Update app.py

app.py

@@ -4,8 +4,8 @@ import streamlit as st
 from langchain_huggingface import HuggingFaceEndpoint
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
-from transformers import pipeline  # for Sentiment Analysis
-from config import NASA_API_KEY  # Import the NASA API key from the configuration file
+from transformers import pipeline
+from config import NASA_API_KEY  # Import the NASA API key
 
 model_id = "mistralai/Mistral-7B-Instruct-v0.3"
 
@@ -23,41 +23,38 @@ def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.1)
 
 def get_nasa_apod():
     """
-    Fetch the Astronomy Picture of the Day (APOD) from the NASA API.
+    Fetch NASA Astronomy Picture of the Day (APOD).
     """
     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()
-        return f"Title: {data['title']}\nExplanation: {data['explanation']}\nURL: {data['url']}"
+        return data.get("url", ""), data.get("title", ""), data.get("explanation", "")
     else:
-        return "I couldn't fetch data from NASA right now. Please try again later."
+        return "", "NASA Data Unavailable", "I couldn't fetch data from NASA right now. Please try again later."
 
 def analyze_sentiment(user_text):
     """
-    Analyzes the sentiment of the user's input to adjust responses.
+    Analyze sentiment of user input.
     """
     result = sentiment_analyzer(user_text)[0]
-    sentiment = result['label']
-    return sentiment
+    return result['label']
 
 def predict_action(user_text):
     """
-    Predicts actions based on user input (e.g., fetch space info or general knowledge).
+    Predicts user's intent based on input.
     """
     if "NASA" in user_text or "space" in user_text:
         return "nasa_info"
-    if "weather" in user_text:
-        return "weather_info"
     return "general_query"
 
 def generate_follow_up(user_text):
     """
-    Generates a relevant follow-up question based on the user's input.
+    Generates a follow-up question to continue the conversation.
     """
     prompt_text = (
-        f"Given the user's message: '{user_text}', ask one natural follow-up question "
-        "that suggests a related topic or offers user the opportunity to go in a new direction."
+        f"Based on the user's message: '{user_text}', suggest a natural follow-up question "
+        "to keep the conversation engaging."
     )
 
     hf = get_llm_hf_inference(max_new_tokens=64, temperature=0.7)
@@ -65,30 +62,26 @@ def generate_follow_up(user_text):
     
     return chat.strip()
 
-def get_response(system_message, chat_history, user_text, 
-                 eos_token_id=['User'], max_new_tokens=256, get_llm_hf_kws={}):
+def get_response(system_message, chat_history, user_text, max_new_tokens=256):
     sentiment = analyze_sentiment(user_text)
     action = predict_action(user_text)
 
     if action == "nasa_info":
-        nasa_response = get_nasa_apod()
+        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})
-        chat_history.append({'role': 'assistant', 'content': nasa_response})
+        chat_history.append({'role': 'assistant', 'content': response})
         
         follow_up = generate_follow_up(user_text)
         chat_history.append({'role': 'assistant', 'content': follow_up})
-        return f"{nasa_response}\n\n{follow_up}", chat_history
+        return response, follow_up, chat_history, nasa_url
 
     hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)
 
     prompt = PromptTemplate.from_template(
-        (
-            "[INST] {system_message}"
-            "\nCurrent Conversation:\n{chat_history}\n\n"
-            "\nUser: {user_text}.\n [/INST]"
-            "\nAI:"
-        )
+        "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\nUser: {user_text}.\n [/INST]\nAI:"
     )
+
     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=chat_history))
     response = response.split("AI:")[-1]
@@ -96,37 +89,72 @@ def get_response(system_message, chat_history, user_text,
     chat_history.append({'role': 'user', 'content': user_text})
     chat_history.append({'role': 'assistant', 'content': response})
 
-    # Modify response based on sentiment analysis (e.g., offer help for negative sentiments)
     if sentiment == "NEGATIVE":
-        response += "\nI'm sorry to hear that. How can I assist you further?"
+        response += "\n😞 I'm sorry to hear that. How can I assist you further?"
 
     follow_up = generate_follow_up(user_text)
     chat_history.append({'role': 'assistant', 'content': follow_up})
 
-    return f"{response}\n\n{follow_up}", chat_history
+    return response, follow_up, chat_history, None
+
+# Streamlit UI Setup
+st.set_page_config(page_title="NASA ChatBot", page_icon="🚀")
 
-# Streamlit setup
-st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗")
-st.title("NASA Personal Assistant")
-st.markdown(f"*This chatbot uses {model_id} and NASA's APIs to provide information and responses.*")
+st.title("🚀 NASA AI ChatBot")
+st.markdown("🌌 *Powered by Hugging Face & NASA APIs!*")
 
-# Initialize session state
+# Custom CSS for chat styling
+st.markdown("""
+    <style>
+    .user-msg { background-color: #ADD8E6; padding: 10px; border-radius: 10px; margin-bottom: 5px; }
+    .assistant-msg { background-color: #F0F0F0; padding: 10px; border-radius: 10px; margin-bottom: 5px; }
+    @media (max-width: 600px) { .user-msg, .assistant-msg { font-size: 16px; } }
+    </style>
+""", unsafe_allow_html=True)
+
+# Initialize chat history
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
-    
-# Sidebar for settings
+
+# Sidebar for chat reset
 if st.sidebar.button("Reset Chat"):
     st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
+    st.experimental_rerun()
 
-# Main chat interface
-user_input = st.chat_input(placeholder="Type your message here...")
-if user_input:
-    response, st.session_state.chat_history = get_response(
-        system_message="You are a helpful AI assistant.",
-        user_text=user_input,
-        chat_history=st.session_state.chat_history,
-        max_new_tokens=128
-    )
-    # Display messages
-    for message in st.session_state.chat_history:
-        st.chat_message(message["role"]).write(message["content"])
+# Chat display
+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>Bot:</strong> {message['content']}</div>", unsafe_allow_html=True)
+
+# User input
+user_input = st.text_area("Type your message:", height=100)
+
+if st.button("Send"):
+    if user_input:
+        response, follow_up, st.session_state.chat_history, image_url = get_response(
+            system_message="You are a helpful AI assistant.",
+            user_text=user_input,
+            chat_history=st.session_state.chat_history
+        )
+
+        # Display response
+        st.markdown(f"<div class='assistant-msg'><strong>Bot:</strong> {response}</div>", unsafe_allow_html=True)
+
+        # Display NASA image if available
+        if image_url:
+            st.image(image_url, caption="NASA Image of the Day")
+
+        # Follow-up options
+        follow_up_options = [follow_up, "Explain differently", "Give me an example"]
+        selected_option = st.radio("What would you like to do next?", follow_up_options)
+
+        if st.button("Continue"):
+            if selected_option:
+                response, _, st.session_state.chat_history, _ = get_response(
+                    system_message="You are a helpful AI assistant.",
+                    user_text=selected_option,
+                    chat_history=st.session_state.chat_history
+                )
+                st.markdown(f"<div class='assistant-msg'><strong>Bot:</strong> {response}</div>", unsafe_allow_html=True)