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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Twitter_Analysis.csv filter=lfs diff=lfs merge=lfs -text

Bi-LSTM-Model-1.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:40d9ea929f9fd7c72e8c53eaabef8e0a53774336d85f716d0e704dadfca1588b
+size 17581304

CNN-Model-1.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ed6d529a612c98a14a56f52757fc54a8a8846e04d3120331b5d58ce8d80870f4
+size 16450800

Twitter_Analysis.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3d15543efca55929f5f27cf89791ee94974e28f4b9f0b6c3b8bbd0d548c17e8e
+size 82375676

app.py

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+import gradio as gr
+import tweepy
+import joblib
+import torch
+from transformers import BertTokenizer
+
+# Load the tokenizer and the model
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+joblib.dump([model, bert], 'c2_new_models2_weights.pt')
+
+def predict_fake_news(text):
+    # Tokenize and encode sequences
+    inputs = tokenizer.encode_plus(
+        text,
+        max_length=60,
+        pad_to_max_length=True,
+        truncation=True,
+        return_tensors="pt"
+    )
+
+    input_ids = inputs['input_ids']
+    attention_mask = inputs['attention_mask']
+
+    # Make prediction
+    model.eval()  # Ensure the model is in evaluation mode
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask)
+
+    # Access the logits directly from the outputs Tensor
+    logits = outputs[0]  # Assuming logits are the first element in the output tuple
+
+    # Get the prediction using argmax
+    prediction = torch.argmax(logits).item()
+
+    # Map prediction to label
+    label_map = {0: 'Real', 1: 'Fake'}
+    return label_map[prediction]
+
+
+
+# Define a function to update on Twitter
+def update_on_Twitter(tweet_text, prediction):
+    CONSUMER_KEY = "q76xzfaSG7jL4unpvaNuPM5Ms"
+    CONSUMER_SECRET = "7h2JCH9fveW3srWarhCmwLbr8rTtVeJ04Qo3q65VItX2L4eFs1"
+    ACCESS_TOKEN = "1636314191198932992-VesD9DTEnagO7fQdCiu5Fh6vuFLbw1"
+    ACCESS_TOKEN_SECRET = "DcTCYDGba8UWlbMEpDvmTMZuVI2XAip7Tu8QgLTrC12AW"
+    BAERER_TOKEN = "AAAAAAAAAAAAAAAAAAAAAPJjnwEAAAAA3DnqW09w51Oufv8UCReOPQLPUtA%3Dz9vzO4DXVbXRU63RZB3TzbCrBc0saEnQZ49GMmGkDqKVu30qwC"
+
+    # Authenticate to Twitter
+    auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
+    auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
+
+    # Create an API object
+    api = tweepy.API(auth)
+
+    # Create a Client object
+    client = tweepy.Client(
+      BAERER_TOKEN,
+      CONSUMER_KEY,
+      CONSUMER_SECRET,
+      ACCESS_TOKEN,
+      ACCESS_TOKEN_SECRET,
+      wait_on_rate_limit=True
+    )
+
+    postText = f"The news: {tweet_text} is {prediction}"
+
+    try:
+        api.verify_credentials()
+        print("Authentication OK")
+        client.create_tweet(text=postText)
+        return f'<a href="https://twitter.com/CANNBot" target="_blank">Detect Fake News on Twitter Bot Account</a>'
+    except Exception as e:
+        print(e)
+        return f'Error: {e}'
+
+# Use Gradio Blocks to create a more flexible interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Fake News Detection")
+    text_input = gr.Textbox(placeholder="Enter a news Tweet here...", label="News Tweet")
+    text_output = gr.Textbox(label="Prediction")
+    link_output = gr.HTML(label="Twitter Bot Account")
+
+    # Button to get prediction
+    gr.Button("Predict").click(predict_fake_news, inputs=text_input, outputs=text_output)
+
+    # Button to generate a Gradio link
+    gr.Button("Detect on Twitter").click(update_on_Twitter, inputs=[text_input, text_output], outputs=link_output)
+
+# Launch the interface
+demo.launch()

c2_new_models2_weights.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:927a11b303b096af3e7ec34225a7efbc52f70e3c01490698ffba101378983185
+size 439590589

requirements.txt

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+numpy
+pandas
+matplotlib.pyplot
+pycaret
+transformers
+sklearn
+torch
+tensorflow
+joblib
+BertTokenizer
+sklearn.model_selection
+train_test_split
+tensorflow.keras.preprocessing.text
+Tokenizer
+tensorflow.keras.preprocessing.sequence
+pad_sequences
+sklearn.metrics
+accuracy_score
+f1_score
+recall_score
+precision_score
+confusion_matrix
+seaborn
+Sequential
+Embedding
+Dense
+Dropout
+Conv1D
+MaxPooling1D
+Flatten
+gradio
+tweepy