Upload main.py

main.py

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+from fastapi import FastAPI, Request
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
+from scipy.special import softmax
+import numpy as np
+import uvicorn
+
+app = FastAPI()
+
+# Load model and tokenizer
+MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+config = AutoConfig.from_pretrained(MODEL)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+
+# Preprocessing function
+def preprocess(text):
+    tokens = []
+    for t in text.split():
+        if t.startswith("@") and len(t) > 1:
+            t = "@user"
+        elif t.startswith("http"):
+            t = "http"
+        tokens.append(t)
+    return " ".join(tokens)
+
+# Inference route
+@app.post("/analyze")
+async def analyze(request: Request):
+    data = await request.json()
+    text = preprocess(data.get("text", ""))
+
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+
+    ranking = np.argsort(scores)[::-1]
+    result = []
+    for i in ranking:
+        label = config.id2label[i]
+        score = round(float(scores[i]), 4)
+        result.append({"label": label, "score": score})
+
+    return {"result": result}