Create app.py

app.py

@@ -0,0 +1,102 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import pipeline
+import torch
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI(title="Model Inference API")
+
+# Allow CORS for external frontend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+MODEL_MAP = {
+    "tinny-llama": "Lyon28/Tinny-Llama",
+    "pythia": "Lyon28/Pythia",
+    "bert-tinny": "Lyon28/Bert-Tinny",
+    "albert-base-v2": "Lyon28/Albert-Base-V2",
+    "t5-small": "Lyon28/T5-Small",
+    "gpt-2": "Lyon28/GPT-2",
+    "gpt-neo": "Lyon28/GPT-Neo",
+    "distilbert-base-uncased": "Lyon28/Distilbert-Base-Uncased",
+    "distil-gpt-2": "Lyon28/Distil_GPT-2",
+    "gpt-2-tinny": "Lyon28/GPT-2-Tinny",
+    "electra-small": "Lyon28/Electra-Small"
+}
+
+TASK_MAP = {
+    "text-generation": ["gpt-2", "gpt-neo", "distil-gpt-2", "gpt-2-tinny", "tinny-llama", "pythia"],
+    "text-classification": ["bert-tinny", "albert-base-v2", "distilbert-base-uncased", "electra-small"],
+    "text2text-generation": ["t5-small"]
+}
+
+class InferenceRequest(BaseModel):
+    text: str
+    max_length: int = 100
+    temperature: float = 0.9
+
+def get_task(model_id: str):
+    for task, models in TASK_MAP.items():
+        if model_id in models:
+            return task
+    return "text-generation"
+
+@app.on_event("startup")
+async def load_models():
+    # Initialize models (optional: pre-load critical models)
+    app.state.pipelines = {}
+    print("Models initialized in memory")
+
+@app.post("/inference/{model_id}")
+async def model_inference(model_id: str, request: InferenceRequest):
+    try:
+        if model_id not in MODEL_MAP:
+            raise HTTPException(status_code=404, detail="Model not found")
+
+        task = get_task(model_id)
+        
+        # Load pipeline with caching
+        if model_id not in app.state.pipelines:
+            app.state.pipelines[model_id] = pipeline(
+                task=task,
+                model=MODEL_MAP[model_id],
+                device_map="auto",
+                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+            )
+
+        pipe = app.state.pipelines[model_id]
+
+        # Process based on task
+        if task == "text-generation":
+            result = pipe(
+                request.text,
+                max_length=request.max_length,
+                temperature=request.temperature
+            )[0]['generated_text']
+        
+        elif task == "text-classification":
+            output = pipe(request.text)[0]
+            result = {
+                "label": output['label'],
+                "confidence": round(output['score'], 4)
+            }
+        
+        elif task == "text2text-generation":
+            result = pipe(request.text)[0]['generated_text']
+
+        return {"result": result}
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/models")
+async def list_models():
+    return {"available_models": list(MODEL_MAP.keys())}
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}