Create app.py

app.py

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+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()