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| import os | |
| import gradio as gr | |
| from transformers import pipeline | |
| import spacy | |
| import subprocess | |
| import nltk | |
| from nltk.corpus import wordnet | |
| from spellchecker import SpellChecker | |
| from fastapi import FastAPI, HTTPException | |
| from pydantic import BaseModel | |
| import uvicorn | |
| import uuid # To generate unique link IDs | |
| # Initialize FastAPI app | |
| api_app = FastAPI() | |
| # Initialize the English text classification pipeline for AI detection | |
| pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta") | |
| # Initialize the spell checker | |
| spell = SpellChecker() | |
| # Ensure necessary NLTK data is downloaded | |
| nltk.download('wordnet') | |
| nltk.download('omw-1.4') | |
| # Ensure the SpaCy model is installed | |
| try: | |
| nlp = spacy.load("en_core_web_sm") | |
| except OSError: | |
| subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"]) | |
| nlp = spacy.load("en_core_web_sm") | |
| # Generate temporary link storage (could be database or in-memory store) | |
| temporary_links = {} | |
| # Define request models for FastAPI | |
| class TextRequest(BaseModel): | |
| text: str | |
| # Function to predict the label and score for English text (AI Detection) | |
| def predict_en(text): | |
| res = pipeline_en(text)[0] | |
| return res['label'], res['score'] | |
| # Function to paraphrase and correct grammar with enhanced accuracy | |
| def paraphrase_and_correct(text): | |
| # Here should go all the paraphrasing and grammar correction logic. | |
| return text # For now just return the input | |
| # API Endpoint to create a new temporary link for Gradio interface | |
| async def generate_temporary_link(task: str): | |
| # Check if the task is either 'ai-detection' or 'paraphrase' | |
| if task not in ["ai-detection", "paraphrase"]: | |
| raise HTTPException(status_code=400, detail="Invalid task type.") | |
| # Create a unique link using UUID | |
| link_id = str(uuid.uuid4()) | |
| # Set up Gradio interface based on task | |
| if task == "ai-detection": | |
| with gr.Blocks() as demo: | |
| t1 = gr.Textbox(lines=5, label='Text') | |
| button1 = gr.Button("🤖 Predict!") | |
| label1 = gr.Textbox(lines=1, label='Predicted Label 🎃') | |
| score1 = gr.Textbox(lines=1, label='Prob') | |
| # Connect the prediction function to the button | |
| button1.click(fn=predict_en, inputs=t1, outputs=[label1, score1]) | |
| elif task == "paraphrase": | |
| with gr.Blocks() as demo: | |
| t2 = gr.Textbox(lines=5, label='Enter text for paraphrasing and grammar correction') | |
| button2 = gr.Button("🔄 Paraphrase and Correct") | |
| result2 = gr.Textbox(lines=10, label='Corrected Text', placeholder="The corrected text will appear here...") | |
| # Connect the paraphrasing and correction function to the button | |
| button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2) | |
| # Launch Gradio and get the link | |
| demo_url = demo.launch(share=True, prevent_thread_lock=True) | |
| # Save the generated link in memory (temporary) | |
| temporary_links[link_id] = {"task": task, "url": demo_url} | |
| # Return the link to the user | |
| return {"link_id": link_id, "url": demo_url} | |
| # API Endpoint to get the status or result via the generated link | |
| async def get_temporary_link(link_id: str): | |
| # Check if the link exists | |
| if link_id not in temporary_links: | |
| raise HTTPException(status_code=404, detail="Link not found.") | |
| # Retrieve the link details | |
| link_details = temporary_links[link_id] | |
| return {"link": link_details["url"]} | |
| # Run the FastAPI app | |
| if __name__ == "__main__": | |
| uvicorn.run(api_app, host="0.0.0.0", port=8000) | |