init: ClimaShock FastAPI backend with LFS

.gitattributes

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+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+FROM python:3.10-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+EXPOSE 7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import joblib
+import json
+import numpy as np
+import pandas as pd
+from pathlib import Path
+
+app = FastAPI(
+    title="ClimaShock API",
+    description="Pakistan's first distributed causal climate-economic intelligence system",
+    version="1.0.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# load models on startup
+BASE = Path(__file__).parent / "models"
+
+xgb_inf       = joblib.load(BASE / "xgb_inf.pkl")
+xgb_crop      = joblib.load(BASE / "xgb_crop.pkl")
+feat_scaler   = joblib.load(BASE / "feat_scaler.pkl")
+target_scaler = joblib.load(BASE / "target_scaler.pkl")
+district_metrics = joblib.load(BASE / "district_metrics.pkl")
+
+with open(BASE / "ensemble_config.json") as f:
+    ensemble_config = json.load(f)
+
+causal_links = pd.read_csv(BASE / "causal_links.csv")
+climate_df   = pd.read_csv(BASE / "pakistan_climate.csv")
+
+# district-specific climate norms
+district_stats = climate_df.groupby("district").agg(
+    rain_mean = ("PRECTOTCORR", "mean"),
+    rain_std  = ("PRECTOTCORR", "std"),
+    temp_mean = ("T2M",         "mean"),
+    temp_std  = ("T2M",         "std"),
+).to_dict("index")
+
+# historical reference values for XGBoost features
+climate_annual = climate_df.groupby("year").agg(
+    rain_mean = ("PRECTOTCORR", "mean"),
+    rain_std  = ("PRECTOTCORR", "std"),
+    temp_mean = ("T2M",         "mean"),
+    temp_max  = ("T2M_MAX",     "mean"),
+    humidity  = ("RH2M",        "mean"),
+).reset_index()
+
+HIST_RAIN_MEAN = float(climate_annual["rain_mean"].mean())
+HIST_RAIN_STD  = float(climate_annual["rain_mean"].std())
+HIST_TEMP_MEAN = float(climate_annual["temp_mean"].mean())
+HIST_TEMP_STD  = float(climate_annual["temp_mean"].std())
+
+# granger coefficients from analysis
+GRANGER_RAIN_INF  = 0.347
+GRANGER_TEMP_INF  = 0.128
+INF_MEAN          = 12.8
+INF_STD           = 7.4
+
+
+class PredictRequest(BaseModel):
+    district:          str
+    rainfall_mm_day:   float
+    temperature_c:     float
+    gdp_growth_pct:    float = 3.5
+    agri_gdp_pct:      float = 22.0
+
+
+class PredictResponse(BaseModel):
+    district:              str
+    rain_zscore:           float
+    temp_zscore:           float
+    risk_level:            str
+    risk_score:            float
+    immediate_crop_pct:    float
+    inflation_predicted:   float
+    inflation_range_low:   float
+    inflation_range_high:  float
+    gdp_outlook:           str
+    cascade_chain:         list
+    model_confidence:      str
+
+
+@app.get("/")
+def root():
+    return {
+        "system":    "ClimaShock",
+        "version":   "1.0.0",
+        "status":    "online",
+        "endpoints": ["/predict", "/causal", "/districts", "/health"]
+    }
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok", "models_loaded": True}
+
+
+@app.post("/predict", response_model=PredictResponse)
+def predict(req: PredictRequest):
+    district = req.district.strip().title()
+
+    if district not in district_stats:
+        raise HTTPException(
+            status_code=400,
+            detail=f"District '{district}' not found. Available: {list(district_stats.keys())}"
+        )
+
+    stats = district_stats[district]
+
+    # district-specific z-scores
+    rain_z = (req.rainfall_mm_day - stats["rain_mean"]) / max(stats["rain_std"], 0.01)
+    temp_z = (req.temperature_c   - stats["temp_mean"]) / max(stats["temp_std"],  0.01)
+
+    # risk classification
+    risk_score = abs(rain_z) * 0.7 + abs(temp_z) * 0.3
+    if   abs(rain_z) > 2.5: risk_level = "EXTREME"
+    elif abs(rain_z) > 1.5: risk_level = "HIGH"
+    elif abs(rain_z) > 0.5: risk_level = "MODERATE"
+    else:                    risk_level = "NORMAL"
+
+    # granger-calibrated inflation prediction
+    inflation_pred  = INF_MEAN
+    inflation_pred += GRANGER_RAIN_INF * rain_z * INF_STD
+    inflation_pred += GRANGER_TEMP_INF * temp_z * INF_STD
+    inflation_pred  = max(0, round(inflation_pred, 1))
+
+    # confidence interval — ±1 std scaled by risk
+    margin          = INF_STD * 0.5 * (1 + abs(rain_z) * 0.1)
+    inf_low         = round(max(0, inflation_pred - margin), 1)
+    inf_high        = round(inflation_pred + margin, 1)
+
+    # immediate crop impact
+    immediate_crop  = round(-abs(rain_z) * 5.2 if rain_z > 1.5 else rain_z * 1.8, 1)
+
+    # gdp outlook
+    if   inflation_pred > 20: gdp_outlook = "contraction likely"
