Create app.py

app.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import gradio as gr
+import re
+
+# Check if GPU is available and use it if possible
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Load the model and tokenizer
+mme_model_name = 'sperkins2116/ConfliBERT-BC-MMEs'
+mme_model = AutoModelForSequenceClassification.from_pretrained(mme_model_name).to(device)
+mme_tokenizer = AutoTokenizer.from_pretrained(mme_model_name)
+
+# Define the class names for text classification
+class_names = ['Negative', 'Positive']
+
+def handle_error_message(e, default_limit=512):
+    error_message = str(e)
+    pattern = re.compile(r"The size of tensor a \((\d+)\) must match the size of tensor b \((\d+)\)")
+    match = pattern.search(error_message)
+    if match:
+        number_1, number_2 = match.groups()
+        return f"<span style='color: red; font-weight: bold;'>Error: Text Input is over limit where inserted text size {number_1} is larger than model limits of {number_2}</span>"
+    return f"<span style='color: red; font-weight: bold;'>Error: Text Input is over limit where inserted text size is larger than model limits of {default_limit}</span>"
+
+def mme_classification(text):
+    try:
+        inputs = mme_tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(device)
+        with torch.no_grad():
+            outputs = mme_model(**inputs)
+        logits = outputs.logits.squeeze().tolist()
+        predicted_class = torch.argmax(outputs.logits, dim=1).item()
+        confidence = torch.softmax(outputs.logits, dim=1).max().item() * 100
+
+        if predicted_class == 1:  # Positive class
+            result = f"<span style='color: green; font-weight: bold;'>Positive: The text contains evidence of a multinational military exercise. (Confidence: {confidence:.2f}%)</span>"
+        else:  # Negative class
+            result = f"<span style='color: red; font-weight: bold;'>Negative: The text does not contain evidence of a multinational military exercise. (Confidence: {confidence:.2f}%)</span>"
+        return result
+    except Exception as e:
+        return handle_error_message(e)
+
+# Define the Gradio interface
+def chatbot(text):
+    return mme_classification(text)
+
+css = """
+body {
+    background-color: #f0f8ff;
+    font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif;
+    color: black; /* Ensure text is visible in dark mode */
+}
+
+h1 {
+    color: #2e8b57;
+    text-align: center;
+    font-size: 2em;
+}
+
+h2 {
+    color: #ff8c00;
+    text-align: center;
+    font-size: 1.5em;
+}
+
+.gradio-container {
+    max-width: 100%;
+    margin: 10px auto;
+    padding: 10px;
+    background-color: #ffffff;
+    border-radius: 10px;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+}
+
+.gr-input, .gr-output {
+    background-color: #ffffff;
+    border: 1px solid #ddd;
+    border-radius: 5px;
+    padding: 10px;
+    font-size: 1em;
+    color: black; /* Ensure text is visible in dark mode */
+}
+
+.gr-title {
+    font-size: 1.5em;
+    font-weight: bold;
+    color: #2e8b57;
+    margin-bottom: 10px;
+    text-align: center;
+}
+
+.gr-description {
+    font-size: 1.2em;
+    color: #ff8c00;
+    margin-bottom: 10px;
+    text-align: center;
+}
+
+.header {
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    padding: 10px;
+    flex-wrap: wrap;
+}
+
+.header-title-center a {
+    font-size: 4em;  /* Increased font size */
+    font-weight: bold;  /* Made text bold */
+    color: darkorange;  /* Darker orange color */
+    text-align: center;
+    display: block;
+}
+
+.gr-button {
+    background-color: #ff8c00;
+    color: white;
+    border: none;
+    padding: 10px 20px;
+    font-size: 1em;
+    border-radius: 5px;
+    cursor: pointer;
+}
+
+.gr-button:hover {
+    background-color: #ff4500;
+}
+
+.footer {
+    text-align: center;
+    margin-top: 10px;
+    font-size: 0.9em;  /* Updated font size */
+    color: black; /* Ensure text is visible in dark mode */
+    width: 100%;
+}
+
+.footer a {
+    color: #2e8b57;
+    font-weight: bold;
+    text-decoration: none;
+}
+
+.footer a:hover {
+    text-decoration: underline;
+}
+
+.footer .inline {
+    display: inline;
+    color: black; /* Ensure text is visible in dark mode */
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Row(elem_id="header"):
+        gr.Markdown("<div class='header-title-center'><a href='https://eventdata.utdallas.edu/conflibert/'>ConfliBERT-MME</a></div>", elem_id="header-title-center")
+    
+    gr.Markdown("<span style='color: black;'>Provide the text for MME Classification.</span>")
+    
+    text_input = gr.Textbox(lines=5, placeholder="Enter the text here...", label="Text")
+    
+    output = gr.HTML(label="Output")
+    
+    submit_button = gr.Button("Submit", elem_id="gr-button")
+    submit_button.click(fn=chatbot, inputs=text_input, outputs=output)
+    
+    gr.Markdown("<div class='footer'><a href='https://eventdata.utdallas.edu/'>UTD Event Data</a> | <a href='https://www.utdallas.edu/'>University of Texas at Dallas</a> | <a href='https://www.wvu.edu/'>West Virginia University</a></div>")
+    gr.Markdown("<div class='footer'><span class='inline'>Developed By: <a href='https://www.linkedin.com/in/sultan-alsarra-phd-56977a63/' target='_blank'>Sultan Alsarra</a> | Finetuned By: Spencer Perkins</span></div>")
+
+demo.launch(share=True)