Create README.md

README.md

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-title: Project2
-emoji: 😻
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 5.18.0
-app_file: app.py
-pinned: false
 license: apache-2.0
+title: 'HeadlinesGen:Partmone '
+sdk: gradio
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Final Project: Part One
+Headlines Generation Project:
+This project is all about making it easier to come up with the perfect headline. The goal is to
+create an app that helps generate clear, engaging, and relevant headlines for articles in both
+Arabic and English. Instead of struggling to summarize an article, users can get an instant, wellmatched headline with just a click. The app is designed to be simple and efficient, allowing
+users to choose their preferred language and quickly get the best possible headline with
+generated audio without the hassle.
+Pipelines:
+Headlines Generator:
+• The app generates headlines based on the provided article text in English using the
+Michau/t5-base-en-generate-headline model.
+• If the input is in English, the headline is generated directly in English.
+Translation:
+• If the input is in Arabic, it is first translated to English using Helsinki-NLP/opus-mt-aren, then the headline is generated in English, and finally, it is translated back to Arabic
+using Helsinki-NLP/opus-mt-en-ar.
+• This ensures the app provides a headline in the desired language, whether the input
+is in English or Arabic.
+Text-to-Speech:
+• The app supports text-to-speech conversion for both English and Arabic.
+• Arabic: To convert the Arabic headline to speech using facebook/mms-tts-ara.
+• English: To convert the English headline to speech using microsoft/speecht5_tts.
+How to use the interface:
+The app uses Gradio for building a simple interface where users can select the language, input
+text, and receive the generated headline with an audio.
+Select Language: Choose between Arabic or English.
+Enter Article Text: Type or paste the article text from which you want to generate a
+headline.
+Get Headline: Once the button is clicked, a headline will be generated.
+Get Audio: An audio of the headline also will be generated.
+Example Input:
+Select Language: English
+Text/Article: " Greenhouse gas emissions, primarily carbon dioxide (CO2) and methane
+(CH4), are the main drivers of global climate change. Human activities, such as burning fossil
+fuels for energy, deforestation, and industrial processes, have significantly increased the
+concentration of these gases in the atmosphere. According to the Intergovernmental Panel
+on Climate Change (IPCC), CO2 levels have risen by over 50% since the pre-industrial era,
+contributing to rising global temperatures. "
+Expected Output (A headline in English): Global Warming - The Main Driver of Global
+Climate Change.
+An audio that read the headline.
+Why We Chose These Models:
+Headlines Generator:
+We selected Michau/t5-base-en-generate-headline for headline generation because it has
+been trained on a collection of 500,000 articles with corresponding headings, making it wellsuited for this task. The model is specifically designed to generate concise and relevant
+headlines from article text.
+Translation:
+We use the Helsinki-NLP/opus-mt-ar-en and Helsinki-NLP/opus-mt-en-ar models to
+translate back and forth between Arabic and English. Since the main headline generation
+model does not support Arabic, we had to find a way to generate headlines in Arabic.
+To achieve this, we first translate Arabic input into English so that the headline generation
+model can process it and produce a proper English headline. Since we want the headline to
+match the article's original language, we then translate the generated English headline back
+into Arabic before outputting it.
+Because translation accuracy is crucial for maintaining the meaning of the headlines, we
+carefully selected our translation models based on popularity and high download rates,
+ensuring they are optimized specifically for Arabic and English.
+Text-to-speech:
+Arabic Text-to-Speech: We selected facebook/mms-tts-ara for Arabic text-to-speech after
+evaluating multiple models. This model provided the best pronunciation and clarity, ensuring
+high-quality Arabic speech.
+English Text-to-Speech: We chose microsoft/speecht5_tts for English text-to-speech due to
+its superior pronunciation and clarity. As a Microsoft-developed model, it leverages state-ofthe-art technology to produce natural and highly reliable speech, making it an ideal choice
+for our project.
+Special Measures Taken to Support the Arabic Language:
+Since our chosen headline generation model Michau/t5-base-en-generate-headline only
+supports English, we implemented a translation pipeline to ensure that Arabic inputs can be
+processed effectively.
+To achieve this, we use the Helsinki-NLP/opus-mt-ar-en model to translate Arabic text into
+English before generating a headline. Once the headline is created in English, we then use
+the Helsinki-NLP/opus-mt-en-ar model to translate it back into Arabic