Create main.py

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import torch
+
+# Load your fine-tuned model
+model_path = "./t5-summarizer"  # Path inside Docker container
+model = T5ForConditionalGeneration.from_pretrained(model_path)
+tokenizer = T5Tokenizer.from_pretrained(model_path)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+app = FastAPI()
+
+class TextInput(BaseModel):
+    text: str
+
+@app.post("/summarize/")
+def summarize_text(input: TextInput):
+    input_text = "summarize: " + input.text.strip().replace("\n", " ")
+
+    inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True).to(device)
+    summary_ids = model.generate(inputs, max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
+
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return {"summary": summary}