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Dockerfile

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+FROM python:3.10
+
+WORKDIR /app
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+app = FastAPI()
+
+MODEL_NAME = "kamalkraj/distilbiobert_cancer_classification"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+LABELS = [
+ "breast_cancer",
+ "lung_cancer",
+ "prostate_cancer",
+ "colon_cancer",
+ "lymphoma",
+ "melanoma",
+ "thyroid_cancer",
+ "kidney_cancer",
+ "pancreatic_cancer",
+ "ovarian_cancer",
+ "cervical_cancer",
+ "brain_tumor"
+]
+
+class Input(BaseModel):
+    text: str
+
+@app.get("/")
+def home():
+    return {"status": "Cancer Type Classifier is running!"}
+
+@app.post("/predict")
+def predict(data: Input):
+    inputs = tokenizer(data.text, return_tensors="pt", truncation=True)
+    outputs = model(**inputs)
+
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+    label_id = torch.argmax(probs).item()
+    confidence = float(torch.max(probs))
+
+    return {
+        "prediction": LABELS[label_id],
+        "confidence": confidence
+    }

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch