Update app.py

app.py

@@ -5,7 +5,16 @@ from langchain_huggingface import HuggingFaceEndpoint
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 from transformers import pipeline
-from config import NASA_API_KEY  # Ensure this file exists with your NASA API Key
+
+# Use the HF_TOKEN environment variable (make sure it's set in your Hugging Face Space)
+HF_TOKEN = os.getenv("HF_TOKEN")
+if HF_TOKEN is None:
+    raise ValueError("HF_TOKEN environment variable not set. Please set it in your Hugging Face Space settings.")
+
+# Use environment variable for the NASA API key too
+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.")
 
 # Set up Streamlit UI
 st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
@@ -21,6 +30,7 @@ if "follow_up" not in st.session_state:
     st.session_state.follow_up = ""  # Stores follow-up question
 
 # --- Set Up Model & API Functions ---
+# Specify your intended model here. If you need to change it, update the model_id.
 model_id = "mistralai/Mistral-7B-Instruct-v0.3"
 
 # Initialize sentiment analysis pipeline with explicit model specification
@@ -31,12 +41,12 @@ sentiment_analyzer = pipeline(
 )
 
 def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.7):
-    # Explicitly specify task="text-generation" so that the endpoint knows which task to run
+    # Explicitly specify task="text-generation" so that the endpoint knows which task to run.
     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"
     )
 
@@ -91,7 +101,8 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=256):
 
     prompt = PromptTemplate.from_template(
         (
-            "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\nUser: {user_text}.\n [/INST]\n"
+            "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\n"
+            "User: {user_text}.\n [/INST]\n"
             "AI: Keep responses conversational and engaging. Start with a friendly phrase like "
             "'Certainly!', 'Of course!', or 'Great question!' before answering. "
             "Keep responses concise but engaging.\nHAL:"
@@ -185,3 +196,4 @@ if user_input:
 if st.session_state.response_ready and st.session_state.follow_up:
     st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {st.session_state.follow_up}</div>", unsafe_allow_html=True)
     st.session_state.response_ready = False
+