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import gradio as gr |
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from fastapi import FastAPI |
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from fastapi.responses import JSONResponse |
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from fastapi.middleware.cors import CORSMiddleware |
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import tensorflow as tf |
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import pandas as pd |
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import numpy as np |
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import joblib |
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from load import * |
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from helper import * |
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from matplotlib import pyplot as plt |
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def predict_fire(temp, temp_unit, humidity, wind, wind_unit, veg, elev, elev_unit, use_trust): |
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input_data = { |
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"temperature": convert_temperature(temp, temp_unit), |
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"humidity": humidity, |
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"wind_speed": convert_wind_speed(wind, wind_unit), |
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"vegetation_index": veg, |
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"elevation": convert_elevation(elev, elev_unit) |
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} |
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input_df = pd.DataFrame([input_data]) |
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base_prob = FireNet.predict(input_df)[0][0] |
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if use_trust: |
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trust_score = FireTrustNet.predict(FireScaler.transform(input_df))[0][0] |
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final = np.clip(base_prob * trust_score, 0, 1) |
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else: |
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final = base_prob |
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if final > 0.49: |
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verdict = "🔥 FIRE LIKELY" |
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elif final > 0.43 and final < 0.50: |
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verdict = "⚠️ Fire Possible" |
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else: |
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verdict = "🛡️ Fire Unlikely" |
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return f"{verdict} ({final:.2f})" |
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def predict_flood(rainfall_val, rainfall_unit, water_level_val, elevation_val, elev_unit, |
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slope_val, distance_val, distance_unit, use_trustnet): |
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rainfall = convert_rainfall(rainfall_val, rainfall_unit) |
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elevation = convert_elevation(elevation_val, elev_unit) |
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distance = convert_distance(distance_val, distance_unit) |
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base_df = pd.DataFrame([{ |
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"Rainfall": rainfall, |
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"Water Level": water_level_val, |
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"Elevation": elevation, |
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"Slope": slope_val, |
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"Distance from River": distance |
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}]) |
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base_prob = FloodNet.predict(base_df)[0][0] |
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if use_trustnet: |
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trust_df = pd.DataFrame([{ |
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"rainfall": rainfall, |
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"water_level": water_level_val, |
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"elevation": elevation, |
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"slope": slope_val, |
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"distance_from_river": distance |
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}]) |
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trust_score = FloodTrustNet.predict(FloodScaler.transform(trust_df))[0][0] |
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final = np.clip(base_prob * trust_score, 0, 1) |
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else: |
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final = base_prob |
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if final > 0.49: |
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verdict = "🏞️ FV-FLOOD LIKELY" |
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elif final > 0.43 and final < 0.50: |
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verdict = "⚠️ FV-Flood Possible" |
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else: |
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verdict = "🛡️ FV-Flood Unlikely" |
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return f"{verdict} ({final:.2f})" |
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def predict_pluvial_flood(rain, imp, drain, urban, conv, use_trust, rainfall_unit): |
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print(rainfall_unit) |
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rain = convert_rainfall_intensity(rain, rainfall_unit) |
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print(rain) |
