Added app.py and requirements.txt for the Image Captioning project

app.py

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+# Import necessary libraries and modules
+from transformers import BlipProcessor, BlipForConditionalGeneration, MBartForConditionalGeneration, MBart50Tokenizer
+from gtts import gTTS
+from PIL import Image
+import gradio as gr
+
+# Pipeline Component 1: Image Captioning Model
+class ImageToText:
+    def __init__(self):
+        """Initializes the BLIP model for image captioning."""
+        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+        self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+        print("BLIP Image Captioning Model Loaded")
+
+    def generate_caption(self, img):
+        """Generates a caption for the given image."""
+        inputs = self.processor(images=img, return_tensors="pt")
+        generated_ids = self.model.generate(**inputs)
+        caption = self.processor.decode(generated_ids[0], skip_special_tokens=True)
+        return caption
+
+# Pipeline Component 2: Arabic Translation Model (mBART)
+class ArabicTranslator:
+    def __init__(self):
+        """Initializes the mBART model for English to Arabic translation."""
+        self.tokenizer = MBart50Tokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+        self.model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+        print("mBART Arabic Translation Model Loaded")
+
+    def translate(self, text):
+        """Translates the given English text to Arabic."""
+        inputs = self.tokenizer(text, return_tensors="pt", src_lang="en_XX")
+        translated = self.model.generate(inputs["input_ids"], forced_bos_token_id=self.tokenizer.lang_code_to_id["ar_AR"])
+        translated_text = self.tokenizer.batch_decode(translated, skip_special_tokens=True)[0]
+        return translated_text
+
+# Pipeline Component 3: Text-to-Speech Model (gTTS)
+class TextToSpeech:
+    def __init__(self, lang='ar'):
+        """Initializes the Text-to-Speech system for Arabic."""
+        self.lang = lang
+
+    def generate_audio(self, text):
+        """Generates audio from the given Arabic text."""
+        tts = gTTS(text=text, lang=self.lang, slow=False)
+        audio_file_path = 'output.mp3'
+        tts.save(audio_file_path)
+        return audio_file_path
+
+# Main Pipeline Integration
+class ImageToArabicSpeechPipeline:
+    def __init__(self):
+        """Initializes all pipeline components."""
+        self.caption_model = ImageToText()
+        self.translation_model = ArabicTranslator()
+        self.tts_model = TextToSpeech()
+
+    def process_image(self, img):
+        """Processes the image, generates a caption, translates it to Arabic, and converts it to speech."""
+        caption = self.caption_model.generate_caption(img)
+        translated_text = self.translation_model.translate(caption)
+        audio_file = self.tts_model.generate_audio(translated_text)
+        return caption, translated_text, audio_file
+
+# Gradio Interface Setup
+def demo(image):
+    """Function to be used in Gradio for processing the image and returning caption, translation, and audio."""
+    img = Image.open(image)
+    pipeline = ImageToArabicSpeechPipeline()
+    caption, translated_text, audio_file = pipeline.process_image(img)
+    return caption, translated_text, audio_file
+
+# Define Gradio Interface
+iface = gr.Interface(
+    fn=demo,
+    inputs=gr.Image(type="filepath"),
+    outputs=[gr.Textbox(label="Caption"), gr.Textbox(label="Translated Text"), gr.Audio(label="Generated Speech")]
+)
+
+# Launch the Gradio Interface
+iface.launch()

requirements.txt

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+gtts
+gradio
+transformers