from fastapi import FastAPI
from pydantic import BaseModel
from transformers import pipeline, AutoTokenizer

app = FastAPI()

model_name = "facebook/bart-large-cnn"
summarizer = pipeline("summarization", model=model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

class SummarizationRequest(BaseModel):
    inputs: str

class SummarizationResponse(BaseModel):
    summary: str


def chunk_text(text, max_tokens=900):
    tokens = tokenizer.encode(text, truncation=False)
    chunks = []

    for i in range(0, len(tokens), max_tokens):
        chunk = tokens[i:i + max_tokens]
        chunks.append(tokenizer.decode(chunk, skip_special_tokens=True))

    return chunks


@app.post("/summarize", response_model=SummarizationResponse)
async def summarize_text(request: SummarizationRequest):
    chunks = chunk_text(request.inputs)
    
    summaries = []

    for chunk in chunks:
        input_length = len(chunk.split())
        max_length = min(250, max(100, int(input_length * 0.4)))
        min_length = min(100, max(50, int(input_length * 0.2)))

        summary = summarizer(chunk, max_length=max_length, min_length=min_length, do_sample=False)
        summaries.append(summary[0]["summary_text"])

    final_summary = " ".join(summaries)

    return {"summary": final_summary}


@app.get("/")
def greet_json():
    return {"message": "BART Summarizer API is running"}