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import gradio as gr
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, legacy=False)
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}
# Gradio UI setup
gr.Interface(
fn=lambda text: summarize_text(TextInput(text=text))["summary"], # Ensure it returns summary
inputs=gr.Textbox(label="Input Text"),
outputs=gr.Textbox(label="Summarized Text"),
flagging=False # Disable flagging to prevent permission issues
).launch()
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