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input_data = { |
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"rainfall_intensity": rain, |
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"impervious_ratio": imp, |
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"drainage_density": drain, |
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"urbanization_index": urban, |
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"convergence_index": conv |
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} |
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input_df = pd.DataFrame([input_data]) |
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base_prob = PV_FloodNet.predict(input_df)[0][0] |
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if use_trust: |
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trust_score = PV_FloodTrustNet.predict(PV_FloodScaler.transform(input_df))[0][0] |
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final = np.clip(base_prob * trust_score, 0, 1) |
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else: |
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final = base_prob |
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if final > 0.52: |
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verdict = "🌧️ PV-FLOOD LIKELY" |
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elif 0.45 < final <= 0.52: |
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verdict = "⚠️ PV-Flood Possible" |
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else: |
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verdict = "🛡️ PV-Flood Unlikely" |
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return f"{verdict} ({final:.2f})" |
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def predict_flash_flood(rainfall, slope, drainage, saturation, convergence, use_trust): |
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input_data = { |
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"rainfall_intensity": rainfall, |
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"slope": slope, |
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"drainage_density": drainage, |
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"soil_saturation": saturation, |
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"convergence_index": convergence |
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} |
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input_df = pd.DataFrame([input_data]) |
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base_pred = FlashFloodNet.predict(input_df)[0][0] |
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if use_trust: |
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trust_score = FlashFloodTrustNet.predict(FlashFloodScaler.transform(input_df))[0][0] |
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adjusted = np.clip(base_pred * trust_score, 0, 1) |
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else: |
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adjusted = base_pred |
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if adjusted > 0.55: |
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return f"🌩️ FLASH FLOOD LIKELY ({adjusted:.2f})" |
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elif 0.40 < adjusted <= 0.55: |
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return f"⚠️ Flash Flood Possible ({adjusted:.2f})" |
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else: |
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return f"🛡️ Flash Flood Unlikely ({adjusted:.2f})" |
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def predict_quake(dotM0, sdr, coulomb, afd, fsr, use_trust): |
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dotM0 = dotM0 * 1e16 |
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input_data = { |
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"seismic_moment_rate": dotM0, |
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"surface_displacement_rate": sdr, |
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"coulomb_stress_change": coulomb, |
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"average_focal_depth": afd, |
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"fault_slip_rate": fsr |
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} |
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input_df = pd.DataFrame([input_data]) |
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base_pred = QuakeNet.predict(input_df)[0][0] |
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if use_trust: |
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trust_score = QuakeTrustNet.predict(QuakeTrustScaler.transform(input_df))[0][0] |
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adjusted = np.clip(base_pred * trust_score, 0, 1) |
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else: |
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adjusted = base_pred |
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if adjusted > 0.55: |
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return f"🌎 EARTHQUAKE LIKELY ({adjusted:.2f})" |
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elif 0.40 < adjusted <= 0.55: |
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return f"⚠️ Earthquake Possible ({adjusted:.2f})" |
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else: |
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return f"🛡️ Earthquake Unlikely ({adjusted:.2f})" |
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def predict_hurricane(sst, ohc, mlh, vws, pv, sstu, use_trust): |
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sst = convert_temp_c(value=sst, unit=sstu) |
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input_data = { |
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"sea_surface_temperature": sst, |
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"ocean_heat_content": ohc, |
