Update app.py

api key send to environment

app.py

@@ -1,61 +1,64 @@
 import streamlit as st
 import taskingai
+import os
 import pprint as pp
 
-# Initialize the Tasking AI with your API key
-api_key = "tav93qvwboJoczXrVlJXA3z3XksD4kTG"
-taskingai.init(api_key)
-
-# Set up Streamlit page configuration
-st.set_page_config(
-    page_title="Research Paper Finder",
-    page_icon="🔍",
-    menu_items={
-        'About': "# Made by Prathamesh Khade"
-    }
-)
-
-# Title of the app
-st.title("Research Paper Finder")
-st.markdown("## Find the latest research papers.")
-
-
-
-# User input with text_area for multi-line input
-user_input = st.text_area(
-    "Enter your query:",
-    value="Get me a list of RAG papers from 2024. Include the source, title, author, publication date, a brief summary, githublink and a link to each paper.",
-    height=150
-)
-
-# Initialize the arxiv_qa_assistant
-assistants = taskingai.assistant.list_assistants()
-arxiv_qa_assistant = next((assistant for assistant in assistants if assistant.name == "arxivagent"), None)
-
-if arxiv_qa_assistant:
-    new_chat = taskingai.assistant.create_chat(assistant_id=arxiv_qa_assistant.assistant_id)
-
-    if st.button("Find Papers"):
-        with st.spinner("Finding papers..."):
-            user_message = taskingai.assistant.create_message(
-                assistant_id=arxiv_qa_assistant.assistant_id,
-                chat_id=new_chat.chat_id,
-                text=user_input
-            )
-            
-            assistant_message = taskingai.assistant.generate_message(
-                assistant_id=arxiv_qa_assistant.assistant_id,
-                chat_id=new_chat.chat_id
-            )
-            
-            # Extract the text content from the assistant_message
-            response_text = assistant_message.content.text
-            
-            # Display the result
-            st.success("Papers found!")
-            st.markdown(response_text)
+# Read the API key from an environment variable
+api_key = os.getenv("TASKINGAI_API_KEY")
+if not api_key:
+    st.error("API key not found. Please set the TASKINGAI_API_KEY environment variable.")
 else:
-    st.error("Could not find arxiv_qa_assistant. Please check the assistant name.")
+    # Initialize the Tasking AI with your API key
+    taskingai.init(api_key)
+
+    # Set up Streamlit page configuration
+    st.set_page_config(
+        page_title="Research Paper Finder",
+        page_icon="🔍",
+        menu_items={
+            'About': "# Made by Prathamesh Khade"
+        }
+    )
+
+    # Title of the app
+    st.title("Research Paper Finder")
+    st.markdown("## Find the latest research papers.")
+
+    # User input with text_area for multi-line input
+    user_input = st.text_area(
+        "Enter your query:",
+        value="Get me a list of RAG papers from 2024. Include the source, title, author, publication date, a brief summary, githublink and a link to each paper.",
+        height=150
+    )
+
+    # Initialize the arxiv_qa_assistant
+    assistants = taskingai.assistant.list_assistants()
+    arxiv_qa_assistant = next((assistant for assistant in assistants if assistant.name == "arxivagent"), None)
+
+    if arxiv_qa_assistant:
+        new_chat = taskingai.assistant.create_chat(assistant_id=arxiv_qa_assistant.assistant_id)
+
+        if st.button("Find Papers"):
+            with st.spinner("Finding papers..."):
+                user_message = taskingai.assistant.create_message(
+                    assistant_id=arxiv_qa_assistant.assistant_id,
+                    chat_id=new_chat.chat_id,
+                    text=user_input
+                )
+                
+                assistant_message = taskingai.assistant.generate_message(
+                    assistant_id=arxiv_qa_assistant.assistant_id,
+                    chat_id=new_chat.chat_id
+                )
+                
+                # Extract the text content from the assistant_message
+                response_text = assistant_message.content.text
+                
+                # Display the result
+                st.success("Papers found!")
+                st.markdown(response_text)
+    else:
+        st.error("Could not find arxiv_qa_assistant. Please check the assistant name.")
 
 # Run Streamlit app
 if __name__ == '__main__':