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| import streamlit as st | |
| from transformers import pipeline | |
| rewriter = pipeline("text2text-generation", model='google/flan-t5-large') | |
| classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") | |
| translator = pipeline("translation", model="Helsinki-NLP/opus-mt-es-en") | |
| translator_arabe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ar") | |
| translator_french = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr") | |
| tone_classifier = pipeline("zero-shot-classification", model="roberta-large-mnli") | |
| text1 = st.text_area('Enter your text for the first exercise') | |
| text2 = st.text_area('Enter your text for the second exercise') | |
| if text1: | |
| text_translate = translator(text1)[0]['translation_text'] | |
| prompt = f""" | |
| Rewrite the following technical text in common language\n | |
| Texto: "{text_translate}" | |
| """ | |
| out = rewriter(prompt,max_length=400, num_return_sequences=1, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.1) | |
| st.write("Text in Common Lenguage:") | |
| st.json(out) | |
| text_response = out[0]['generated_text'] | |
| prompt = f""" | |
| Identify the main theme of the text\n | |
| Texto: "{text_response}" | |
| """ | |
| theme_labels = ["biology", "genetics", "technology", "engineering", "medicine"] | |
| theme_result = classifier(text_response, candidate_labels=theme_labels) | |
| st.write("Main Theme:") | |
| st.json(theme_result) | |
| out_arabe = translator_arabe(text_response) | |
| st.write("Translation to Arabic:") | |
| st.json(out_arabe) | |
| out_french = translator_french(text_response) | |
| st.write("Translation to French:") | |
| st.json(out_french) | |
| prompt = f""" | |
| Identify the tone in which the text is written\n | |
| Text: "{text_response}" | |
| """ | |
| tone_labels = ["neutral", "formal", "informal"] | |
| tone_result = tone_classifier(text_response, candidate_labels=tone_labels) | |
| st.write("Tone:") | |
| st.json(tone_result) | |
| if text2: | |
| text_translate = translator(text2)[0]['translation_text'] | |
| prompt = f""" | |
| From this information, infer two possible conclusions explained in a way that a 10-year-old child can understand.\n | |
| Text: "{text_translate}" | |
| """ | |
| out = rewriter(prompt,max_length=400, num_return_sequences=1, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.1) | |
| st.write("Conclusions Infered:") | |
| st.json(out) | |
| prompt = f""" | |
| Suggests two areas of future research related to these results. | |
| Text: "{out[0]['generated_text']}" | |
| """ | |
| out = rewriter(prompt,max_length=400, num_return_sequences=1, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.1) | |
| st.write("Areas of future research:") | |
| st.json(out) | |