import os import subprocess # 🔹 Clear Hugging Face Pip Cache Before Installing subprocess.run("rm -rf /home/user/.cache/pip/*", shell=True) import streamlit as st import numpy as np import os os.system("pip install --no-cache-dir matplotlib") import matplotlib.pyplot as plt import pandas as pd import subprocess # Ensure SpaCy is installed try: import spacy except ModuleNotFoundError: subprocess.run(["pip", "install", "spacy"]) import spacy # Ensure the model is installed try: nlp = spacy.load("en_core_web_sm") except OSError: subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"]) nlp = spacy.load("en_core_web_sm") def extract_equation_params(text): """ Extracts demand and supply equation parameters from a structured natural language query. """ doc = nlp(text.lower()) numbers = [float(token.text) for token in doc if token.like_num] if len(numbers) < 14: return None demand_intercept_corn, demand_slope_corn, supply_intercept_corn, supply_slope_corn, \ supply_intercept_prod_pay, supply_slope_prod_pay, \ demand_intercept_cleaned, demand_slope_cleaned, supply_intercept_cleaned, supply_slope_cleaned, \ demand_intercept_no_loading, demand_slope_no_loading, supply_intercept_no_loading, supply_slope_no_loading = numbers[:14] demand_slope_corn = -abs(demand_slope_corn) demand_slope_cleaned = -abs(demand_slope_cleaned) demand_slope_no_loading = -abs(demand_slope_no_loading) return demand_intercept_corn, demand_slope_corn, supply_intercept_corn, supply_slope_corn, \ supply_intercept_prod_pay, supply_slope_prod_pay, \ demand_intercept_cleaned, demand_slope_cleaned, supply_intercept_cleaned, supply_slope_cleaned, \ demand_intercept_no_loading, demand_slope_no_loading, supply_intercept_no_loading, supply_slope_no_loading def calculate_equilibrium(demand_intercept, demand_slope, supply_intercept, supply_slope): """ Computes Competitive Equilibrium: Quantity and Price. Also calculates CS, PS, and SW for the competitive market. """ quantity_eq = (demand_intercept - supply_intercept) / (supply_slope - demand_slope) price_eq = demand_intercept + demand_slope * quantity_eq cs_eq = (demand_intercept - price_eq) * quantity_eq / 2 ps_eq = (price_eq - supply_intercept) * quantity_eq / 2 sw_eq = cs_eq + ps_eq return round(quantity_eq, 2), round(price_eq, 2), round(cs_eq, 2), round(ps_eq, 2), round(sw_eq, 2) def plot_market(demand_intercept, demand_slope, supply_intercept, supply_slope, quantity_eq, price_eq, market_name, supply_intercept_alt=None, supply_slope_alt=None): """ Plots the supply and demand curves for a given market. """ q_range = np.linspace(0, quantity_eq * 1.5, 100) demand_curve = demand_intercept + demand_slope * q_range supply_curve_primary = supply_intercept + supply_slope * q_range supply_curve_alt = supply_intercept_alt + supply_slope_alt * q_range if supply_intercept_alt is not None else None plt.figure(figsize=(8, 6)) plt.plot(q_range, demand_curve, label="Demand Curve", color="blue") plt.plot(q_range, supply_curve_primary, label="Supply Curve (Original)", color="green") if supply_curve_alt is not None: plt.plot(q_range, supply_curve_alt, label="Supply Curve (Alternative)", color="red", linestyle="dashed") plt.xlabel("Quantity") plt.ylabel("Price") plt.title(f"Market Equilibrium: {market_name}") plt.legend() st.pyplot(plt) # Streamlit Interface st.title("Market Equilibrium Solver") st.write("Enter a question describing supply and demand equations for corn and water.") user_query = st.text_area( "Type your question here:", value="Corn market demand: intercept 100, slope -2. Original Corn supply (water consumers pay): intercept 20, slope 3. Intervene Corn supply (corn producer pays): intercept 30, slope 3.