Update app.py

app.py

@@ -32,20 +32,23 @@ DEFAULT_SUGGESTION = "Tente respirar fundo e escrever seus sentimentos."
 # Chamada segura à API do Hugging Face
 async def query_inference(model: str, payload: dict):
     headers = {"Authorization": f"Bearer {HF_API_KEY}"}
-    async with httpx.AsyncClient(timeout=30) as client:
-        response = await client.post(
-            f"https://api-inference.huggingface.co/models/{model}",
-            headers=headers,
-            json=payload
-        )
-    if response.status_code != 200:
-        return {"error": f"Erro {response.status_code}: {response.text}"}
-    return response.json()
+    try:
+        async with httpx.AsyncClient(timeout=60) as client:
+            response = await client.post(
+                f"https://api-inference.huggingface.co/models/{model}",
+                headers=headers,
+                json=payload
+            )
+        if response.status_code != 200:
+            return {"error": f"Erro {response.status_code}: {response.text}"}
+        return response.json()
+    except Exception as e:
+        return {"error": f"Falha ao conectar com a Inference API: {str(e)}"}
 
 # Tradução (PT → EN)
 async def translate_to_english(text: str) -> str:
     result = await query_inference(TRANSLATION_MODEL, {"inputs": text})
-    if isinstance(result, list) and "translation_text" in result[0]:
+    if isinstance(result, list) and len(result) > 0 and "translation_text" in result[0]:
         return result[0]["translation_text"]
     return text
 
@@ -54,12 +57,12 @@ async def classify_emotion(text: str) -> dict:
     result = await query_inference(EMOTION_MODEL, {"inputs": text})
 
     if isinstance(result, dict) and "error" in result:
-        return {"emotion": "unknown", "suggestion": "Não foi possível analisar a emoção."}
+        return {"emotion": "unknown", "suggestion": "Não foi possível analisar a emoção.", "debug": result}
 
     if isinstance(result, list) and len(result) > 0:
         top_label = result[0]["label"].lower()
         suggestion = EMOTION_SUGGESTIONS.get(top_label, DEFAULT_SUGGESTION)
-        return {"emotion": top_label, "suggestion": suggestion}
+        return {"emotion": top_label, "suggestion": suggestion, "raw": result}
 
     return {"emotion": "unknown", "suggestion": "Não foi possível analisar a emoção."}
 
@@ -67,10 +70,13 @@ async def classify_emotion(text: str) -> dict:
 @app.post("/analyze")
 async def analyze_text(input: TextInput):
     text = input.text.strip()
+
+    # Se tiver acentos, traduz para inglês antes
     if any(c in text for c in "áéíóúãõâêôçà"):
         text_en = await translate_to_english(text)
     else:
         text_en = text
+
     return await classify_emotion(text_en)
 
 # Endpoint simples para verificar se está rodando
@@ -79,4 +85,4 @@ async def root():
     return {"status": "ok", "message": "API de análise emocional rodando 🚀"}
 
 if __name__ == "__main__":
-    uvicorn.run("app:app", host="0.0.0.0", port=7860)
+    uvicorn.run("app:app", host="0.0.0.0", port=7860)