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import gradio as gr
from transformers import pipeline
#pipelines
qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
classification_pipeline = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
translation_pipeline = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
topic_classification_pipeline = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english") # Fine-tuned model for topic classification
summarization_pipeline = pipeline("summarization", model="facebook/bart-large-cnn")
#functions
def answer_question(context, question):
return qa_pipeline(question=question, context=context)["answer"]
def classify_text(text, labels):
labels = labels.split(",")
results = classification_pipeline(text, candidate_labels=labels)
return {label: float(f"{prob:.4f}") for label, prob in zip(results["labels"], results["scores"])}
def translate_text(text):
return translation_pipeline(text)[0]['translation_text'] if text else "No translation available"
def classify_topic(text):
results = topic_classification_pipeline(text)
return ", ".join([f"{result['label']}: {result['score']:.4f}" for result in results])
def summarize_text(text):
result = summarization_pipeline(text, max_length=60)
return result[0]['summary_text'] if result else "No summary available"
def multi_model_interaction(text):
summary = summarize_text(text)
translated_summary = translate_text(summary)
return {
"Summary (English)": summary,
"Summary (French)": translated_summary,
}
with gr.Blocks() as demo:
with gr.Tab("Single Models"):
with gr.Column():
gr.Markdown("### Question Answering")
context = gr.Textbox(label="Context")
question = gr.Textbox(label="Question")
answer_output = gr.Text(label="Answer")
gr.Button("Answer").click(answer_question, inputs=[context, question], outputs=answer_output)
with gr.Column():
gr.Markdown("### Zero-Shot Classification")
text_zsc = gr.Textbox(label="Text")
labels = gr.Textbox(label="Labels (comma separated)")
classification_result = gr.JSON(label="Classification Results")
gr.Button("Classify").click(classify_text, inputs=[text_zsc, labels], outputs=classification_result)
with gr.Column():
gr.Markdown("### Translation")
text_to_translate = gr.Textbox(label="Text")
translated_text = gr.Text(label="Translated Text")
gr.Button("Translate").click(translate_text, inputs=[text_to_translate], outputs=translated_text)
with gr.Column():
gr.Markdown("### Sentiment Analysis")
text_for_sentiment = gr.Textbox(label="Text for Sentiment Analysis")
sentiment_result = gr.Text(label="Sentiment")
gr.Button("Classify Sentiment").click(classify_topic, inputs=[text_for_sentiment], outputs=sentiment_result)
with gr.Column():
gr.Markdown("### Summarization")
text_to_summarize = gr.Textbox(label="Text")
summary = gr.Text(label="Summary")
gr.Button("Summarize").click(summarize_text, inputs=[text_to_summarize], outputs=summary)
with gr.Tab("Multi-Model"):
gr.Markdown("### Multi-Model")
input_text = gr.Textbox(label="Enter Text for Multi-Model Analysis")
multi_output = gr.Text(label="Results")
gr.Button("Process").click(multi_model_interaction, inputs=[input_text], outputs=multi_output)
demo.launch(share=True, debug=True) |