Update app/main.py

app/main.py

@@ -1,145 +1,141 @@
-import json
-import os
-from fastapi import FastAPI
-import uvicorn
-from engine.risk_engine import VertexRiskEngine
-from engine.semantic import VertexSemanticAgent
-from engine.web3_engine import VertexWeb3Engine
-
-app = FastAPI()
-
-# Archivo de persistencia de la watchlist
-WATCHLIST_FILE = "app/watchlist.json"
-
-@app.get("/batch_audit")
-def run_batch_audit():
-    """Audita toda la watchlist y devuelve alertas para n8n."""
-    if not os.path.exists(WATCHLIST_FILE):
-        return {"error": "Watchlist file not found", "alerts": []}
-
-    try:
-        with open(WATCHLIST_FILE, "r") as f:
-            watchlist = json.load(f)
-    except Exception as e:
-        return {"error": f"Failed to read watchlist: {e}", "alerts": []}
-
-    results = []
-    for ticker in watchlist:
-        try:
-            # Reutilizamos la lógica del motor Némesis
-            engine = VertexRiskEngine(ticker.upper())
-            num_res = engine.run_audit()
-            
-            sem_agent = VertexSemanticAgent(ticker.upper())
-            risk_text = sem_agent.get_sec_risk_factors()
-            sem_res = sem_agent.judge_risks(risk_text)
-
-            # Blindaje de tipos para evitar errores de comparación
-            z_score = float(num_res.get('altman_z', 0.0))
-            m_dsri = float(num_res.get('m_score_dsri', 0.0))
-            s_score = float(sem_res.get('semantic_score', 0.0))
-
-            # Lógica de Semáforo de Riesgo
-            if z_score < 1.8 or m_dsri > 1.4 or s_score > 3:
-                status = "RED"
-            elif z_score < 3.0:
-                status = "YELLOW"
-            else:
-                status = "GREEN"
-
-            results.append({
-                "ticker": ticker,
-                "status": status,
-                "z_score": z_score,
-                "summary": sem_res.get("summary", ""),
-                "alert": True if status == "RED" else False
-            })
-        except Exception as ticker_error:
-            results.append({"ticker": ticker, "status": "ERROR", "msg": str(ticker_error)})
-
-    critical_alerts = [r for r in results if r.get("status") in ["RED", "YELLOW"]]
-    return {
-        "total_analyzed": len(watchlist),
-        "critical_count": len(critical_alerts),
-        "alerts": critical_alerts,
-        "full_results": results
-    }
-
-@app.get("/audit/{ticker}")
-def audit_company(ticker: str):
-    """Auditoría individual para el tab de Stock Audit."""
-    try:
-        engine = VertexRiskEngine(ticker.upper())
-        num_res = engine.run_audit()
-        
-        sem_agent = VertexSemanticAgent(ticker.upper())
-        risk_text = sem_agent.get_sec_risk_factors()
-        sem_res = sem_agent.judge_risks(risk_text)
-
-        # Blindaje de tipos
-        z_score = float(num_res.get('altman_z', 0.0))
-        m_dsri = float(num_res.get('m_score_dsri', 0.0))
-        s_score = float(sem_res.get('semantic_score', 0.0))
-
-        if z_score < 1.8 or m_dsri > 1.4 or s_score > 3:
-            status = "RED"
-            msg = "CRITICAL RISK: Red flags detected."
-        elif z_score < 3.0:
-            status = "YELLOW"
-            msg = "CAUTION: Monitor closely."
-        else:
-            status = "GREEN"
-            msg = "SAFE: Solid fundamentals."
-
-        return {
-            "ticker": ticker.upper(),
-            "status": status,
-            "numeric_analysis": {"altman_z": z_score, "m_score_dsri": m_dsri},
-            "semantic_analysis": sem_res,
-            "msg": msg
-        }
-    except Exception as e:
-        return {"status": "ERROR", "msg": str(e)}
-
-@app.get("/audit_contract/{address}")
-def audit_smart_contract(address: str):
-    """Auditoría de Web3 usando el nuevo motor V2."""
-    try:
-        web3_engine = VertexWeb3Engine(address)
-        audit_res = web3_engine.get_contract_source()
-        
-        if not audit_res["success"]:
-            return {"status": "ERROR", "msg": audit_res["error"]}
-        
-        source = audit_res["source_code"]
-        vulnerabilities = web3_engine.scan_basic_vulnerabilities(source)
-        
-        return {
-            "address": address,
-            "status": "DANGER" if len(vulnerabilities) > 0 else "SAFE",
-            "vulnerabilities": vulnerabilities,
-            "source_preview": source[:500] + "..." # Ahora source es string y el slice no falla
-        }
-    except Exception as e:
-        return {"status": "ERROR", "msg": str(e)}
-
-@app.get("/get_settings")
-async def get_settings():
-    try:
-        path = "app/settings.json"
-        if os.path.exists(path):
-            with open(path, "r") as f:
-                content = f.read().strip()
-                if not content:
-                    return {"bot_token": "", "chat_id": "", "status": "empty_file"}
-                return json.loads(content)
-        return {"bot_token": "", "chat_id": "", "status": "not_found"}
-    except Exception as e:
-        # Esto captura el error "Expecting value" y devuelve un JSON válido para n8n
-        return {"bot_token": "", "chat_id": "", "error": f"JSON Error: {str(e)}"}
-
-if __name__ == "__main__":
-    # Hugging Face SIEMPRE usa el puerto 7860 internamente
-    port = int(os.environ.get("PORT", 7860))
-    # Importante: host="0.0.0.0" para que sea accesible desde fuera del contenedor
-    uvicorn.run(app, host="0.0.0.0", port=port)
+import json
+import os
+from fastapi import FastAPI
+import uvicorn
+from engine.risk_engine import VertexRiskEngine
+from engine.semantic import VertexSemanticAgent
+from engine.web3_engine import VertexWeb3Engine
+
+app = FastAPI()
+
+# Archivo de persistencia de la watchlist
+WATCHLIST_FILE = "app/watchlist.json"
+
+@app.get("/batch_audit")
+def run_batch_audit():
+    """Audita toda la watchlist y devuelve alertas para n8n."""
+    if not os.path.exists(WATCHLIST_FILE):
+        return {"error": "Watchlist file not found", "alerts": []}
+
+    try:
+        with open(WATCHLIST_FILE, "r") as f:
+            watchlist = json.load(f)
+    except Exception as e:
+        return {"error": f"Failed to read watchlist: {e}", "alerts": []}
+
+    results = []
+    for ticker in watchlist:
+        try:
+            # Reutilizamos la lógica del motor Némesis
+            engine = VertexRiskEngine(ticker.upper())
+            num_res = engine.run_audit()
+            
+            sem_agent = VertexSemanticAgent(ticker.upper())
+            risk_text = sem_agent.get_sec_risk_factors()
+            sem_res = sem_agent.judge_risks(risk_text)
+
+            # Blindaje de tipos para evitar errores de comparación
+            z_score = float(num_res.get('altman_z', 0.0))
+            m_dsri = float(num_res.get('m_score_dsri', 0.0))
+            s_score = float(sem_res.get('semantic_score', 0.0))
+
+            # Lógica de Semáforo de Riesgo
+            if z_score < 1.8 or m_dsri > 1.4 or s_score > 3:
+                status = "RED"
+            elif z_score < 3.0:
+                status = "YELLOW"
+            else:
+                status = "GREEN"
+
+            results.append({
+                "ticker": ticker,
+                "status": status,
+                "z_score": z_score,
+                "summary": sem_res.get("summary", ""),
+                "alert": True if status == "RED" else False
+            })
+        except Exception as ticker_error:
+            results.append({"ticker": ticker, "status": "ERROR", "msg": str(ticker_error)})
+
+    critical_alerts = [r for r in results if r.get("status") in ["RED", "YELLOW"]]
+    return {
+        "total_analyzed": len(watchlist),
+        "critical_count": len(critical_alerts),
+        "alerts": critical_alerts,
+        "full_results": results
+    }
+
+@app.get("/audit/{ticker}")
+def audit_company(ticker: str):
+    """Auditoría individual para el tab de Stock Audit."""
+    try:
+        engine = VertexRiskEngine(ticker.upper())
+        num_res = engine.run_audit()
+        
+        sem_agent = VertexSemanticAgent(ticker.upper())
+        risk_text = sem_agent.get_sec_risk_factors()
+        sem_res = sem_agent.judge_risks(risk_text)
+
+        # Blindaje de tipos
+        z_score = float(num_res.get('altman_z', 0.0))
+        m_dsri = float(num_res.get('m_score_dsri', 0.0))
+        s_score = float(sem_res.get('semantic_score', 0.0))
+
+        if z_score < 1.8 or m_dsri > 1.4 or s_score > 3:
+            status = "RED"
+            msg = "CRITICAL RISK: Red flags detected."
+        elif z_score < 3.0:
+            status = "YELLOW"
+            msg = "CAUTION: Monitor closely."
+        else:
+            status = "GREEN"
+            msg = "SAFE: Solid fundamentals."
+
+        return {
+            "ticker": ticker.upper(),
+            "status": status,
+            "numeric_analysis": {"altman_z": z_score, "m_score_dsri": m_dsri},
+            "semantic_analysis": sem_res,
+            "msg": msg
+        }
+    except Exception as e:
+        return {"status": "ERROR", "msg": str(e)}
+
+@app.get("/audit_contract/{address}")
+def audit_smart_contract(address: str):
+    """Auditoría de Web3 usando el nuevo motor V2."""
+    try:
+        web3_engine = VertexWeb3Engine(address)
+        audit_res = web3_engine.get_contract_source()
+        
+        if not audit_res["success"]:
+            return {"status": "ERROR", "msg": audit_res["error"]}
+        
+        source = audit_res["source_code"]
+        vulnerabilities = web3_engine.scan_basic_vulnerabilities(source)
+        
+        return {
+            "address": address,
+            "status": "DANGER" if len(vulnerabilities) > 0 else "SAFE",
+            "vulnerabilities": vulnerabilities,
+            "source_preview": source[:500] + "..." # Ahora source es string y el slice no falla
+        }
+    except Exception as e:
+        return {"status": "ERROR", "msg": str(e)}
+
+@app.get("/get_settings")
+async def get_settings():
+    # Esta línea es la que saca las llaves de los Secrets de Hugging Face
+    token = os.environ.get("BOT_TOKEN", "")
+    chat = os.environ.get("CHAT_ID", "")
+    
+    if token and chat:
+        return {"bot_token": token, "chat_id": chat, "status": "loaded_from_secrets"}
+    
+    return {"bot_token": "", "chat_id": "", "status": "secrets_not_found"}
+
+if __name__ == "__main__":
+    # Hugging Face SIEMPRE usa el puerto 7860 internamente
+    port = int(os.environ.get("PORT", 7860))
+    # Importante: host="0.0.0.0" para que sea accesible desde fuera del contenedor
+    uvicorn.run(app, host="0.0.0.0", port=port)