Fix: extract Sentinel-2 vegetation_indices_summary and SAR crop_health

app.py

@@ -317,18 +317,19 @@ def build_llm_prompt(query: str, context: Dict, history: List[Dict] = None) -> s
                 vi_lines.append(f"- {name}: {mean:.4f}{change_str}" if isinstance(mean, float) else f"- {name}: {mean}")
         context_parts.append("\n".join(vi_lines))
     
-    # SAR bands
+    # SAR Analysis (crop health from SAR data)
     if context.get("sar_bands"):
         sar = context["sar_bands"]
-        vv = sar.get('VV')
-        vh = sar.get('VH')
-        vv_val = vv.get('mean', vv) if isinstance(vv, dict) else (vv if vv else 'N/A')
-        vh_val = vh.get('mean', vh) if isinstance(vh, dict) else (vh if vh else 'N/A')
-        context_parts.append(f"""## SAR Backscatter (Sentinel-1)
-- VV: {vv_val} dB
-- VH: {vh_val} dB
-- VV/VH Ratio: {sar.get('VV_VH_ratio', 'N/A')}
-- Interpretation: {sar.get('interpretation', 'N/A')}""")
+        health = sar.get("crop_health", "unknown")
+        stress = sar.get("stress_score", 0)
+        summary = sar.get("summary", "")
+        recs = sar.get("recommendations", [])
+        context_parts.append(f"""## SAR Crop Health Analysis
+- Overall Health: {health}
+- Stress Score: {stress:.2f if isinstance(stress, float) else stress}
+- Confidence: {sar.get('confidence', 0)}%
+- Summary: {summary[:200] if summary else 'N/A'}
+- Top Recommendation: {recs[0] if recs else 'No specific recommendations'}""")
     
     # Soil indicators
     if context.get("soil_indicators"):

context_aggregator.py

@@ -234,15 +234,15 @@ class ContextAggregator:
     # =========================================================================
     
     def _extract_sar_bands(self, data: Dict) -> Dict:
-        """Extract SAR band values."""
-        sar = data.get("sar_bands", {})
+        """Extract SAR analysis data."""
+        # SAR API returns: status, crop_health, health_summary, average_stress_score, etc.
         return {
-            "VV": sar.get("vv"),
-            "VH": sar.get("vh"),
-            "VV_VH_ratio": sar.get("ratio"),
-            "VV_trend": sar.get("vv_trend", "stable"),
-            "VH_trend": sar.get("vh_trend", "stable"),
-            "interpretation": self._interpret_sar(sar.get("vv"), sar.get("vh"))
+            "crop_health": data.get("crop_health", "unknown"),
+            "confidence": data.get("confidence_score", 0),
+            "stress_score": data.get("average_stress_score", 0),
+            "summary": data.get("summary", ""),
+            "health_summary": data.get("health_summary", {}),
+            "recommendations": data.get("recommendations", [])
         }
     
     def _interpret_sar(self, vv: Optional[float], vh: Optional[float]) -> str:
@@ -260,7 +260,8 @@ class ContextAggregator:
     
     def _extract_vegetation_indices(self, data: Dict) -> Dict:
         """Extract and structure vegetation indices."""
-        veg = data.get("vegetation_indices", {})
+        # Try both possible keys from API response
+        veg = data.get("vegetation_indices", {}) or data.get("vegetation_indices_summary", {})
         
         result = {}
         for idx in ["ndvi", "evi", "ndre", "reci", "ndwi", "smi", "psri", "pri", "mcari"]: