{message['role']}: {message['content']}\n"
     
-    # 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:
-        match = re.search(r"(in the voice of|speaking as)(.*)", lower_text)
-        if match:
-            style_instruction = match.group(2).strip().capitalize()
-            style_instruction = f" Please respond in the voice of {style_instruction}."
-    
-    context_info = extract_context(user_text)
-    context_clause = f" The key topics here are: {context_info}." if context_info else ""
-    language_clause = " Answer exclusively in English."
-    
     style_clause = style_instruction if style_instruction else ""
     
+    # 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 + language_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!'." 
+            "Answer exclusively in English, and do not include extra commentary."+ style_clause +
             "\nHAL:"
         )
     )
     
     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))
-    response = raw_output.split("HAL:")[-1].strip()
+    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]."
     
@@ -182,13 +164,14 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=1024):
 st.title("🚀 HAL - Your NASA AI Assistant")
 st.markdown("🌌 *Ask me about space, NASA, and beyond!*")
 
+#Reset Button
 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()
 
-# --- Appearance CSS ---
+#Style and Appearance
 st.markdown("""
     <style>
     .user-msg {
@@ -220,14 +203,6 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=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)
-
 user_input = st.chat_input("Type your message here...")
 
 if user_input:
@@ -240,3 +215,11 @@ 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)