Agregando endpoints API para integracion con chat NTIA

app.py

@@ -562,4 +562,95 @@ if __name__ == "__main__":
         server_name="0.0.0.0",
         server_port=7860,
         share=False
-    ) 
+    )
+
+# Endpoints API para integración con chat NTIA
+import gradio_client
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import base64
+from io import BytesIO
+
+app = FastAPI(title="NTIA Space API")
+
+# Configurar CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class TextRequest(BaseModel):
+    prompt: str
+    model_name: str
+    max_length: int = 100
+
+class ImageRequest(BaseModel):
+    prompt: str
+    model_name: str
+    num_inference_steps: int = 20
+
+class VideoRequest(BaseModel):
+    prompt: str
+    model_name: str
+    num_frames: int = 16
+    num_inference_steps: int = 20
+
+class ChatRequest(BaseModel):
+    message: str
+    history: list
+    model_name: str
+
+@app.post("/generate_text")
+async def api_generate_text(request: TextRequest):
+    try:
+        result = generate_text(request.prompt, request.model_name, request.max_length)
+        return {"response": result}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/generate_image")
+async def api_generate_image(request: ImageRequest):
+    try:
+        result = generate_image(request.prompt, request.model_name, request.num_inference_steps)
+        
+        # Convertir imagen a base64
+        if isinstance(result, str) and result.startswith("Error"):
+            raise HTTPException(status_code=500, detail=result)
+        
+        # Convertir PIL Image a base64
+        buffer = BytesIO()
+        result.save(buffer, format="PNG")
+        img_str = base64.b64encode(buffer.getvalue()).decode()
+        
+        return {"image": f"data:image/png;base64,{img_str}"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/generate_video")
+async def api_generate_video(request: VideoRequest):
+    try:
+        result = generate_video(request.prompt, request.model_name, request.num_frames, request.num_inference_steps)
+        
+        if isinstance(result, str) and result.startswith("Error"):
+            raise HTTPException(status_code=500, detail=result)
+        
+        # Convertir frames a video (simplificado)
+        # En producción, usarías ffmpeg para crear un video real
+        return {"video": "video_data_placeholder", "frames": len(result) if isinstance(result, list) else 0}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/chat")
+async def api_chat(request: ChatRequest):
+    try:
+        result = chat_with_model(request.message, request.history, request.model_name)
+        return {"response": result[-1]["content"] if result else "No response"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Montar la API en el mismo servidor que Gradio
+demo.app = app 

requirements.txt

@@ -11,4 +11,7 @@ xformers>=0.0.20
 huggingface_hub>=0.19.0
 opencv-python>=4.8.0
 imageio>=2.31.0
-imageio-ffmpeg>=0.4.8 
+imageio-ffmpeg>=0.4.8
+fastapi>=0.104.0
+uvicorn>=0.24.0
+pydantic>=2.0.0