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"mid_level_humidity": mlh, |
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"vertical_wind_shear": vws, |
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"potential_vorticity": pv |
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} |
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input_df = pd.DataFrame([input_data]) |
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base_pred = HurricaneNet.predict(input_df)[0][0] |
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if use_trust: |
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trust_score = HurricaneTrustNet.predict(HurricaneTrustScaler.transform(input_df))[0][0] |
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adjusted = np.clip(base_pred * trust_score, 0, 1) |
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else: |
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adjusted = base_pred |
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if adjusted > 0.55: |
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return f"🌀 HURRICANE LIKELY ({adjusted:.2f})" |
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elif 0.40 < adjusted <= 0.55: |
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return f"⚠️ Hurricane Possible ({adjusted:.2f})" |
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else: |
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return f"🛡️ Hurricane Unlikely ({adjusted:.2f})" |
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def generate_plot(axis, use_trustnet): |
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sweep_values = np.linspace({ |
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"temperature": (280, 320), |
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"humidity": (0, 100), |
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"wind_speed": (0, 50), |
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"vegetation_index": (0.0, 2.0), |
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"elevation": (0, 3000) |
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}[axis][0], { |
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"temperature": (280, 320), |
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"humidity": (0, 100), |
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"wind_speed": (0, 50), |
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"vegetation_index": (0.0, 2.0), |
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"elevation": (0, 3000) |
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}[axis][1], 100) |
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base_input = { |
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"temperature": 300.0, |
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"humidity": 30.0, |
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"wind_speed": 10.0, |
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"vegetation_index": 1.0, |
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"elevation": 500.0 |
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} |
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sweep_df = pd.DataFrame([{ |
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**base_input, |
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axis: val |
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} for val in sweep_values]) |
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raw_probs = FireNet.predict(sweep_df).flatten() |
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if use_trustnet: |
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trust_mods = FireTrustNet.predict(FireScaler.transform(sweep_df)).flatten() |
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adjusted_probs = np.clip(raw_probs * trust_mods, 0, 1) |
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else: |
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adjusted_probs = raw_probs |
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fig, ax = plt.subplots() |
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ax.plot(sweep_values, raw_probs, "--", color="gray", label="Base Model") |
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if use_trustnet: |
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ax.plot(sweep_values, adjusted_probs, color="orangered", label="With FireTrustNet") |
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ax.set_xlabel(axis.replace("_", " ").title()) |
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ax.set_ylabel("Fire Probability") |
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ax.set_title(f"Fire Probability vs. {axis.replace('_', ' ').title()}") |
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ax.legend() |
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ax.grid(True) |
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return fig |
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def generate_flood_plot(axis, use_trustnet): |
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sweep_range = { |
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"rainfall": (0, 150), |
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"water_level": (0, 8000), |
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"elevation": (0, 20), |
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"slope": (0, 20), |
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"distance_from_river": (0, 2000) |
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} |
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values = np.linspace(*sweep_range[axis], 100) |
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base_example = { |
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"rainfall": 50.0, |
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"water_level": 3000.0, |
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"elevation": 5.0, |
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"slope": 2.0, |
