Update app.py
Browse files
app.py
CHANGED
@@ -1,83 +1,65 @@
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import streamlit as st
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import pandas as pd
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def save_data():
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sem_df = pd.DataFrame(sem_mem)
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sem_df.to_csv("semantic_memory.csv", index=False)
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epi_df = pd.DataFrame(epi_mem)
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epi_df.to_csv("episodic_memory.csv", index=False)
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def load_data():
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try:
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sem_df = pd.read_csv("semantic_memory.csv")
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sem_mem = sem_df.to_dict("records")
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except:
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sem_mem = [{"fact": "The Earth is round", "category": "science", "source": "NASA"}, {"fact": "Pizza is delicious", "category": "food", "source": "me"}]
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try:
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epi_df = pd.read_csv("episodic_memory.csv")
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epi_mem = epi_df.to_dict("records")
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except:
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epi_mem = [{"event": "I went to the beach", "sentiment": "happy", "date": "2022-02-28"}, {"event": "I had a fight with my friend", "sentiment": "sad", "date": "2022-02-25"}]
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return sem_mem, epi_mem
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# Define function to add a new fact to semantic memory
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def add_fact(fact, category, source):
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sem_mem.append({"fact": fact, "category": category, "source": source})
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def add_event(event, sentiment, date):
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epi_mem.append({"event": event, "sentiment": sentiment, "date": date})
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st.write("# Semantic Memory")
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for item in sem_mem:
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st.write(f"**{item['fact']}** ({item['category']}) - {item['source']}")
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st.write("# Episodic Memory")
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for item in epi_mem:
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st.write(f"**{item['event']}** ({item['sentiment']}) - {item['date']}")
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option = st.sidebar.selectbox("Select an option", ["View Semantic Memory", "View Episodic Memory", "Add Fact to Semantic Memory", "Add Event to Episodic Memory"])
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#
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display_sem_mem()
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elif option == "View Episodic Memory":
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display_epi_mem()
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elif option == "Add Fact to Semantic Memory":
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fact = st.text_input("Enter a fact")
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category = st.text_input("Enter a category")
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source = st.text_input("Enter a source")
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if st.button("Add Fact"):
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add_fact(fact, category, source)
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save_data()
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st.success("Fact added to semantic memory!")
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st.sidebar.success("Fact added to semantic memory!")
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elif option == "Add Event to Episodic Memory":
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event = st.text_input("Enter an event")
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sentiment = st.selectbox("Select a sentiment", ["happy", "sad", "neutral"])
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date = st.date_input("Select a date")
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if st.button("Add Event"):
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add_event(event, sentiment, date)
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save_data()
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st.success("Event added to episodic memory!")
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st.sidebar.success("Event added to episodic memory!")
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else:
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st.write("Please select an option from the sidebar.")
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# This program uses Streamlit to create a web app that allows the user to view and add to both semantic and episodic memory. The semantic memory is stored as a list of dictionaries, where each dictionary represents a fact and includes the fact itself, the category it belongs to, and the source of the fact. The episodic memory is also stored as a list of dictionaries, where each dictionary represents an event and includes the event itself, the sentiment associated with the event, and the date the event occurred.
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import streamlit as st
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import pandas as pd
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def load_data():
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sem_df = pd.read_csv("semantic_memory.csv")
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sem_mem = sem_df.to_dict("records")
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epi_df = pd.read_csv("episodic_memory.csv")
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epi_mem = epi_df.to_dict("records")
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return sem_mem, epi_mem
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def save_data(sem_mem, epi_mem):
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sem_df = pd.DataFrame(sem_mem)
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sem_df.to_csv("semantic_memory.csv", index=False)
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epi_df = pd.DataFrame(epi_mem)
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epi_df.to_csv("episodic_memory.csv", index=False)
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def add_fact(sem_mem, fact, category, source):
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sem_mem.append({"fact": fact, "category": category, "source": source})
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def add_event(epi_mem, event, sentiment, date):
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epi_mem.append({"event": event, "sentiment": sentiment, "date": date})
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def run_app():
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sem_mem, epi_mem = load_data()
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st.title("Cognitive Agent")
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option = st.sidebar.selectbox("Select an option", ["View Semantic Memory", "View Episodic Memory", "Add Fact to Semantic Memory", "Add Event to Episodic Memory"])
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if option == "View Semantic Memory":
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st.write("# Semantic Memory")
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for item in sem_mem:
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st.write(f"**{item['fact']}** ({item['category']}) - {item['source']}")
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elif option == "View Episodic Memory":
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st.write("# Episodic Memory")
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for item in epi_mem:
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st.write(f"**{item['event']}** ({item['sentiment']}) - {item['date']}")
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elif option == "Add Fact to Semantic Memory":
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fact = st.text_input("Enter a fact")
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category = st.text_input("Enter a category")
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source = st.text_input("Enter a source")
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if st.button("Add Fact"):
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add_fact(sem_mem, fact, category, source)
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save_data(sem_mem, epi_mem)
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st.success("Fact added to semantic memory!")
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st.sidebar.success("Fact added to semantic memory!")
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elif option == "Add Event to Episodic Memory":
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event = st.text_input("Enter an event")
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sentiment = st.selectbox("Select a sentiment", ["happy", "sad", "neutral"])
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date = st.date_input("Select a date")
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if st.button("Add Event"):
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add_event(epi_mem, event, sentiment, date)
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save_data(sem_mem, epi_mem)
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st.success("Event added to episodic memory!")
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st.sidebar.success("Event added to episodic memory!")
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else:
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st.write("Please select an option from the sidebar.")
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if __name__ == "__main__":
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run_app()
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# AW: Restructure the code listing into four functions. shorten the code by eliminating comments and unnecessary whitespace and empty lines.
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# AI: This revised code splits the app into four functions: load_data, save_data, add_fact, and add_event. The run_app function handles the logic of the Streamlit app and calls these other functions as necessary. The code has been shortened by removing unnecessary whitespace and comments, but retains its functionality.
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# This program uses Streamlit to create a web app that allows the user to view and add to both semantic and episodic memory. The semantic memory is stored as a list of dictionaries, where each dictionary represents a fact and includes the fact itself, the category it belongs to, and the source of the fact. The episodic memory is also stored as a list of dictionaries, where each dictionary represents an event and includes the event itself, the sentiment associated with the event, and the date the event occurred.
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