Update app.py

app.py

@@ -1,13 +1,17 @@
 import os
-from langchain_huggingface import HuggingFaceEndpoint
+import requests
 import streamlit as st
+from langchain_huggingface import HuggingFaceEndpoint
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
-import requests
+from transformers import pipeline  # for Sentiment Analysis
 from config import NASA_API_KEY  # Import the NASA API key from the configuration file
 
 model_id = "mistralai/Mistral-7B-Instruct-v0.3"
 
+# Initialize sentiment analysis pipeline
+sentiment_analyzer = pipeline("sentiment-analysis")
+
 def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.1):
     llm = HuggingFaceEndpoint(
         repo_id=model_id,
@@ -29,13 +33,51 @@ def get_nasa_apod():
     else:
         return "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.
+    """
+    result = sentiment_analyzer(user_text)[0]
+    sentiment = result['label']
+    return sentiment
+
+def predict_action(user_text):
+    """
+    Predicts actions based on user input (e.g., fetch space info or general knowledge).
+    """
+    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.
+    """
+    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."
+    )
+
+    hf = get_llm_hf_inference(max_new_tokens=64, temperature=0.7)
+    chat = hf.invoke(input=prompt_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={}):
-    if "NASA" in user_text or "space" in user_text:
+    sentiment = analyze_sentiment(user_text)
+    action = predict_action(user_text)
+
+    if action == "nasa_info":
         nasa_response = get_nasa_apod()
         chat_history.append({'role': 'user', 'content': user_text})
         chat_history.append({'role': 'assistant', 'content': nasa_response})
-        return nasa_response, chat_history
+        
+        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
 
     hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)
 
@@ -53,7 +95,15 @@ def get_response(system_message, chat_history, user_text,
 
     chat_history.append({'role': 'user', 'content': user_text})
     chat_history.append({'role': 'assistant', 'content': response})
-    return response, chat_history
+
+    # 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?"
+
+    follow_up = generate_follow_up(user_text)
+    chat_history.append({'role': 'assistant', 'content': follow_up})
+
+    return f"{response}\n\n{follow_up}", chat_history
 
 # Streamlit setup
 st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗")
@@ -81,3 +131,33 @@ if user_input:
     for message in st.session_state.chat_history:
         st.chat_message(message["role"]).write(message["content"])
 
+
+
+
+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>HAL:</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 question suggestions
+        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>HAL:</strong> {response}</div>", unsafe_allow_html=True)