Update app.py

app.py

@@ -1,17 +1,13 @@
 import os
-import requests
-import streamlit as st
 from langchain_huggingface import HuggingFaceEndpoint
+import streamlit as st
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
-from transformers import pipeline  # for Sentiment Analysis
+import requests
 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,
@@ -33,51 +29,13 @@ 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={}):
-    sentiment = analyze_sentiment(user_text)
-    action = predict_action(user_text)
-
-    if action == "nasa_info":
+    if "NASA" in user_text or "space" in user_text:
         nasa_response = get_nasa_apod()
         chat_history.append({'role': 'user', 'content': user_text})
         chat_history.append({'role': 'assistant', 'content': nasa_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 nasa_response, chat_history
 
     hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)
 
@@ -95,15 +53,7 @@ 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?"
-
-    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, chat_history
 
 # Streamlit setup
 st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗")
@@ -131,35 +81,3 @@ 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)
-
-