Add cascading fallback for 4 Groq API keys

app.py

@@ -350,62 +350,69 @@ def generate_heatmap_image(data: np.ndarray, index_type: str, gaussian_sigma: fl
 # ============================================================================
 # LLM ANALYSIS (for risk metrics)
 # ============================================================================
+
+# Groq API keys with cascading fallback
+GROQ_API_KEYS = [
+    "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",
+    "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",
+    "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",
+    "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",
+]
+
 def run_llm_analysis(metric: str, stress_context: dict, indices_data: dict, 
                      time_series_data: dict = None, weather_data: dict = None) -> dict:
-    """Call Groq LLM with full context from stress detection, timeseries, and weather."""
-    try:
-        from groq import Groq
-        
-        GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "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")
-        GROQ_MODEL = "llama-3.3-70b-versatile"
-        
-        client = Groq(api_key=GROQ_API_KEY)
-        
-        # Format stress context
-        stress_text = format_stress_context(stress_context)
-        
-        # Format time series data for all indices
-        ts_text = ""
-        if time_series_data:
-            ts_text = "\n\nTIME SERIES DATA (ALL INDICES - HISTORICAL + FORECAST):\n"
-            ts_text += "=" * 50 + "\n"
-            for index_name, ts_data in time_series_data.items():
-                ts_text += f"\n{index_name}:\n"
-                # Historical
-                if ts_data.get('historical'):
-                    hist = ts_data['historical']
-                    if len(hist) > 0:
-                        first_val = hist[0].get('value', 0) if isinstance(hist[0], dict) else 0
-                        last_val = hist[-1].get('value', 0) if isinstance(hist[-1], dict) else 0
-                        ts_text += f"  Historical ({len(hist)} points): from {first_val:.4f} to {last_val:.4f} (change: {last_val-first_val:+.4f})\n"
-                # Forecast
-                if ts_data.get('forecast'):
-                    fcast = ts_data['forecast']
-                    if len(fcast) > 0:
-                        first_val = fcast[0].get('value', 0) if isinstance(fcast[0], dict) else 0
-                        last_val = fcast[-1].get('value', 0) if isinstance(fcast[-1], dict) else 0
-                        ts_text += f"  Forecast ({len(fcast)} days): from {first_val:.4f} to {last_val:.4f} (predicted: {last_val-first_val:+.4f})\n"
-        
-        # Format weather data
-        weather_text = ""
-        if weather_data:
-            weather_text = "\n\nWEATHER CONDITIONS:\n"
-            weather_text += "=" * 30 + "\n"
-            if 'temperature' in weather_data:
-                weather_text += f"- Temperature: {weather_data['temperature']}°C\n"
-            if 'humidity' in weather_data:
-                weather_text += f"- Humidity: {weather_data['humidity']}%\n"
-            if 'precipitation' in weather_data:
-                weather_text += f"- Precipitation: {weather_data['precipitation']} mm\n"
-            if 'wind_speed' in weather_data:
-                weather_text += f"- Wind Speed: {weather_data['wind_speed']} km/h\n"
-            if 'conditions' in weather_data:
-                weather_text += f"- Conditions: {weather_data['conditions']}\n"
-            if 'forecast' in weather_data:
-                weather_text += f"- Forecast: {weather_data['forecast']}\n"
-        
-        # Create targeted prompt based on metric
-        prompt = f"""CROP STRESS ANALYSIS REQUEST
+    """Call Groq LLM with full context from stress detection, timeseries, and weather.
+    Uses cascading fallback through 4 API keys if one fails."""
+    from groq import Groq
+    import json
+    
+    GROQ_MODEL = "llama-3.3-70b-versatile"
+    
+    # Format stress context
+    stress_text = format_stress_context(stress_context)
+    
+    # Format time series data for all indices
+    ts_text = ""
+    if time_series_data:
+        ts_text = "\n\nTIME SERIES DATA (ALL INDICES - HISTORICAL + FORECAST):\n"
+        ts_text += "=" * 50 + "\n"
+        for index_name, ts_data in time_series_data.items():
+            ts_text += f"\n{index_name}:\n"
+            # Historical
+            if ts_data.get('historical'):
+                hist = ts_data['historical']
+                if len(hist) > 0:
+                    first_val = hist[0].get('value', 0) if isinstance(hist[0], dict) else 0
+                    last_val = hist[-1].get('value', 0) if isinstance(hist[-1], dict) else 0
+                    ts_text += f"  Historical ({len(hist)} points): from {first_val:.4f} to {last_val:.4f} (change: {last_val-first_val:+.4f})\n"
+            # Forecast
+            if ts_data.get('forecast'):
+                fcast = ts_data['forecast']
+                if len(fcast) > 0:
+                    first_val = fcast[0].get('value', 0) if isinstance(fcast[0], dict) else 0
+                    last_val = fcast[-1].get('value', 0) if isinstance(fcast[-1], dict) else 0
+                    ts_text += f"  Forecast ({len(fcast)} days): from {first_val:.4f} to {last_val:.4f} (predicted: {last_val-first_val:+.4f})\n"
+    
+    # Format weather data
+    weather_text = ""
+    if weather_data:
+        weather_text = "\n\nWEATHER CONDITIONS:\n"
+        weather_text += "=" * 30 + "\n"
+        if 'temperature' in weather_data:
+            weather_text += f"- Temperature: {weather_data['temperature']}°C\n"
+        if 'humidity' in weather_data:
+            weather_text += f"- Humidity: {weather_data['humidity']}%\n"
+        if 'precipitation' in weather_data:
+            weather_text += f"- Precipitation: {weather_data['precipitation']} mm\n"
+        if 'wind_speed' in weather_data:
+            weather_text += f"- Wind Speed: {weather_data['wind_speed']} km/h\n"
+        if 'conditions' in weather_data:
+            weather_text += f"- Conditions: {weather_data['conditions']}\n"
+        if 'forecast' in weather_data:
+            weather_text += f"- Forecast: {weather_data['forecast']}\n"
+    
+    # Create targeted prompt based on metric
+    prompt = f"""CROP STRESS ANALYSIS REQUEST
 