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"distance_from_river": 100.0 |
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} |
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inputs = pd.DataFrame([ |
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{**base_example, axis: v} for v in values |
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]) |
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floodnet_inputs = inputs.rename(columns={ |
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"rainfall": "Rainfall", |
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"water_level": "Water Level", |
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"elevation": "Elevation", |
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"slope": "Slope", |
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"distance_from_river": "Distance from River" |
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}) |
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base_probs = FloodNet.predict(floodnet_inputs).flatten() |
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if use_trustnet: |
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trust_inputs = inputs.copy() |
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trust_scores = FloodTrustNet.predict(FloodScaler.transform(trust_inputs)).flatten() |
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modulated_probs = np.clip(base_probs * trust_scores, 0, 1) |
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else: |
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modulated_probs = base_probs |
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fig, ax = plt.subplots() |
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ax.plot(values, base_probs, "--", color="gray", label="FloodNet") |
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if use_trustnet: |
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ax.plot(values, modulated_probs, color="blue", label="With FloodTrustNet") |
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ax.set_xlabel(axis.replace("_", " ").title()) |
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ax.set_ylabel("Flood Probability") |
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ax.set_title(f"Flood Probability vs. {axis.replace('_', ' ').title()}") |
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ax.grid(True) |
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ax.legend() |
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return fig |
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def generate_pluvial_plot(axis, use_trust): |
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sweep_range = { |
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"rainfall_intensity": (0, 160), |
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"impervious_ratio": (0.0, 1.0), |
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"drainage_density": (1.0, 5.0), |
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"urbanization_index": (0.0, 1.0), |
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"convergence_index": (0.0, 1.0) |
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} |
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sweep_values = np.linspace(*sweep_range[axis], 100) |
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base_input = { |
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"rainfall_intensity": 60.0, |
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"impervious_ratio": 0.5, |
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"drainage_density": 2.5, |
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"urbanization_index": 0.6, |
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"convergence_index": 0.5 |
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} |
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sweep_df = pd.DataFrame([ |
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{**base_input, axis: val} for val in sweep_values |
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]) |
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base_probs = PV_FloodNet.predict(sweep_df).flatten() |
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if use_trust: |
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trust_mods = PV_FloodTrustNet.predict(PV_FloodScaler.transform(sweep_df)).flatten() |
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adjusted = np.clip(base_probs * trust_mods, 0, 1) |
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else: |
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adjusted = base_probs |
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fig, ax = plt.subplots() |
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ax.plot(sweep_values, base_probs, "--", color="gray", label="Base Model") |
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if use_trust: |
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ax.plot(sweep_values, adjusted, color="royalblue", label="With PV-FloodTrustNet") |
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ax.set_xlabel(axis.replace("_", " ").title()) |
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ax.set_ylabel("PV Flood Probability") |
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ax.set_title(f"PV Flood Probability vs. {axis.replace('_', ' ').title()}") |
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ax.legend() |
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ax.grid(True) |
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return fig |
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def generate_flash_plot(axis, use_trust): |
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sweep_values = np.linspace( |
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{"rainfall_intensity": 0, "slope": 0, "drainage_density": 1.0, |
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"soil_saturation": 0.3, "convergence_index": 0.0}[axis], |
