Update app.py

app.py

@@ -1,22 +1,45 @@
-
 import streamlit as st
-import requests
-
-# st.title('Number Doubler')
-
-
-# user_input = st.number_input('Enter a number')
+from dotenv import load_dotenv
 
-# def double_number(number):
-#     url = 'http://localhost:8000/double_number' 
-#     data = {"number": number}
-#     response = requests.post(url, json=data)
-#     return response.json()
+load_dotenv()
 
 def kickoff(inputs={}):
-    return inputs
-    
-til_content = st.text_area('Enter what you learnt today:',
+    # Simulate feedback processing
+    return {"feedback": [{"til": inputs["content"], "feedback": "ok"}]}
+
+def main():
+    st.markdown("<div class='container'>", unsafe_allow_html=True)
+
+    st.markdown(
+        """
+        <div class="centered">
+            <p class="title">Today I Learnt Feedback</p>
+            <p class="description">Feedback on Today I Learnt</p>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+    # default_content = (
+    #     "* Quantization is the process of reducing the size of LLM models by reducing the underlying weights.\n"
+    #     "* The weights are reduced by scaling down the datatypes from a datatype that takes larger space to a data type that takes a smaller space, this is also known as downcasting.\n"
+    #     "* Quantization offers benefits such as reduced storage space usage and faster computation.\n"
+    #     "* Disadvantages: Answers are less precise\n"
+    #     "* I learnt how to use Go Routines to handle concurrency in React.\n"
+    # )
+
+    # # Initialize session state for the text area content if not already set
+    # if 'til_content' not in st.session_state:
+    #     st.session_state.til_content = default_content
+
+    # til_content = st.text_area(
+    #     'Enter what you learnt today:',
+    #     value=st.session_state.til_content,
+    #     key='til_content',
+    #     help='Enter what you learnt today'
+    # )
+
+    til_content = st.text_area('Enter what you learnt today:',
                                "* Quantization is the process of reducing the size of LLM models by reducing the underlying weights.\n"
                                "* The weights are reduced by scaling down the datatypes from a datatype that takes larger space to a data type that takes a smaller space, this is also known as downcasting.\n"
                                "* Quantization offers benefits such as reduced storage space usage and faster computation.\n"
@@ -24,14 +47,16 @@ til_content = st.text_area('Enter what you learnt today:',
                                "* I learnt how to use Go Routines to handle concurrency in React.\n",
                                key='til_content', help='Enter what you learnt today')
 
-if st.button("Get Feedback"):
-     inputs = {"content": til_content}
-     results = kickoff(inputs=inputs)
+    if st.button("Get Feedback"):
+        inputs = {"content": til_content}
+        results = kickoff(inputs=inputs)
+        
+        for result in results["feedback"]:
+            st.markdown(f"#### TIL: {result['til']}")
+            st.markdown(f"**Feedback:** {result['feedback']}")
 
-# if st.button('Double'):
-#     if user_input:
-#         result = double_number(user_input)
-#         st.write('Doubled Number:', result['doubled_number'])
-#     else:
-#         st.warning('Please enter a number')
+        # Update the session state with the new input content
+        st.session_state.til_content = til_content
 
+if __name__ == "__main__":
+    main()