added basic distilbart and it should most probablly work

Dockerfile

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+FROM python:3.8-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender-dev \
+    libgl1-mesa-glx \
+    wget \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "4"]

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import torch
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+from torch.nn.functional import softmax
+
+app = FastAPI(
+    title="Contact Information Detection API",
+    description="API for detecting contact information in text",
+    version="1.0.0",
+    docs_url="/"
+)
+
+class ContactDetector:
+    def __init__(self):
+        self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+        self.model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.model.to(self.device)
+        self.model.eval()
+
+    def detect_contact_info(self, text):
+        inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(self.device)
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        probabilities = softmax(outputs.logits, dim=1)
+        return probabilities[0][1].item()  # Probability of contact info
+
+    def is_contact_info(self, text, threshold=0.5):
+        return self.detect_contact_info(text) > threshold
+
+detector = ContactDetector()
+
+class TextInput(BaseModel):
+    text: str
+
+@app.post("/detect_contact", summary="Detect contact information in text")
+async def detect_contact(input: TextInput):
+    try:
+        probability = detector.detect_contact_info(input.text)
+        is_contact = detector.is_contact_info(input.text)
+        return {
+            "text": input.text,
+            "contact_probability": probability,
+            "is_contact_info": is_contact
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

requirements.txt

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+fastapi==0.68.0
+uvicorn==0.15.0
+torch==2.4.1
+transformers==4.10.0