main.py

from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from huggingface_hub import InferenceClient
import uvicorn
from typing import Generator
import json  # Asegúrate de que esta línea esté al principio del archivo
import nltk
import os
import google.protobuf  # This line should execute without errors if protobuf is installed correctly
import sentencepiece
from transformers import pipeline, AutoTokenizer,AutoModelForSeq2SeqLM


nltk.data.path.append(os.getenv('NLTK_DATA'))

app = FastAPI()

# Initialize the InferenceClient with your model
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")



# summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")


class Item(BaseModel):
    prompt: str
    history: list
    system_prompt: str
    temperature: float = 0.8
    max_new_tokens: int = 12000
    top_p: float = 0.15
    repetition_penalty: float = 1.0

def format_prompt(current_prompt, history):
    formatted_history = "<s>"
    for entry in history:
        if entry["role"] == "user":
            formatted_history += f"[USER] {entry['content']} [/USER]"
        elif entry["role"] == "assistant":
            formatted_history += f"[ASSISTANT] {entry['content']} [/ASSISTANT]"
    formatted_history += f"[USER] {current_prompt} [/USER]</s>"
    return formatted_history


def generate_stream(item: Item) -> Generator[bytes, None, None]:
    formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
    # Estimate token count for the formatted_prompt
    input_token_count = len(nltk.word_tokenize(formatted_prompt))  # NLTK tokenization

    # Ensure total token count doesn't exceed the maximum limit
    max_tokens_allowed = 32768
    max_new_tokens_adjusted = max(1, min(item.max_new_tokens, max_tokens_allowed - input_token_count))

    generate_kwargs = {
        "temperature": item.temperature,
        "max_new_tokens": max_new_tokens_adjusted,
        "top_p": item.top_p,
        "repetition_penalty": item.repetition_penalty,
        "do_sample": True,
        "seed": 42,
    }

    # Stream the response from the InferenceClient
    for response in client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True):
        # This assumes 'details=True' gives you a structure where you can access the text like this
        chunk = {
            "text": response.token.text,
            "complete": response.generated_text is not None  # Adjust based on how you detect completion
        }
        yield json.dumps(chunk).encode("utf-8") + b"\n"


class SummarizeRequest(BaseModel):
    text: str

@app.post("/generate/")
async def generate_text(item: Item):
    # Stream response back to the client
    return StreamingResponse(generate_stream(item), media_type="application/x-ndjson")

def split_text_by_tokens(text, max_tokens=1024):
    print("Tokenizing text...")
    tokens = tokenizer.tokenize(text)
    
    chunks = []
    token_counts = []
    
    for i in range(0, len(tokens), max_tokens):
        chunk = tokenizer.convert_tokens_to_string(tokens[i:i+max_tokens])
        chunks.append(chunk)
        token_counts.append(len(tokenizer.encode(chunk)))  # Count tokens of the current chunk

    print("Tokenization complete.")
    return chunks, token_counts

# Load the tokenizer and model from Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained("nsi319/legal-pegasus")
model = AutoModelForSeq2SeqLM.from_pretrained("nsi319/legal-pegasus")


class SummarizeRequest(BaseModel):
    text: str

def chunk_text(text, max_length=1024):
    """Split the text into manageable parts for the model to handle."""
    words = text.split()
    current_chunk = ""
    chunks = []

    for word in words:
        if len(tokenizer.encode(current_chunk + word)) < max_length:
            current_chunk += word + ' '
        else:
            chunks.append(current_chunk.strip())
            current_chunk = word + ' '
    chunks.append(current_chunk.strip())  # Add the last chunk
    return chunks

def summarize_legal_text(text):
    """Generate summaries for each chunk and combine them."""
    chunks = chunk_text(text, max_length=900)  # A bit less than 1024 to be safe
    all_summaries = []

    for chunk in chunks:
        inputs = tokenizer.encode(chunk, return_tensors='pt', max_length=1024, truncation=True)
        summary_ids = model.generate(
            inputs,
            num_beams=5,
            no_repeat_ngram_size=3,
            length_penalty=1.0,
            min_length=150,
            max_length=300,  # You can adjust this based on your needs
            early_stopping=True
        )
        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        all_summaries.append(summary)

    return " ".join(all_summaries)

@app.post("/summarize")
async def summarize_text(request: SummarizeRequest):
    try:
        summarized_text = summarize_legal_text(request.text)
        return JSONResponse(content={"summary": summarized_text})
    except Exception as e:
        print(f"Error during summarization: {e}")
        raise HTTPException(status_code=500, detail=str(e))


if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)