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app.py

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+# -*- coding: utf-8 -*-
+"""Fake News Generator & Detector.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1k_GVU6WO9Mggr87RxntJFq2S1M6R4dI0
+
+# 📰 **Fake News Generator & Detector**
+
+This educational and awareness-focused Generative AI application explores the dual power of AI in creating and detecting fake news. It allows users to generate fake news headlines based on a selected category (e.g., politics, tech), a subject or entity (e.g., Apple, Government), and number of headlines. It also provides a detection system where users can input a news snippet to check whether it is likely to be fake or real using a fine-tuned BERT model.
+
+**The application is built using:**
+
+Transformers library
+(For GPT-2 based fake news generation and BERT-based fake news classification)
+
+Gradio
+(To create a clean, tabbed web interface for both generation and detection)
+
+Google Colab / Python
+(For backend development, model training/fine-tuning, and deployment)
+
+**This project aims to:**
+
+✨ Demonstrate the capabilities and risks of large language models
+
+🔍 Raise awareness about misinformation in media
+
+🧠 Promote responsible AI development through practical exploration
+
+This project showcases how AI can both contribute to and combat misinformation, serving as a tool for learning, experimentation, and ethics in AI."""
+
+# app.py
+
+import torch
+import torch.nn.functional as F
+from transformers import (
+    GPT2LMHeadModel,
+    GPT2Tokenizer,
+    AutoTokenizer,
+    AutoModelForSequenceClassification
+)
+import gradio as gr
+
+# Device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load GPT-2 for Fake News Generation
+gpt2_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+gpt2_model = GPT2LMHeadModel.from_pretrained("gpt2").to(device)
+gpt2_model.eval()
+
+# Headline generation function
+def generate_fake_headlines(category, subject, num_headlines=1, max_length=20):
+    headlines = []
+    for i in range(num_headlines):
+        prompt = f"Write a fake {category} news headline about {subject}:\n"
+        input_ids = gpt2_tokenizer.encode(prompt, return_tensors='pt').to(device)
+
+        output = gpt2_model.generate(
+            input_ids,
+            max_length=len(input_ids[0]) + max_length,
+            temperature=0.9,
+            top_p=0.9,
+            do_sample=True,
+            pad_token_id=gpt2_tokenizer.eos_token_id,
+            no_repeat_ngram_size=2,
+            early_stopping=True
+        )
+
+        generated_text = gpt2_tokenizer.decode(output[0], skip_special_tokens=True)
+        headline = generated_text.replace(prompt, "").strip().split("\n")[0]
+        headlines.append(headline)
+    return headlines
+
+# Gradio function wrapper
+def gradio_fake_news_generator(category, subject, num_headlines):
+    try:
+        num = int(num_headlines)
+        if num <= 0 or num > 10:
+            return "Please enter a number between 1 and 10."
+    except:
+        return "Invalid input for number of headlines."
+
+    headlines = generate_fake_headlines(category, subject, num)
+    return "\n".join([f"{i+1}. {h}" for i, h in enumerate(headlines)])
+
+# Load BERT model for fake news detection (no token required)
+bert_tokenizer = AutoTokenizer.from_pretrained("Pulk17/Fake-News-Detection")
+bert_model = AutoModelForSequenceClassification.from_pretrained("Pulk17/Fake-News-Detection").to(device)
+bert_model.eval()
+
+# Detection function
+def detect_fake_news(text):
+    inputs = bert_tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512).to(device)
+    outputs = bert_model(**inputs)
+    probs = F.softmax(outputs.logits, dim=1)
+    predicted_class = torch.argmax(probs, dim=1).item()
+    confidence = torch.max(probs).item()
+    label = "FAKE 🟥" if predicted_class == 0 else "REAL 🟩"
+    return f"Prediction: {label}  ({confidence*100:.2f}% confidence)"
+
+# Gradio interfaces
+generator_interface = gr.Interface(
+    fn=gradio_fake_news_generator,
+    inputs=[
+        gr.Textbox(label="News Category (e.g., politics, tech)"),
+        gr.Textbox(label="Main Subject / Entity (e.g., Apple, Government)"),
+        gr.Textbox(label="Number of Headlines (1-10)")
+    ],
+    outputs=gr.Textbox(label="Generated Fake News Headlines"),
+    title="📰 Fake News Generator",
+    description="Generate fake news headlines using GPT-2"
+)
+
+detector_interface = gr.Interface(
+    fn=detect_fake_news,
+    inputs=gr.Textbox(lines=5, label="Enter full news text"),
+    outputs=gr.Textbox(label="Fake News Detection Result"),
+    title="🔍 Fake News Detector",
+    description="Detect whether a news article is FAKE or REAL using BERT"
+)
+
+# Combine both interfaces
+gr.TabbedInterface([generator_interface, detector_interface],
+                   tab_names=["📰 Generator", "🔍 Detector"]).launch()
+