Create app.py

app.py

@@ -0,0 +1,37 @@
+from fastapi import FastAPI, Request, Query
+from fastapi.responses import StreamingResponse 
+from transformers import AutoModelForSequenceClassification, AutoTokenizer 
+
+import torch
+
+import time
+app = FastAPI()
+model_name = "prajjwal1/bert-tiny"  # Pretrained BERT-Tiny on Hugging Face
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+ 
+
+# SSE generator
+def event_stream(text: str):
+    
+    time.sleep(1)
+
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+        prediction = torch.argmax(probs, dim=1).item()
+ 
+    yield f"data: {prediction}\n\n"
+
+
+
+@app.get("/chatstrm")
+async def chat(query: str = Query(..., description="User's message")):
+      return StreamingResponse(event_stream(query) , media_type="text/event-stream")
+
+# Entry point
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("app:app", host="0.0.0.0", port=7899)