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import streamlit as st
import requests
import pandas as pd
from sklearn.linear_model import LinearRegression
import random
import matplotlib.pyplot as plt
import numpy as np
st.title('Oracle Function Simulation')
# Oracle function
def oracle(task_complexity, ether_price, active_users, solved_tasks, unsolved_tasks, user_kpis, service_level_agreements):
weights = [random.random() for _ in range(7)]
return (
weights[0] * task_complexity
+ weights[1] * ether_price
+ weights[2] * active_users
+ weights[3] * solved_tasks
+ weights[4] * unsolved_tasks
+ weights[5] * user_kpis
+ weights[6] * service_level_agreements
)
# Get historical data for Ether
url = "https://api.coingecko.com/api/v3/coins/ethereum/market_chart"
params = {"vs_currency": "usd", "days": "1095"} # 1095 days is approximately 3 years
response = requests.get(url, params=params)
data = response.json()
# Convert the price data to a Pandas DataFrame
df = pd.DataFrame(data['prices'], columns=['time', 'price'])
df['time'] = pd.to_datetime(df['time'], unit='ms')
# Generate mock data for the oracle function and simulate the last 3 years
oracle_outputs = []
variables = {'task_complexity': [], 'ether_price': [], 'active_users': [], 'solved_tasks': [], 'unsolved_tasks': [], 'user_kpis': [], 'service_level_agreements': []}
for _ in range(len(df)):
task_complexity = random.randint(1, 10)
active_users = random.randint(1, 10000)
solved_tasks = random.randint(1, 1000)
unsolved_tasks = random.randint(1, 1000)
user_kpis = random.uniform(0.1, 1)
service_level_agreements = random.uniform(0.1, 1)
ether_price = df.iloc[_]['price']
oracle_outputs.append(oracle(task_complexity, ether_price, active_users, solved_tasks, unsolved_tasks, user_kpis, service_level_agreements))
variables['task_complexity'].append(task_complexity)
variables['ether_price'].append(ether_price)
variables['active_users'].append(active_users)
variables['solved_tasks'].append(solved_tasks)
variables['unsolved_tasks'].append(unsolved_tasks)
variables['user_kpis'].append(user_kpis)
variables['service_level_agreements'].append(service_level_agreements)
# Train a linear regression model to adjust the oracle output based on Ether price
model = LinearRegression()
model.fit(df['price'].values.reshape(-1, 1), oracle_outputs)
# Resample the price data to monthly data and calculate average price for each month
df['oracle_output'] = oracle_outputs
df.set_index('time', inplace=True)
monthly_df = df.resample('M').mean()
# Predict the oracle output for each average monthly price
monthly_df['predicted_oracle_output'] = model.predict(monthly_df['price'].values.reshape(-1, 1))
# Display a line chart of the predicted oracle output and Ether price over time
st.subheader('Predicted Oracle Output and Ether Price Over Time')
st.line_chart(monthly_df[['predicted_oracle_output', 'price']])
# Display a scatter plot with linear relation between Predicted Oracle output and Ether price
st.subheader('Predicted Oracle output vs Ether price')
plt.figure(figsize=(8,6))
plt.scatter(monthly_df['predicted_oracle_output'], monthly_df['price'])
m, b = np.polyfit(monthly_df['predicted_oracle_output'], monthly_df['price'], 1)
plt.plot(monthly_df['predicted_oracle_output'], m*monthly_df['predicted_oracle_output'] + b, color='red')
plt.xlabel('Predicted Oracle Output')
plt.ylabel('Ether Price')
st.pyplot(plt)
# Display tables showing average values of the variables over time
st.subheader('Average Values of the Variables Over Time')
for var in variables:
st.write(f"{var}: {sum(variables[var])/len(variables[var])}")
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