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| # import gradio as gr | |
| # gr.load("models/karthikvarunn/skar_propaganda_v1.1").launch() | |
| # import gradio as gr | |
| # from transformers import pipeline | |
| # # Load the model correctly by specifying the repo id in the correct format | |
| # classifier = pipeline("zero-shot-classification", model="karthikvarunn/skar_propaganda_v1.1") | |
| # def format_output(result): | |
| # # Unpack the result | |
| # sequence = result['sequence'] | |
| # labels = result['labels'] | |
| # scores = result['scores'] | |
| # # Create a formatted string or list to display results clearly | |
| # output_lines = [] | |
| # for label, score in zip(labels, scores): | |
| # # Strip any leading/trailing whitespace from the label | |
| # label = label.strip() | |
| # output_lines.append(f"{label}: {score:.2f}") # Formatting score to two decimal places | |
| # return "\n".join(output_lines) | |
| # def predict(text, classes): | |
| # labels = [label.strip() for label in classes.split(",")] # Ensure labels are properly stripped of whitespace | |
| # result = classifier(text, labels) | |
| # formatted_result = format_output(result) | |
| # return formatted_result | |
| # demo = gr.Interface( | |
| # fn=predict, | |
| # inputs=[ | |
| # gr.Textbox(lines=2, placeholder="Type something here...", value="This miracle drug can save lives!"), | |
| # gr.Textbox(lines=2, placeholder="Possible labels, comma-separated", value="Doubt, Loaded Language, Jingoism, Hyperbole, no_propaganda") | |
| # ], | |
| # outputs=gr.Textbox(label="Formatted Output"), | |
| # title="Text Classification", | |
| # description="Enter text and labels to classify the text." | |
| # ) | |
| # demo.launch() | |
| import gradio as gr | |
| from transformers import pipeline | |
| # Initialize the zero-shot classification pipeline with your model from Hugging Face | |
| classifier = pipeline("zero-shot-classification", model="karthikvarunn/skar_propaganda_v1.1") | |
| def predict(text, classes): | |
| # Split the comma-separated labels provided by the user and strip whitespace | |
| labels = [label.strip() for label in classes.split(",")] | |
| # Use the classifier to get predictions | |
| result = classifier(text, labels) | |
| # Convert labels and scores to a dictionary for the Gradio Label component | |
| label_scores = {label: score for label, score in zip(result['labels'], result['scores'])} | |
| return label_scores | |
| # Create the Gradio interface | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.Textbox(lines=2, placeholder="Type a sentence to find the propaganda technique...", value="This miracle drug can save lives!"), | |
| gr.Textbox(lines=2, placeholder="Possible labels, comma-separated", value="Doubt, Loaded Language, Jingoism, Hyperbole, no_propaganda") | |
| ], | |
| outputs=gr.Label(num_top_classes=None), # Automatically render scores as a bar chart | |
| title="Zero-shot Classification for propaganda techniques", | |
| description="Enter text and labels to classify the text." | |
| ) | |
| # Launch the Gradio app | |
| demo.launch() | |