Create server.py

server.py

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+import os
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import openai
+import torch
+from transformers import pipeline
+from diffusers import StableDiffusionPipeline
+from PIL import Image
+import io
+import base64
+import soundfile as sf
+import numpy as np
+
+# Initialize APIs
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+app = FastAPI(title="Maxenoz AI Server")
+
+# Request models
+class TextRequest(BaseModel):
+    input_text: str
+
+class ImageRequest(BaseModel):
+    prompt: str
+
+class AudioRequest(BaseModel):
+    seed: int = 42
+
+# --- Health check ---
+@app.get("/")
+def health():
+    return {"status": "OK", "message": "Server is running"}
+
+# --- GPT Text Endpoint ---
+@app.post("/ai/text")
+def ai_text(request: TextRequest):
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-4-turbo",
+            messages=[{"role": "user", "content": request.input_text}]
+        )
+        return {"output_text": response.choices[0].message.content}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# --- Image Generation Endpoint ---
+@app.post("/ai/image")
+def ai_image(request: ImageRequest):
+    try:
+        # Minimal example using StableDiffusion
+        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        pipe = pipe.to("cpu")  # Change to "cuda" if GPU available
+        image = pipe(request.prompt).images[0]
+        # Encode image to base64
+        buf = io.BytesIO()
+        image.save(buf, format="PNG")
+        img_bytes = buf.getvalue()
+        img_b64 = base64.b64encode(img_bytes).decode("utf-8")
+        return {"image_base64": img_b64}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# --- Audio Generation Endpoint (placeholder) ---
+@app.post("/ai/audio")
+def ai_audio(request: AudioRequest):
+    try:
+        # Example: generate 1-second silent audio
+        audio = np.zeros(16000)  # 16kHz, 1 sec
+        buf = io.BytesIO()
+        sf.write(buf, audio, 16000, format="WAV")
+        audio_b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
+        return {"audio_base64": audio_b64}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# --- Run server ---
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)