from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import gradio as gr
import pytesseract
from PIL import Image
import requests
from bs4 import BeautifulSoup

# Load model
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prediction function
def predict_job_post(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    prediction = torch.argmax(logits, dim=1).item()
    prob = torch.nn.functional.softmax(logits, dim=1)[prediction].item()

    if prediction == 1:
        return f"🟢 REAL Job (Confidence: {prob:.2f})\nReason: The post has positive and trustworthy language."
    else:
        return f"🔴 FAKE Job (Confidence: {prob:.2f})\nReason: The post uses negative or suspicious language patterns."

# OCR for image
def process_image(image):
    text = pytesseract.image_to_string(image)
    return predict_job_post(text)

# Scraper for URL
def process_url(url):
    try:
        response = requests.get(url, timeout=5)
        soup = BeautifulSoup(response.text, 'html.parser')
        text = soup.get_text(separator=' ', strip=True)
        return predict_job_post(text[:1000])
    except:
        return "❌ Error: Could not fetch or process the URL."

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 🕵‍♀ Fake Job Detector AI App")
    gr.Markdown("Input job description, image, or link to check if the job is real or fake.")

    with gr.Tab("Text"):
        text_input = gr.Textbox(lines=4, label="Enter Job Text")
        text_output = gr.Textbox(label="Result")
        text_button = gr.Button("Analyze Text")
        text_button.click(fn=predict_job_post, inputs=text_input, outputs=text_output)

    with gr.Tab("Image"):
        image_input = gr.Image(type="pil", label="Upload Image")
        image_output = gr.Textbox(label="Result")
        image_button = gr.Button("Analyze Image")
        image_button.click(fn=process_image, inputs=image_input, outputs=image_output)

    with gr.Tab("URL"):
        url_input = gr.Textbox(label="Paste Job Post URL")
        url_output = gr.Textbox(label="Result")
        url_button = gr.Button("Analyze URL")
        url_button.click(fn=process_url, inputs=url_input, outputs=url_output)

demo.launch()