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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/1C8ZWvJVZzTCngwpz788EpcT2lrbBWhYa
+
+### 📰 **Project Description: Fake News Generator & Detector using Generative AI and NLP**
+This project is an interactive AI-powered tool that enables users to both generate realistic-looking fake news articles and detect whether a given news text is fake or real using state-of-the-art natural language processing models. It highlights how language models can both create and combat misinformation.
+
+🧰 **The application is built using:**
+
+1. **Transformers Library (by Hugging Face)**:
+For loading and using pretrained models — GPT-2 for fake news generation and fine-tuned BERT for fake news detection.
+
+2. **Gradio:** To create a simple, interactive web-based user interface with tabbed sections for generation and detection.
+
+3. **Google Colab / Python:** For backend development, prototyping, and running the application in a cloud-based notebook environment.
+
+🎯 **Project Objectives**
+
+**1. Generate Fake News Text**
+
+Use GPT-2 to simulate fake news articles from user-provided prompts for for awareness and experimentation.
+
+**2. Detect Fake or Real News**
+
+Utilize a fine-tuned BERT model (Pulk17/Fake-News-Detection) to accurately classify news content as fake or real.
+
+**3. Provide an Interactive Interface**
+
+Use Gradio to build a user-friendly web interface.
+
+**4. Demonstrate Dual Use of AI in Misinformation**
+
+Showcase how AI can both create and detect fake news, promoting awareness and responsible AI usage.
+
+**Step 2: Import Required Modules**
+
+ Import libraries for model handling, tokenization, and interface building.
+"""
+
+# app.py
+
+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from transformers import 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)
+
+# Load fine-tuned BERT model for fake news detection
+bert_tokenizer = AutoTokenizer.from_pretrained("Pulk17/Fake-News-Detection")
+bert_model = AutoModelForSequenceClassification.from_pretrained(
+    "Pulk17/Fake-News-Detection"
+).to(device)
+
+# Function to generate fake news from a prompt
+def generate_fake_news(prompt):
+    inputs = gpt2_tokenizer.encode(prompt, return_tensors="pt").to(device)
+    outputs = gpt2_model.generate(
+        inputs,
+        max_length=200,
+        num_return_sequences=1,
+        no_repeat_ngram_size=2,
+        do_sample=True,
+        temperature=0.7,
+        top_k=50,
+        top_p=0.95,
+        early_stopping=True
+    )
+    generated_text = gpt2_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+# Function to detect if news is fake or real
+def detect_news(text):
+    inputs = bert_tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
+    with torch.no_grad():
+        outputs = bert_model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+    confidence = torch.softmax(logits, dim=1)[0][predicted_class].item()
+    label = "🟥 Fake News" if predicted_class == 0 else "🟩 Real News"
+    return f"{label} (Confidence: {confidence:.2f})"
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("## 📰 Fake News Generator & Detector (GPT-2 + BERT)")
+
+    with gr.Tab("🛠️ Generate Fake News"):
+        with gr.Row():
+            input_text = gr.Textbox(
+                label="Enter a News Headline or Prompt",
+                placeholder="e.g. Scientists discover a talking dolphin species near Japan...",
+                lines=2
+            )
+        generate_btn = gr.Button("Generate")
+        output_text = gr.Textbox(label="Generated News Article")
+        generate_btn.click(generate_fake_news, inputs=input_text, outputs=output_text)
+
+    with gr.Tab("🔍 Detect Fake or Real"):
+        with gr.Row():
+            detect_input = gr.Textbox(
+                label="Enter a News Article or Statement",
+                placeholder="Paste a paragraph to detect if it's fake or real...",
+                lines=5
+            )
+        detect_btn = gr.Button("Detect")
+        detect_output = gr.Textbox(label="Detection Result")
+        detect_btn.click(detect_news, inputs=detect_input, outputs=detect_output)
+
+# Launch the app
+demo.launch()
+
+

requirements.txt

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+torch
+transformers
+gradio