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import streamlit as st
import graphviz as gv
import json
import os
FILE_NAME = 'saved_data.json'
# Function to create the Graphviz graph
def create_graph():
g = gv.Digraph('G', engine='dot', format='png')
# Create the first box
box1_label = 'Source Sequence: Question{s1,s2,s3}\nContext: {sx,sy,sz}\nAnswer: {s1,s2,s3}'
g.node('box1', label=box1_label, shape='box', style='rounded')
# Create the second box
box2_label = 'Target Sequence: The answer to the question given the context is yes.'
g.node('box2', label=box2_label, shape='box', style='rounded')
# Add the line connecting the two boxes
g.edge('box1', 'box2')
return g
def save_data(data):
with open(FILE_NAME, 'w') as f:
json.dump(data, f)
def load_data():
if not os.path.exists(FILE_NAME):
return {}
with open(FILE_NAME, 'r') as f:
return json.load(f)
# Create the graph
graph = create_graph()
# Streamlit app
st.title("In Context Learning - Prompt Targeting QA Pattern")
st.subheader("The Question / Answer pattern below can be used in concert with a LLM to do real time in context learning using general intelligence.")
st.graphviz_chart(graph)
data = load_data()
# Input fields
st.header("Enter your data")
question = st.text_input("Question:")
context = st.text_input("Context:")
answer = st.text_input("Answer:")
target_sequence = st.text_input("Target Sequence:")
if st.button("Save"):
if question and context and answer and target_sequence:
data["question"] = question
data["context"] = context
data["answer"] = answer
data["target_sequence"] = target_sequence
save_data(data)
st.success("Data saved successfully.")
else:
st.error("Please fill in all fields.")
st.header("Saved data")
st.write(data)
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