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{"rainfall_intensity": 150, "slope": 30, "drainage_density": 5.0, |
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"soil_saturation": 1.0, "convergence_index": 1.0}[axis], |
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100 |
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) |
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base_input = { |
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"rainfall_intensity": 90, |
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"slope": 15, |
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"drainage_density": 3.0, |
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"soil_saturation": 0.7, |
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"convergence_index": 0.5 |
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} |
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sweep_df = pd.DataFrame([{**base_input, axis: val} for val in sweep_values]) |
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raw_probs = FlashFloodNet.predict(sweep_df).flatten() |
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if use_trust: |
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trust_mods = FlashFloodTrustNet.predict(FlashFloodScaler.transform(sweep_df)).flatten() |
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adjusted_probs = np.clip(raw_probs * trust_mods, 0, 1) |
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else: |
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adjusted_probs = raw_probs |
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fig, ax = plt.subplots() |
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ax.plot(sweep_values, raw_probs, "--", color="gray", label="Base Model") |
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if use_trust: |
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ax.plot(sweep_values, adjusted_probs, color="darkcyan", label="With FlashFloodTrustNet") |
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ax.set_xlabel(axis.replace("_", " ").title()) |
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ax.set_ylabel("Flash Flood Probability") |
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ax.set_title(f"Flash Flood Probability vs. {axis.replace('_', ' ').title()}") |
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ax.legend() |
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ax.grid(True) |
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return fig |
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def generate_quake_plot(axis, use_trustnet): |
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axis_ranges = { |
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"seismic_moment_rate": (5e14, 2.5e16), |
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"surface_displacement_rate": (0, 100), |
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"coulomb_stress_change": (0, 700), |
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"average_focal_depth": (0, 60), |
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"fault_slip_rate": (0, 20) |
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} |
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sweep_values = np.linspace(*axis_ranges[axis], 100) |
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base_input = { |
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"seismic_moment_rate": 1.5e16, |
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"surface_displacement_rate": 35, |
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"coulomb_stress_change": 300, |
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"average_focal_depth": 18, |
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"fault_slip_rate": 7.0 |
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} |
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sweep_df = pd.DataFrame([{**base_input, axis: val} for val in sweep_values]) |
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raw_preds = QuakeNet.predict(sweep_df).flatten() |
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if use_trustnet: |
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scaled_df = QuakeTrustScaler.transform(sweep_df) |
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trust_scores = QuakeTrustNet.predict(scaled_df).flatten() |
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modulated = np.clip(raw_preds * trust_scores, 0, 1) |
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else: |
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modulated = raw_preds |
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fig, ax = plt.subplots() |
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ax.plot(sweep_values, raw_preds, "--", color="gray", label="QuakeNet") |
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if use_trustnet: |
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ax.plot(sweep_values, modulated, color="darkred", label="With QuakeTrustNet") |
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ax.set_xlabel(axis.replace("_", " ").title()) |
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ax.set_ylabel("Quake Probability") |
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ax.set_title(f"Earthquake Likelihood vs. {axis.replace('_', ' ').title()}") |
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ax.legend() |
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ax.grid(True) |
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return fig |
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def generate_hurricane_plot(axis, use_trustnet): |
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axis_ranges = { |
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"sea_surface_temperature": (24, 32), |
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"ocean_heat_content": (20, 150), |
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"mid_level_humidity": (20, 100), |
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"vertical_wind_shear": (0, 30), |
