initial commit

app.py

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+import gradio as gr
+from setfit import SetFitModel
+
+# Load a pre-trained SetFit model (replace with your specific model)
+model = SetFitModel.from_pretrained("CundK/ePA-Classifier-Agreement")
+
+# Define the mapping of labels
+label_mapping = {0: "Ablehnung", 1: "Neutral", 2: "Befürwortung"}
+
+# Define the color mapping
+color_mapping = {
+    "Ablehnung": "red",
+    "Neutral": "yellow",
+    "Befürwortung": "green"
+}
+
+# Function to classify text and return the label with background color
+def classify_text(text):
+    # Get predictions from the model
+    predictions = model([text])
+    
+    # Convert the prediction tensor to an integer index
+    label_index = int(predictions[0].item())
+    
+    # Map the integer index to the corresponding label
+    label = label_mapping[label_index]
+    
+    # Return the label with the appropriate background color
+    return f'<div style="background-color: {color_mapping[label]}; padding: 10px; border-radius: 5px;">{label}</div>'
+
+# Create the Gradio interface using Blocks for custom layout
+with gr.Blocks() as interface:
+    gr.Markdown("# Text Classification with SetFit")
+    gr.Markdown("Enter a sentence and get it classified into 'Ablehnung', 'Neutral', or 'Befürwortung'.")
+    
+    # Input text box with multiline set to False to avoid Shift+Enter issue
+    text_input = gr.Textbox(lines=2, placeholder="Enter your text here...", multiline=False)
+    
+    # Submit button
+    submit_btn = gr.Button("Submit")
+    
+    # Placeholder for result with an initial message
+    result_output = gr.HTML(value="<div style='color:gray;'>The classification result will be displayed here.</div>")
+
+    # Connect the submit button to the classification function
+    submit_btn.click(fn=classify_text, inputs=text_input, outputs=result_output)
+
+    # Trigger the classification function when the user presses Enter in the text box
+    text_input.submit(fn=classify_text, inputs=text_input, outputs=result_output)
+
+# Launch the interface
+interface.launch()