 {stress_text}
 {ts_text}
@@ -424,37 +431,50 @@ Respond with ONLY a valid JSON object (no markdown):
     "recommendations": ["action 1", "action 2", "action 3"]
 }}
 """
-        
-        chat_completion = client.chat.completions.create(
-            messages=[
-                {"role": "system", "content": "You are an expert agricultural AI. Provide detailed, data-driven analysis. Respond with valid JSON only."},
-                {"role": "user", "content": prompt}
-            ],
-            model=GROQ_MODEL,
-            temperature=0.7,
-            max_tokens=1500,
-        )
-        
-        response_text = chat_completion.choices[0].message.content.strip()
-        
-        # Clean markdown if present
-        if response_text.startswith("```"):
-            lines = response_text.split("\n")
-            response_text = "\n".join(lines[1:-1])
-        if response_text.startswith("json"):
-            response_text = response_text[4:].strip()
-        
-        import json
-        return json.loads(response_text)
-        
-    except Exception as e:
-        logger.error(f"LLM analysis failed: {e}")
-        return {
-            "level": "Moderate", 
-            "analysis": "Analysis unavailable", 
-            "detailed_analysis": "Unable to generate detailed analysis due to processing error. Please try refreshing.",
-            "recommendations": ["Manual inspection recommended"]
-        }
+    
+    # Try each API key in sequence (cascading fallback)
+    last_error = None
+    for i, api_key in enumerate(GROQ_API_KEYS):
+        try:
+            logger.info(f"Trying Groq API key {i+1}/{len(GROQ_API_KEYS)}")
+            client = Groq(api_key=api_key)
+            
+            chat_completion = client.chat.completions.create(
+                messages=[
+                    {"role": "system", "content": "You are an expert agricultural AI. Provide detailed, data-driven analysis. Respond with valid JSON only."},
+                    {"role": "user", "content": prompt}
+                ],
+                model=GROQ_MODEL,
+                temperature=0.7,
+                max_tokens=1500,
+            )
+            
+            response_text = chat_completion.choices[0].message.content.strip()
+            
+            # Clean markdown if present
+            if response_text.startswith("```"):
+                lines = response_text.split("\n")
+                response_text = "\n".join(lines[1:-1])
+            if response_text.startswith("json"):
+                response_text = response_text[4:].strip()
+            
+            result = json.loads(response_text)
+            logger.info(f"Groq API key {i+1} succeeded")
+            return result
+            
+        except Exception as e:
+            last_error = e
+            logger.warning(f"Groq API key {i+1} failed: {e}")
+            continue
+    
+    # All keys failed
+    logger.error(f"All {len(GROQ_API_KEYS)} Groq API keys failed. Last error: {last_error}")
+    return {
+        "level": "Moderate", 
+        "analysis": "Analysis unavailable", 
+        "detailed_analysis": "Unable to generate detailed analysis due to API errors. All API keys exhausted. Please try refreshing.",
+        "recommendations": ["Manual inspection recommended"]
+    }
 
 # ============================================================================
 # API ENDPOINTS