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"potential_vorticity": (0.0, 3.0) |
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} |
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sweep_values = np.linspace(*axis_ranges[axis], 100) |
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base_input = { |
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"sea_surface_temperature": 29.0, |
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"ocean_heat_content": 95, |
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"mid_level_humidity": 65, |
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"vertical_wind_shear": 9, |
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"potential_vorticity": 1.2 |
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} |
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df = pd.DataFrame([{**base_input, axis: val} for val in sweep_values]) |
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raw_preds = HurricaneNet.predict(df).flatten() |
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if use_trustnet: |
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scaled_df = HurricaneTrustScaler.transform(df) |
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trust_scores = HurricaneTrustNet.predict(scaled_df).flatten() |
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modulated = np.clip(raw_preds * trust_scores, 0, 1) |
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else: |
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modulated = raw_preds |
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fig, ax = plt.subplots() |
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ax.plot(sweep_values, raw_preds, "--", color="gray", label="HurricaneNet") |
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if use_trustnet: |
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ax.plot(sweep_values, modulated, color="navy", label="Trust-Modulated") |
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ax.set_xlabel(axis.replace("_", " ").title()) |
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ax.set_ylabel("Formation Likelihood") |
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ax.set_title(f"Hurricane Formation vs. {axis.replace('_', ' ').title()}") |
|
|
ax.legend() |
|
|
ax.grid(True) |
|
|
|
|
|
return fig |
|
|
|
|
|
|
|
|
with gr.Blocks(theme=gr.themes.Default(), css=".tab-nav-button { font-size: 1.1rem !important; padding: 0.8em; } ") as demo: |
|
|
gr.Markdown("### STRIKE: A High-Precision AI Framework for Early Prediction of Natural Diasters") |
|
|
gr.Markdown("##### Preview") |
|
|
with gr.Tab("🔥 Wildfires"): |
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
with gr.Row(): |
|
|
temp = gr.Slider(280, 330, value=300, label="Temperature (K)") |
|
|
temp_unit = gr.Dropdown(["K", "°C", "°F"], value="K", label="", scale=0.2) |
|
|
|
|
|
temp_unit.change(fn=update_temp_slider, inputs=temp_unit, outputs=temp) |
|
|
|
|
|
with gr.Row(): |
|
|
humidity = gr.Slider(0, 100, value=30, label="Humidity (%)") |
|
|
gr.Dropdown(["%"], value="%", label="", scale=0.1) |
|
|
|
|
|
with gr.Row(): |
|
|
wind_speed = gr.Slider(0, 50, value=10, label="Wind Speed (m/s)") |
|
|
wind_unit = gr.Dropdown(["m/s", "km/h", "mp/h"], value="m/s", label="", scale=0.2) |
|
|
|
|
|
wind_unit.change(update_wind_slider, inputs=wind_unit, outputs=wind_speed) |
|
|
|
|
|
with gr.Row(): |
|
|
elevation = gr.Slider(0, 3000, value=500, label="Elevation (m)") |
|
|
elev_unit = gr.Dropdown(["m", "ft"], value="m", label="", scale=0.2) |
|
|
|
|
|
elev_unit.change(update_elevation_slider, inputs=elev_unit, outputs=elevation) |
|
|
|
|
|
with gr.Row(): |
|
|
vegetation_index = gr.Slider(0.0, 2.0, value=1.0, label="Vegetation Index (NDVI)") |
|
|
gr.Dropdown(["NDVI"], value="NDVI", label="", scale=0.2) |
|
|
use_trust = gr.Checkbox(label="Use FireTrustNet", value=True) |
|
|
sweep_axis = gr.Radio(["temperature", "humidity", "wind_speed", "vegetation_index", "elevation"], |
|
|
label="Sweep Axis", value="temperature") |
|
|
predict_btn = gr.Button("Predict") |
|
|
with gr.Column(): |
|
|
with gr.Accordion("ℹ️ Feature Definitions", open=False): |
|
|
gr.Markdown(""" |
|
|
**Temperaure:** Current Temperature |
|
|
|
|
|
**Humidity:** Current Humidity |
|
|
|
|
|
**Wind Speed:** Current Wind Speed |
|
|
|
|
|
**Elevation:** Current Elevation Relative to Sea Level |
|
|
|
|
|
**Vegitation Index:** Your area's NDVI score. |
|
|
""") |
|
|
output = gr.Textbox(label="Result") |
|
|
plot_output = gr.Plot(label="Trust Modulation Plot") |
|
|
|
|
|
predict_btn.click( |
|
|
fn=lambda t, tu, h, w, wu, v, e, eu, trust, axis: ( |
|
|
predict_fire(t, tu, h, w, wu, v, e, eu, trust), |
|
|
generate_plot(axis, trust) |
|
|
), |
|
|
inputs=[ |
|
|
temp, temp_unit, |
|
|
humidity, |
|
|
wind_speed, wind_unit, |
|
|
vegetation_index, |
|
|
elevation, elev_unit, |
|
|
use_trust, |
|
|
sweep_axis |
|
|
], |
|
|
outputs=[output, plot_output] |
|
|
) |
|
|
|
|
|
with gr.Tab("🌊 Fluvial Floods"): |
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
with gr.Row(): |
|
|
rainfall = gr.Slider(0, 200, value=50, label="Rainfall (mm)") |
|
|
rainfall_unit = gr.Dropdown(["mm", "in"], value="mm", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
water_level = gr.Slider(0, 8000, value=3000, label="Relative Water Level (mm)") |
|
|
gr.Dropdown(["mm"], value="mm", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
elevation_flood = gr.Slider(0, 20, value=5, label="Relative Elevation (m)") |
|
|
elev_flood_unit = gr.Dropdown(["m", "ft"], value="m", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
slope = gr.Slider(0.0, 20.0, value=2.0, label="Slope (°)") |
|
|
gr.Dropdown(["°"], label="",scale=0.2) |
|
|
with gr.Row(): |
|
|
distance = gr.Slider(0, 2000, value=100, label="Distance from River (m)") |
|
|
distance_unit = gr.Dropdown(["m", "ft"], value="m", label="", scale=0.2) |
|
|
|
|
|
