Upload app.py

app.py

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+import numpy as np
+import os
+import pandas as pd
+import torch
+import matplotlib.pyplot as plt
+from transformers import XLMRobertaModel, XLMRobertaTokenizer
+import torch.nn as nn
+import gradio as gr
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from transformers import AutoTokenizer, AutoModelForTokenClassification, AutoModelForSequenceClassification
+# Load BERT model and tokenizer via HuggingFace Transformers
+bert = XLMRobertaModel.from_pretrained('castorini/afriberta_large')
+tokenizer = XLMRobertaTokenizer.from_pretrained('castorini/afriberta_large')
+# Define the model architecture
+class BERT_Arch(nn.Module):
+    def __init__(self, bert):
+        super(BERT_Arch, self).__init__()
+        self.bert = bert
+        self.dropout = nn.Dropout(0.1)          # Dropout layer
+        self.relu = nn.ReLU()                   # ReLU activation function
+        self.fc1 = nn.Linear(768, 512)          # Dense layer 1
+        self.fc2 = nn.Linear(512, 2)            # Dense layer 2 (Output layer)
+        self.softmax = nn.LogSoftmax(dim=1)     # Softmax activation function
+
+    def forward(self, sent_id, mask):           # Define the forward pass
+        cls_hs = self.bert(sent_id, attention_mask=mask)['pooler_output']
+        x = self.fc1(cls_hs)
+        x = self.relu(x)
+        x = self.dropout(x)
+        x = self.fc2(x)                         # Output layer
+        x = self.softmax(x)                     # Apply softmax activation
+        return x
+
+# Load the model and set it to evaluation mode
+model = BERT_Arch(bert)
+fake_news_model_path = "Fake_model.pt"
+fake_news_model = torch.load(fake_news_model_path, map_location=torch.device('cpu'))
+fake_news_model.eval()
+
+# Function to detect fake news
+def detect_fake_news(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+    with torch.no_grad():
+        outputs = fake_news_model(inputs['input_ids'], inputs['attention_mask'])
+        label = torch.argmax(outputs, dim=1).item()
+    fake_news_result = "Fake" if label == 1 else "Not Fake"
+    return fake_news_result
+
+# Function to handle post logic
+def post_text(text, fake_news_result):
+    if fake_news_result == "Fake":
+        return "Your message contains Fake News and cannot be posted.", ""
+    else:
+        return "The text is safe to post.", text
+
+# Gradio Interface
+interface = gr.Blocks()
+with interface:
+    gr.Markdown("## Fake News Detection")
+    with gr.Row():
+        text_input = gr.Textbox(label="Enter Text", lines=5)
+    with gr.Row():
+        detect_fake_button = gr.Button("Detect Fake News")
+    with gr.Row():
+        fake_news_result_box = gr.Textbox(label="Fake News Detection Result", interactive=False)
+    with gr.Row():
+        post_button = gr.Button("Post Text")
+    with gr.Row():
+        post_result_box = gr.Textbox(label="Posting Status", interactive=False)
+        posted_text_box = gr.Textbox(label="Posted Text", interactive=False)
+
+    detect_fake_button.click(
+        fn=detect_fake_news,
+        inputs=text_input,
+        outputs=fake_news_result_box,
+    )
+
+    post_button.click(
+        fn=post_text,
+        inputs=[text_input, fake_news_result_box],
+        outputs=[post_result_box, posted_text_box],
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()