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Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application
+COPY app.py .
+
+# HuggingFace Spaces usa porta 7860
+EXPOSE 7860
+
+# Run the application
+CMD ["python", "app.py"]

README.md

@@ -1,11 +1,36 @@
----
-title: Anima
-emoji: 🏢
-colorFrom: gray
-colorTo: blue
-sdk: docker
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Anima API
+emoji: 🎭
+colorFrom: purple
+colorTo: blue
+sdk: docker
+pinned: false
+---
+
+# Anima API
+
+Backend para o Anima - AI Avatar Chat.
+
+## Endpoints
+
+- `GET /health` - Health check
+- `POST /chat` - Enviar mensagem e receber resposta com áudio
+
+## Uso
+
+```python
+import requests
+
+response = requests.post(
+    "https://seu-space.hf.space/chat",
+    json={"message": "Olá!", "history": []}
+)
+data = response.json()
+print(data["text"])  # Resposta do LLM
+# data["audio_base64"] contém o áudio em base64
+```
+
+## Variáveis de Ambiente
+
+Configure no HuggingFace Spaces Settings:
+- `GROQ_API_KEY` - Sua API key do Groq

app.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import edge_tts
+import asyncio
+import base64
+import os
+from groq import Groq
+
+app = FastAPI(title="Anima - AI Avatar Chat")
+
+# CORS para permitir frontend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Groq client
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# Voz do Edge-TTS (português brasileiro feminino)
+VOICE = "pt-BR-FranciscaNeural"
+
+class ChatRequest(BaseModel):
+    message: str
+    history: list = []
+
+class ChatResponse(BaseModel):
+    text: str
+    audio_base64: str
+
+@app.get("/health")
+async def health():
+    return {"status": "ok"}
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    try:
+        # Monta o histórico para o LLM
+        messages = [
+            {"role": "system", "content": "Você é Anima, uma assistente virtual amigável e prestativa. Responda de forma natural e concisa em português brasileiro."}
+        ]
+        
+        for msg in request.history:
+            messages.append(msg)
+        
+        messages.append({"role": "user", "content": request.message})
+        
+        # Chama o Groq
+        completion = client.chat.completions.create(
+            model="llama-3.1-8b-instant",
+            messages=messages,
+            temperature=0.7,
+            max_tokens=500,
+        )
+        
+        response_text = completion.choices[0].message.content
+        
+        # Gera áudio com Edge-TTS
+        communicate = edge_tts.Communicate(response_text, VOICE)
+        audio_data = b""
+        
+        async for chunk in communicate.stream():
+            if chunk["type"] == "audio":
+                audio_data += chunk["data"]
+        
+        audio_base64 = base64.b64encode(audio_data).decode("utf-8")
+        
+        return ChatResponse(text=response_text, audio_base64=audio_base64)
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+fastapi==0.104.1
+uvicorn==0.24.0
+edge-tts==6.1.9
+groq==0.4.2
+python-multipart==0.0.6