elev_flood_unit.change(update_flood_elevation_slider, inputs=elev_flood_unit, outputs=elevation_flood) |
|
|
distance_unit.change(update_flood_distance_slider, inputs=distance_unit, outputs=distance) |
|
|
rainfall_unit.change(update_flood_rainfall_slider, inputs=rainfall_unit, outputs=rainfall) |
|
|
use_trust_flood = gr.Checkbox(label="Use FV-FloodTrustNet", value=True) |
|
|
|
|
|
flood_sweep_axis = gr.Radio( |
|
|
["rainfall", "water_level", "elevation", "slope", "distance_from_river"], |
|
|
label="Sweep Axis", value="rainfall" |
|
|
) |
|
|
|
|
|
predict_btn_flood = gr.Button("Predict") |
|
|
|
|
|
with gr.Column(): |
|
|
with gr.Accordion("ℹ️ Feature Definitions", open=False): |
|
|
gr.Markdown(""" |
|
|
**Rainfall:** Total recent precipitation - Last 24 hours. |
|
|
|
|
|
**Relative Water Level:** Height of river assuming river is 2.5m (8.202 ft) deep. Adjust accordingly. |
|
|
|
|
|
**Relative Elevation:** Ground height relative to nearest body of water (river). |
|
|
|
|
|
**Slope:** Terrain gradient measured in degrees. |
|
|
|
|
|
**Distance from River:** Horizontal distance from riverbed in meters. This does not account for levees or terrain barriers. |
|
|
""") |
|
|
|
|
|
flood_output = gr.Textbox(label="Flood Risk") |
|
|
flood_plot = gr.Plot(label="Trust Modulation Plot") |
|
|
|
|
|
predict_btn_flood.click( |
|
|
fn=lambda r, ru, wl, e, eu, s, d, du, trust, axis: ( |
|
|
predict_flood(r, ru, wl, e, eu, s, d, du, trust), |
|
|
generate_flood_plot(axis, trust) |
|
|
), |
|
|
inputs=[ |
|
|
rainfall, rainfall_unit, |
|
|
water_level, |
|
|
elevation_flood, elev_flood_unit, |
|
|
slope, |
|
|
distance, distance_unit, |
|
|
use_trust_flood, |
|
|
flood_sweep_axis |
|
|
], |
|
|
outputs=[flood_output, flood_plot] |
|
|
) |
|
|
with gr.Tab("🌧️ Pluvial Floods"): |
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
with gr.Row(): |
|
|
rain_input = gr.Slider(0, 150, value=12, label="Rainfall Intensity (mm/hr)") |
|
|
rain_unit_dropdown = gr.Dropdown(["mm/hr", "in/hr"], value="mm/hr", label="", scale=0.2) |
|
|
with gr.Row(): |
|
|
imp_input = gr.Slider(0.0, 1.0, value=0.5, label="Impervious Ratio") |
|
|
gr.Dropdown(["ISR"], value="ISR", label="", scale=0.2) |
|
|
with gr.Row(): |
|
|
drain_input = gr.Slider(1.0, 5.0, value=2.5, label="Drainage Density") |
|
|
gr.Dropdown(["L/A"], value="L/A", label="", scale=0.2) |
|
|
with gr.Row(): |
|
|
urban_input = gr.Slider(0.0, 1.0, value=0.6, label="Urbanization Index") |
|
|
gr.Dropdown(["uP/tP"], value="uP/tP", label="", scale=0.2) |
|
|
with gr.Row(): |
|
|
conv_input = gr.Slider(0.0, 1.0, value=0.5, label="Convergence Index") |
|
|
gr.Dropdown(["CI"], value="CI", label="", scale=0.2) |
|
|
rain_unit_dropdown.change(update_rain_slider, inputs=rain_unit_dropdown, outputs=rain_input) |
|
|
use_trust_pv = gr.Checkbox(label="Use PV-FloodTrustNet", value=True) |
|
|
pv_sweep_axis = gr.Radio( |
|
|
["rainfall_intensity", "impervious_ratio", "drainage_density", "urbanization_index", "convergence_index"], |
|
|
label="Sweep Axis", value="rainfall_intensity" |
|
|
) |
|
|
pv_predict_btn = gr.Button("Predict") |
|
|
|
|
|
with gr.Column(): |
|
|
with gr.Accordion("ℹ️ Feature Definitions", open=False): |
|
|
gr.Markdown(""" |
|
|
**Rainfall Intensity:** Recent precipitation rate, typically measured in mm/hr. |
|
|
|
|
|
**Impervious Ratio:** Proportion of surface area that cannot absorb water. |
|
|
|
|
|
**Drainage Density:** Drainage channel length per unit area. |
|
|
|
|
|
**Urbanization Index:** Estimate of built-up density and infrastructure pressure. |
|
|
|
|
|
**Convergence Index:** Terrain feature promoting water pooling or runoff directionality. |
|
|
""") |
|
|
pv_output = gr.Textbox(label="PV-Flood Risk Verdict") |
|
|
pv_plot = gr.Plot(label="Trust Modulation Plot") |
|
|
|
|
|
pv_predict_btn.click( |
|
|
fn=lambda r, ra, i, d, u, c, trust, axis: ( |
|
|
predict_pluvial_flood(r, i, d, u, c, trust, ra), |
|
|
generate_pluvial_plot(axis, trust) |
|
|
), |
|
|
inputs=[rain_input, rain_unit_dropdown, imp_input, drain_input, urban_input, conv_input, use_trust_pv, pv_sweep_axis], |
|
|
outputs=[pv_output, pv_plot] |
|
|
) |
|
|
|
|
|
with gr.Tab("🌩️ Flash Floods"): |
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
with gr.Row(): |
|
|
rainfall_intensity = gr.Slider(0, 150, value=12, label="Rainfall Intensity (mm/hr)") |
|
|
rainfall_unit_dropdown = gr.Dropdown(["mm/hr", "in/hr"], value="mm/hr", label="", scale=0.2) |
|
|
with gr.Row(): |
|
|
slope_input = gr.Slider(0, 30, value=15, label="Slope (°)") |
|
|
gr.Dropdown(["°"], label="", scale=0.1) |
|
|
|
|
|
with gr.Row(): |
|
|
drainage_input = gr.Slider(1.0, 5.0, value=3.0, label="Drainage Density") |
|
|
gr.Dropdown(["L/A"], value="L/A", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
saturation_input = gr.Slider(0.3, 1.0, value=0.7, label="Soil Saturation") |
|
|
gr.Dropdown(["VWC"], value="VWC", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
convergence_input = gr.Slider(0.0, 1.0, value=0.5, label="Convergence Index") |
|
|
gr.Dropdown(["CI"], value="CI", label="", scale=0.2) |
|
|
|
|
|
rainfall_unit_dropdown.change(update_rain_slider, inputs=rainfall_unit_dropdown, outputs=rainfall_intensity) |
|
|
|
|
|
use_trust_flash = gr.Checkbox(label="Use FlashFloodTrustNet", value=True) |
|
|
|
|
|
flash_sweep_axis = gr.Radio( |
|
|
["rainfall_intensity", "slope", "drainage_density", "soil_saturation", "convergence_index"], |
|
|
label="Sweep Axis", value="rainfall_intensity" |
|
|
) |
|
|
|
|
|
flash_predict_btn = gr.Button("Predict") |
|
|
|
|
|
with gr.Column(): |
|
|
with gr.Accordion("ℹ️ Feature Definitions", open=False): |
|
|
gr.Markdown(""" |
|
|
**Rainfall Intensity:** Measured in mm/hr or in/hr. |
|
|
|
|
|
**Slope:** Terrain gradient in degrees. |
|
|
|
|
|
**Drainage Density:** Total stream length per unit area. |
|
|
|
|
|
**Soil Saturation:** Volumetric water content — higher values = wetter ground. |
|
|
|
|
|
**Convergence Index:** Measures topographical tendency to channel runoff. |
|
|
""") |
|
