create app.py

app/app.py

@@ -0,0 +1,26 @@
+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model = AutoModelForSequenceClassification.from_pretrained("iconic7/Finance_GPT_Sentiment_Analysis")
+tokenizer = AutoTokenizer.from_pretrained("iconic7/Finance_GPT_Sentiment_Analysis")
+labels = ["Negative", "Neutral", "Positive"]
+
+@app.post("/predict")
+async def predict(request: Request):
+    data = await request.json()
+    text = data.get("text", "")
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+    return {"result": {label: float(prob) for label, prob in zip(labels, probs[0])}}