Create app.py

app.py

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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])}")
+