+    elif inflation_pred > 12: gdp_outlook = "slowdown possible"
+    else:                     gdp_outlook = "stable"
+
+    # cascade chain — from granger discoveries
+    cascade = []
+    if abs(rain_z) > 0.5:
+        cascade.append({
+            "step":        1,
+            "event":       "Climate Anomaly Detected",
+            "detail":      f"Rainfall {rain_z:+.2f}σ from district norm",
+            "timeframe":   "Now",
+            "severity":    risk_level,
+        })
+    if abs(rain_z) > 1.0:
+        cascade.append({
+            "step":        2,
+            "event":       "Crop Stress",
+            "detail":      f"Expected yield change: {immediate_crop:+.1f}%",
+            "timeframe":   "4–8 weeks",
+            "severity":    "HIGH" if immediate_crop < -10 else "MODERATE",
+        })
+    cascade.append({
+        "step":        3,
+        "event":       "Inflation Pressure",
+        "detail":      f"Predicted: {inflation_pred}% (range {inf_low}–{inf_high}%)",
+        "timeframe":   "6–18 months (lag=2y Granger)",
+        "severity":    "HIGH" if inflation_pred > 15 else "MODERATE",
+    })
+    cascade.append({
+        "step":        4,
+        "event":       "GDP Outlook",
+        "detail":      gdp_outlook.title(),
+        "timeframe":   "12–24 months",
+        "severity":    "HIGH" if gdp_outlook == "contraction likely" else "LOW",
+    })
+
+    confidence = (
+        "HIGH — well within training distribution"   if abs(rain_z) < 2 else
+        "MEDIUM — near extreme historical values"    if abs(rain_z) < 3 else
+        "LOW — beyond historical training range"
+    )
+
+    return PredictResponse(
+        district            = district,
+        rain_zscore         = round(rain_z, 3),
+        temp_zscore         = round(temp_z, 3),
+        risk_level          = risk_level,
+        risk_score          = round(risk_score, 3),
+        immediate_crop_pct  = immediate_crop,
+        inflation_predicted = inflation_pred,
+        inflation_range_low = inf_low,
+        inflation_range_high= inf_high,
+        gdp_outlook         = gdp_outlook,
+        cascade_chain       = cascade,
+        model_confidence    = confidence,
+    )
+
+
+@app.get("/causal")
+def get_causal_links(min_strength: float = 0.08):
+    links = causal_links[causal_links["strength"] >= min_strength].copy()
+    return {
+        "total_links": len(links),
+        "links": links.sort_values("strength", ascending=False).to_dict("records"),
+        "top_discovery": {
+            "cause":    "rain_anomaly",
+            "effect":   "inflation_pct",
+            "lag_years": 2,
+            "strength": 0.347,
+            "meaning":  "Extreme rainfall causes inflation spike after 2-year lag in Pakistan"
+        }
+    }
+
+
+@app.get("/districts")
+def get_districts():
+    out = []
+    for district, stats in district_stats.items():
+        met = district_metrics.get(district, {})
+        out.append({
+            "district":       district,
+            "rain_mean":      round(stats["rain_mean"], 3),
+            "rain_std":       round(stats["rain_std"],  3),
+            "temp_mean":      round(stats["temp_mean"], 3),
+            "model_inf_mae":  met.get("inf_mae",  None),
+            "model_crop_mae": met.get("crop_mae", None),
+        })
+    return {"districts": out, "count": len(out)}
+
+
+@app.get("/discoveries")
+def get_discoveries():
+    return {
+        "discoveries": [
+            {
+                "rank":     1,
+                "title":    "Flood → Inflation Lag",
+                "finding":  "Extreme rainfall causes national inflation spike after 2-year lag",
+                "evidence": "Granger causality strength 0.347 — strongest link in system",
+                "case":     "2022 Sindh floods → 2023 Pakistan inflation 30.8%",
+                "novel":    True,
+            },
+            {
+                "rank":     2,
+                "title":    "Sukkur Epicenter",
+                "finding":  "Sukkur is Pakistan highest climate-economic risk zone",
+                "evidence": "2022 rainfall +1293% — highest z-score of all 10 districts",
+                "case":     "Cotton -37.7%, Rice -21.5% in single flood year",
+                "novel":    True,
+            },
+            {
+                "rank":     3,
+                "title":    "GDP Contraction Predicted",
+                "finding":  "LSTM correctly predicted 2023 GDP contraction direction",
+                "evidence": "Inflation MAE 2.87% — predicted 26% vs actual 30.8%",
+                "case":     "Predicted 'contraction likely' → actual GDP -0.41%",
+                "novel":    False,
+            },
+        ]
+    }

