Create app.py

app.py

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+from fastapi import FastAPI, File, UploadFile, Request
+from fastapi.responses import StreamingResponse, JSONResponse
+from transformers import pipeline
+from TTS.api import TTS
+import whisper
+import torch
+from io import BytesIO
+from PIL import Image
+import base64
+import os
+
+app = FastAPI(title="NasFit AI Server")
+
+# 🔐 API KEY
+API_KEY = os.getenv("API_KEY", "nasfit_secret_key")
+
+# 🧠 Carga de modelos
+print("Cargando modelos...")
+
+chat_pipe = pipeline("text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct")
+vision_pipe = pipeline("image-text-to-text", model="lmms-lab/llava-onevision-1.6-7b-hf")
+whisper_model = whisper.load_model("small")
+tts = TTS("coqui/XTTS-v2")
+
+print("✅ Modelos listos.")
+
+# -------------------------------
+# Auth
+# -------------------------------
+async def check_auth(request: Request):
+    auth = request.headers.get("Authorization", "")
+    if not auth or not auth.startswith("Bearer ") or auth.split(" ")[1] != API_KEY:
+        return False
+    return True
+
+# -------------------------------
+# Chat + Vision
+# -------------------------------
+@app.post("/v1/chat/completions")
+async def chat_endpoint(request: Request):
+    if not await check_auth(request):
+        return JSONResponse({"error": "Unauthorized"}, status_code=401)
+    payload = await request.json()
+    messages = payload.get("messages", [])
+    model = payload.get("model", "llama3")
+
+    # Soporte de imagen
+    image_content = None
+    text_content = ""
+    for msg in messages:
+        content = msg.get("content", "")
+        if isinstance(content, list):
+            for c in content:
+                if c.get("type") == "text":
+                    text_content += c.get("text", "")
+                elif c.get("type") == "image_url":
+                    img_url = c["image_url"]["url"]
+                    if img_url.startswith("data:image"):
+                        image_content = Image.open(BytesIO(base64.b64decode(img_url.split(",")[1])))
+        else:
+            text_content += content
+
+    if image_content:
+        response = vision_pipe(text_content, images=image_content)[0]["generated_text"]
+    else:
+        response = chat_pipe(text_content, max_new_tokens=300)[0]["generated_text"]
+
+    return {"choices": [{"message": {"content": response}}]}
+
+# -------------------------------
+# Speech to text
+# -------------------------------
+@app.post("/v1/audio/transcriptions")
+async def transcribe(request: Request, file: UploadFile = File(...)):
+    if not await check_auth(request):
+        return JSONResponse({"error": "Unauthorized"}, status_code=401)
+    audio = await file.read()
+    with open("temp.wav", "wb") as f:
+        f.write(audio)
+    result = whisper_model.transcribe("temp.wav")
+    return {"text": result["text"]}
+
+# -------------------------------
+# Text to speech
+# -------------------------------
+@app.post("/v1/audio/speech")
+async def tts_endpoint(request: Request):
+    if not await check_auth(request):
+        return JSONResponse({"error": "Unauthorized"}, status_code=401)
+    payload = await request.json()
+    text = payload.get("input", "")
+    voice = payload.get("voice", "es_male_01")
+    tts.tts_to_file(text=text, file_path="output.wav", speaker=voice)
+    with open("output.wav", "rb") as f:
+        audio = f.read()
+    return StreamingResponse(BytesIO(audio), media_type="audio/wav")