Create app.py

app.py

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+from datasets import load_dataset
+from transformers import pipeline
+import soundfile as sf
+import torch
+import gradio as gr
+import numpy as np
+
+def predict_image(image):
+  pipe = pipeline("image-classification", model="google/vit-base-patch16-224")
+  ClassifedImage=pipe(image)
+
+  result=ClassifedImage[0]['label']
+  return result
+
+def translate_to_arabic(text):
+  pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ar")
+  result=pipe(text , max_length=100)
+  return result[0]['translation_text']
+
+def text_to_speech(text):
+  pipe = pipeline("text-to-speech", model="MBZUAI/speecht5_tts_clartts_ar")
+  embedding_dataset=load_dataset("herwoww/arabic_xvector_embeddings" , split="validation")
+  speaker_embedding=torch.tensor(embedding_dataset[100]['speaker_embeddings']).unsqueeze(0)
+  speech=pipe(text , forward_params={'speaker_embeddings':speaker_embedding})
+
+  return (speech['sampling_rate'],np.array(speech['audio'], dtype=np.float32))
+
+from PIL import Image
+with gr.Blocks() as app:
+  gr.Markdown("Image Classification, Arabic Translation, TTS")
+
+  with gr.Row():
+    with gr.Column():
+      image_input=gr.Image(type="pil",label="Upload the Image to classify it" )
+      classify_image=gr.Button("Classify the Image")
+      pred=gr.Textbox(label="Classifcation Result")
+
+    classify_image.click(fn=predict_image , inputs=image_input , outputs=pred)
+
+  with gr.Row():
+    translated_output=gr.Textbox(label="Translated Text")
+    translate_btn=gr.Button("Translate to Arabic")
+
+    translate_btn.click(fn=translate_to_arabic , inputs=pred , outputs=translated_output)
+
+  with gr.Row():
+    tts_btn=gr.Button("Convert to Speech")
+    audio_output=gr.Audio(label="Audio Output")
+
+    tts_btn.click(fn=text_to_speech , inputs=translated_output , outputs=audio_output)
+
+app.launch()