\n" "Original Sceanrio (water consumer pays): Water Demand has intercept 200, slope -4. Water Supply has intercept 64.38, slope 2.1.\n" "Intervene Scenario (corn producer pays): Water demand has intercept 200, slope -4. Water supply has intercept 44.91, slope 2.1." ) if st.button("Solve"): params = extract_equation_params(user_query) if params: demand_intercept_corn, demand_slope_corn, supply_intercept_corn, supply_slope_corn, \ supply_intercept_prod_pay, supply_slope_prod_pay, \ demand_intercept_cleaned, demand_slope_cleaned, supply_intercept_cleaned, supply_slope_cleaned, \ demand_intercept_no_loading, demand_slope_no_loading, supply_intercept_no_loading, supply_slope_no_loading = params # Compute Equilibria for Corn quantity_eq_corn, price_eq_corn, cs_eq_corn, ps_eq_corn, sw_eq_corn = \ calculate_equilibrium(demand_intercept_corn, demand_slope_corn, supply_intercept_corn, supply_slope_corn) quantity_eq_corn_prod_pay, price_eq_corn_prod_pay, cs_eq_corn_prod_pay, ps_eq_corn_prod_pay, sw_eq_corn_prod_pay = \ calculate_equilibrium(demand_intercept_corn, demand_slope_corn, supply_intercept_prod_pay, supply_slope_prod_pay) # Compute Equilibria for Water quantity_eq_cleaned, price_eq_cleaned, cs_eq_cleaned, ps_eq_cleaned, sw_eq_cleaned = \ calculate_equilibrium(demand_intercept_cleaned, demand_slope_cleaned, supply_intercept_cleaned, supply_slope_cleaned) quantity_eq_no_loading, price_eq_no_loading, cs_eq_no_loading, ps_eq_no_loading, sw_eq_no_loading = \ calculate_equilibrium(demand_intercept_no_loading, demand_slope_no_loading, supply_intercept_no_loading, supply_slope_no_loading) # Display Equilibrium Prices and Quantities st.write("\n**Equilibrium Prices and Quantities:**") st.write(f"- **Corn Mkt(Original:Water Consumer Pay):** Price: ${price_eq_corn}, Quantity: {quantity_eq_corn} units") st.write(f"- **Corn Mkt(Intervene:Corn Producer Pays):** Price: ${price_eq_corn_prod_pay}, Quantity: {quantity_eq_corn_prod_pay} units") st.write(f"- **Water Market(Original:Water Consumer Pays):** Price: ${price_eq_cleaned}, Quantity: {quantity_eq_cleaned} units") st.write(f"- **Water Mkt:(intervene:Corn Producer Pays):** Price: ${price_eq_no_loading}, Quantity: {quantity_eq_no_loading} units") # Plot Graphs for Corn with both supply curves plot_market(demand_intercept_corn, demand_slope_corn, supply_intercept_corn, supply_slope_corn, quantity_eq_corn, price_eq_corn, "Corn Market", supply_intercept_prod_pay, supply_slope_prod_pay) # Plot Graphs for Water with both supply curves plot_market(demand_intercept_cleaned, demand_slope_cleaned, supply_intercept_cleaned, supply_slope_cleaned, quantity_eq_cleaned, price_eq_cleaned, "Water Market", supply_intercept_no_loading, supply_slope_no_loading) # Display Surplus Tables st.write("\n**Surplus Tables:**") st.table(pd.DataFrame({ "Scenario": ["Orig Corn Mkt:Water Cons Pay", "Intervene Corn Mkt:Corn Prod Pay)", "Difference"], "Consumer Surplus": [cs_eq_corn, cs_eq_corn_prod_pay, cs_eq_corn_prod_pay - cs_eq_corn], "Producer Surplus": [ps_eq_corn, ps_eq_corn_prod_pay, ps_eq_corn_prod_pay - ps_eq_corn], "Total Social Welfare": [sw_eq_corn, sw_eq_corn_prod_pay, sw_eq_corn_prod_pay - sw_eq_corn] })) st.table(pd.DataFrame({ "Scenario": ["Orig Water Mkt:Water Cons Pay", "Intervene Water Mkt:Corn Prod Pay", "Difference"], "Consumer Surplus": [cs_eq_cleaned, cs_eq_no_loading, cs_eq_no_loading - cs_eq_cleaned], "Producer Surplus": [ps_eq_cleaned, ps_eq_no_loading, ps_eq_no_loading - ps_eq_cleaned], "Total Social Welfare": [sw_eq_cleaned, sw_eq_no_loading, sw_eq_no_loading - sw_eq_cleaned] }))