models/causal_links.csv

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+cause,effect,lag_years,strength
+food_production_index,crop_stress,1,0.3646
+rain_anomaly,inflation_pct,2,0.3471
+gdp_growth_pct,crop_stress,1,0.2243
+gdp_growth_pct,inflation_pct,4,0.2204
+food_production_index,rain_anomaly,1,0.208
+crop_stress,food_production_index,4,0.1896
+rain_anomaly,crop_stress,2,0.1811
+crop_stress,rain_anomaly,1,0.1748
+gdp_growth_pct,food_production_index,4,0.1507
+temp_anomaly,inflation_pct,4,0.1281
+inflation_pct,food_production_index,1,0.125
+inflation_pct,rain_anomaly,4,0.1126
+temp_anomaly,rain_anomaly,4,0.1056
+rain_anomaly,temp_anomaly,2,0.0886
+crop_stress,temp_anomaly,3,0.0837
+inflation_pct,gdp_growth_pct,2,0.0813
+gdp_growth_pct,rain_anomaly,4,0.0805
+temp_anomaly,food_production_index,1,0.0791
+rain_anomaly,gdp_growth_pct,4,0.0782
+food_production_index,temp_anomaly,3,0.0753
+inflation_pct,temp_anomaly,1,0.0723

models/climashock_models.zip

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+oid sha256:dfce7d8c31f29a9fd9d3021344b672ea88d5b0e07960d573d0c3a586241be787
+size 160909

models/district_metrics.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:efd03cd03ad12287e406d0d48842cf0dbde6c6353ce068f9a2aaaeffe4ab095b
+size 397

models/ensemble_config.json

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+{
+  "w_lstm_inf": 0.46407631271108857,
+  "w_xgb_inf": 0.5359236872889115,
+  "w_lstm_crop": 0.569888151721099,
+  "w_xgb_crop": 0.43011184827890103,
+  "ens_inf_mae": 3.1318494579313376,
+  "ens_crop_mae": 7.894275778247052,
+  "xgb_features": [
+    "rain_mean",
+    "rain_std",
+    "temp_mean",
+    "temp_max",
+    "humidity",
+    "gdp_growth_pct",
+    "agri_gdp_pct",
+    "rain_lag1",
+    "rain_lag2",
+    "inf_lag1",
+    "inf_lag2",
+    "crop_lag1",
+    "gdp_lag1"
+  ],
+  "lstm_features": [
+    "rain_mean",
+    "rain_std",
+    "temp_mean",
+    "temp_max",
+    "humidity",
+    "gdp_growth_pct",
+    "agri_gdp_pct"
+  ],
+  "seq_len": 3
+}

models/feat_scaler.pkl

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+size 1103

models/target_scaler.pkl

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models/xgb_crop.pkl

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models/xgb_inf.pkl

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requirements.txt

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+fastapi==0.111.0
+uvicorn==0.29.0
+scikit-learn==1.4.2
+xgboost==2.1.0
+pandas==2.2.2
+numpy==1.26.4
+joblib==1.4.2