import gradio as gr
import logging
from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
from fastapi.middleware.cors import CORSMiddleware

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Load FLAN-T5 model
model_name = "google/flan-t5-base"
logger.info(f"Loading {model_name} model...")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
logger.info("Model loaded successfully!")

# -----------------------------
# REST API SECTION
# -----------------------------
api = FastAPI()

api.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Change to your domain later
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

class SummarizeRequest(BaseModel):
    text: str
    max_length: int = 150
    min_length: int = 30

@api.post("/summarize")
def summarize_endpoint(request: SummarizeRequest):
    text = request.text.strip()
    if not text or len(text) < 50:
        return {"summary": text}
    
    logger.info(f"Summarizing via API. Length: {len(text)}")

    input_text = f"summarize: {text}"
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=1024)

    # Safe dynamic length handling
    max_tokens = min(request.max_length, 512)
    min_tokens = min(request.min_length, max_tokens - 1)

    outputs = model.generate(
        **inputs,
        max_new_tokens=max_tokens,
        min_length=min_tokens
    )
    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"summary": summary}

# -----------------------------
# GRADIO UI SECTION
# -----------------------------
def summarize_text(text, max_length=150, min_length=30):
    return summarize_endpoint(SummarizeRequest(text=text, max_length=max_length, min_length=min_length))["summary"]

demo = gr.Interface(
    fn=summarize_text,
    inputs=[
        gr.Textbox(lines=10, label="Text to Summarize"),
        gr.Slider(50, 512, value=150, label="Max Length"),
        gr.Slider(10, 300, value=30, label="Min Length")
    ],
    outputs=gr.Textbox(label="Summary"),
    title="StudAI Text Summarization",
    description="Powered by google/flan-t5-base model"
)

# Mount Gradio + API
app = FastAPI()
app.mount("/", api)
demo.launch(server_name="0.0.0.0", server_port=7860, root_path="/", app=app)