|
flash_output = gr.Textbox(label="Flash Flood Risk Verdict") |
|
|
flash_plot = gr.Plot(label="Trust Modulation Plot") |
|
|
|
|
|
flash_predict_btn.click( |
|
|
fn=lambda r, ru, s, d, ss, c, trust, axis: ( |
|
|
predict_flash_flood(convert_rainfall_intensity(r, ru), s, d, ss, c, trust), |
|
|
generate_flash_plot(axis, trust) |
|
|
), |
|
|
inputs=[ |
|
|
rainfall_intensity, rainfall_unit_dropdown, |
|
|
slope_input, drainage_input, saturation_input, |
|
|
convergence_input, use_trust_flash, flash_sweep_axis |
|
|
], |
|
|
outputs=[flash_output, flash_plot] |
|
|
) |
|
|
|
|
|
with gr.Tab("🌎 Earthquakes"): |
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
with gr.Row(): |
|
|
moment_input = gr.Slider( |
|
|
minimum=0.5, maximum=25.0, value=15.0, |
|
|
label="Seismic Moment Rate (×10¹⁶ Nm/s)" |
|
|
) |
|
|
gr.Dropdown(["Nm/s"], value="Nm/s", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
displacement_input = gr.Slider(0, 100, value=35, label="Surface Displacement Rate (mm/yr)") |
|
|
gr.Dropdown(["mm/yr"], value="mm/yr", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
stress_input = gr.Slider(0, 700, value=300, label="Coulomb Stress Change (Pa)") |
|
|
gr.Dropdown(["Pa"], value="Pa", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
depth_input = gr.Slider(0, 60, value=18, label="Average Focal Depth (km)") |
|
|
gr.Dropdown(["km"], value="km", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
slip_input = gr.Slider(0, 20, value=7.0, label="Fault Slip Rate (mm/yr)") |
|
|
gr.Dropdown(["mm/yr"], value="mm/yr", label="", scale=0.2) |
|
|
|
|
|
use_trust_quake = gr.Checkbox(label="Use QuakeTrustNet", value=True) |
|
|
|
|
|
quake_sweep_axis = gr.Radio( |
|
|
["seismic_moment_rate", "surface_displacement_rate", |
|
|
"coulomb_stress_change", "average_focal_depth", "fault_slip_rate"], |
|
|
label="Sweep Axis", value="seismic_moment_rate" |
|
|
) |
|
|
|
|
|
quake_predict_btn = gr.Button("Predict") |
|
|
|
|
|
with gr.Column(): |
|
|
with gr.Accordion("ℹ️ Feature Definitions", open=False): |
|
|
gr.Markdown(""" |
|
|
**Seismic Moment Rate (Nm/s):** Total energy release rate from seismic events. |
|
|
|
|
|
**Surface Displacement Rate (mm/yr):** Horizontal/vertical ground motion observed via GPS/InSAR. |
|
|
|
|
|
**Coulomb Stress Change (Pa):** Fault stress changes post-seismic activity. |
|
|
|
|
|
**Average Focal Depth (km):** Typical depth of earthquakes in the region. |
|
|
|
|
|
**Fault Slip Rate (mm/yr):** Long-term fault motion due to tectonic loading. |
|
|
""") |
|
|
|
|
|
quake_output = gr.Textbox(label="Earthquake Risk Verdict") |
|
|
quake_plot = gr.Plot(label="Trust Modulation Plot") |
|
|
|
|
|
quake_predict_btn.click( |
|
|
fn=lambda m, d, s, dp, sl, trust, axis: ( |
|
|
predict_quake(m, d, s, dp, sl, trust), |
|
|
generate_quake_plot(axis, trust) |
|
|
), |
|
|
inputs=[ |
|
|
moment_input, displacement_input, |
|
|
stress_input, depth_input, slip_input, |
|
|
use_trust_quake, quake_sweep_axis |
|
|
], |
|
|
outputs=[quake_output, quake_plot] |
|
|
) |
|
|
|
|
|
with gr.Tab("🌀 Hurricanes"): |
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
with gr.Row(): |
|
|
sst_input = gr.Slider(40, 140, value=80.0, label="Sea Surface Temperature (°C)") |
|
|
sst_unit = gr.Dropdown(["°C", "°F"], value="°F", label="", scale=0.2) |
|
|
sst_unit.change(update_temp_slider, inputs=sst_unit, outputs=sst_input) |
|
|
with gr.Row(): |
|
|
ohc_input = gr.Slider(20, 150, value=95, label="Ocean Heat Content (kJ/cm²)") |
|
|
gr.Dropdown(["kJ/cm²"], value="kJ/cm²", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
humidity_input = gr.Slider(20, 100, value=65, label="Mid-Level Relative Humidity (%)") |
|
|
gr.Dropdown(["%"], value="%", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
shear_input = gr.Slider(0, 30, value=9, label="Vertical Wind Shear (m/s)") |
|
|
gr.Dropdown(["m/s"], value="m/s", label="", scale=0.2) |
|
|
|
|
|
with gr.Row(): |
|
|
vort_input = gr.Slider(0.0, 3.0, value=1.2, label="Potential Vorticity (PVU)") |
|
|
gr.Dropdown(["PVU"], value="PVU", label="", scale=0.2) |
|
|
|
|
|
use_trust_cyclone = gr.Checkbox(label="Use HurricaneTrustNet", value=True) |
|
|
|
|
|
hurricane_sweep_axis = gr.Radio( |
|
|
["sea_surface_temperature", "ocean_heat_content", |
|
|
"mid_level_humidity", "vertical_wind_shear", "potential_vorticity"], |
|
|
label="Sweep Axis", value="sea_surface_temperature" |
|
|
) |
|
|
|
|
|
hurricane_predict_btn = gr.Button("Predict") |
|
|
|
|
|
with gr.Column(): |
|
|
with gr.Accordion("ℹ️ Feature Definitions", open=False): |
|
|
gr.Markdown(""" |
|
|
**Sea Surface Temperature (°C):** Thermal fuel for cyclone formation. |
|
|
|
|
|
**Ocean Heat Content (kJ/cm²):** Depth-integrated ocean energy — sustains storm growth. |
|
|
|
|
|
**Mid-Level Relative Humidity (%):** Moisture around 500 hPa — supports convective core. |
|
|
|
|
|
**Vertical Wind Shear (m/s):** Disrupts vertical structure — high values inhibit intensification. |
|
|
|
|
|
**Potential Vorticity (PVU):** Atmospheric spin and structure — higher values favor formation. |
|
|
""") |
|
|
|
|
|
hurricane_output = gr.Textbox(label="Hurricane Formation Verdict") |
|
|
hurricane_plot = gr.Plot(label="Trust Modulation Plot") |
|
|
|
|
|
hurricane_predict_btn.click( |
|
|
fn=lambda sst, sst_unit, ohc, humid, shear, vort, trust, axis: ( |
|
|
predict_hurricane(sst, ohc, humid, shear, vort, sst_unit, trust), |
|
|
generate_hurricane_plot(axis, trust) |
|
|
), |
|
|
inputs=[ |
|
|
sst_input, sst_unit, ohc_input, humidity_input, |
|
|
shear_input, vort_input, use_trust_cyclone, |
|
|
hurricane_sweep_axis |
|
|
], |
|
|
outputs=[hurricane_output, hurricane_plot] |
|
|
) |
|
|
|
|
|
|
|
|
app = FastAPI() |
|
|
|
|
|
app.add_middleware( |
|
|
CORSMiddleware, |
|
|
allow_origins=["*"], |
|
|
allow_methods=["*"], |
|
|
allow_headers=["*"], |
|
|
) |
|
|
|
|
|
app = gr.mount_gradio_app(app, demo, path="") |
|
|
|
|
|
@app.get("/api/status") |
|
|
def hello(): |
|
|
return JSONResponse({"status": "ok"}) |
