Create app.py

app.py

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+from transformers import pipeline
+from datasets import load_dataset
+import gradio as gr
+import torch
+from diffusers import DiffusionPipeline
+
+pipe_ar = pipeline('text-generation', framework='pt', model='akhooli/ap2023', tokenizer='akhooli/ap2023')
+pipe_en = pipeline("text-generation", model="ismaelfaro/gpt2-poems.en")
+pipe_image = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipe_translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ar-en")
+
+# Initialize text-to-speech models for Arabic and English
+# Arabic: text-to-speech
+synthesiser_arabic = pipeline("text-to-speech", model="MBZUAI/speecht5_tts_clartts_ar")
+embeddings_dataset_arabic = load_dataset("herwoww/arabic_xvector_embeddings", split="validation")
+speaker_embedding_arabic = torch.tensor(embeddings_dataset_arabic[105]["speaker_embeddings"]).unsqueeze(0)
+
+# English: text-to-speech
+synthesiser_english = pipeline("text-to-speech", model="microsoft/speecht5_tts")
+embeddings_dataset_english = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embedding_english = torch.tensor(embeddings_dataset_english[7306]["xvector"]).unsqueeze(0)
+
+# Generate poem based on language and convert it to audio and image
+def generate_poem(selected_language, text):
+    if selected_language == "English":
+        poem = generate_poem_english(text) #retrun the generated poem from the generate_poem_english function
+        sampling_rate, audio_data = text_to_speech_english(poem) #return the audio from the text_to_speech_english function
+        image = generate_image_from_poem(text) #return the image from the generate_image_from_poem function
+    elif selected_language == "Arabic":
+        poem = generate_poem_arabic(text) #retrun the generated poem from the generate_poem_arabic function
+        sampling_rate, audio_data = text_to_speech_arabic(poem) #return the audio from the text_to_speech_arabic function
+        translated_text = translate_arabic_to_english(text) #return the translated poem from arabic to englsih, using translate_arabic_to_english function
+        image = generate_image_from_poem(translated_text) #return the image from the generate_image_from_poem function
+
+    return poem, (sampling_rate, audio_data), image
+
+# Poem generation for Arabic
+def generate_poem_arabic(text):
+    generated_text = pipe_ar(text, do_sample=True, max_length=96, top_k=50, top_p=1.0, temperature=1.0, num_return_sequences=1,
+                              no_repeat_ngram_size = 3, return_full_text=True)[0]["generated_text"]
+    clean_text = generated_text.replace("-", "") #To get rid of the dashs generated by the model.
+    return clean_text
+
+# Poem generation for English
+def generate_poem_english(text):
+    generated_text = pipe_en(text, do_sample=True, max_length=50)[0]['generated_text']
+    clean_text = generated_text.replace("-", "")  # Remove dashes generated by the model
+    clean_text = clean_text.replace("\\n", " ")  # Replace newlines with a space
+    return clean_text
+
+# Text-to-speech conversion for Arabic
+def text_to_speech_arabic(text):
+    speech = synthesiser_arabic(text, forward_params={"speaker_embeddings": speaker_embedding_arabic})
+    audio_data = speech["audio"]
+    sampling_rate = speech["sampling_rate"]
+    return (sampling_rate, audio_data)
+
+# Text-to-speech conversion for English
+def text_to_speech_english(text):
+    speech = synthesiser_english(text, forward_params={"speaker_embeddings": speaker_embedding_english})
+    audio_data = speech["audio"]
+    sampling_rate = speech["sampling_rate"]
+    return (sampling_rate, audio_data)
+
+#Image Function
+def generate_image_from_poem(poem_text):
+    image = pipe_image(poem_text).images[0]
+    return image
+
+#Translation Function from Arabic to English
+def translate_arabic_to_english(text):
+    translated_text = pipe_translator(text)[0]['translation_text']
+    return translated_text
+
+custom_css = """
+body {
+    background-color: #f4f4f9;
+    color: #333;
+}
+.gradio-container {
+    border-radius: 10px;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    background-color: #fff;
+}
+label {
+    color: #4A90E2;
+    font-weight: bold;
+}
+
+input[type="text"],
+textarea {
+    border: 1px solid #4A90E2;
+}
+textarea {
+    height: 150px;
+}
+
+button {
+    background-color: #4A90E2;
+    color: #fff;
+    border-radius: 5px;
+    cursor: pointer;
+}
+button:hover {
+    background-color: #357ABD;
+}
+
+.dropdown {
+    border: 1px solid #4A90E2;
+    border-radius: 4px;
+}
+
+"""
+
+examples = [
+    #First parameter is for the dropdown menu, and the second parameter is for the starter of the poem
+    ["English", "The shining sun rises over the calm ocean"],
+    ["Arabic", "الورود تتفتح في الربيع"],
+    ["English", "The night sky is filled with stars and dreams"],
+    ["Arabic", "اشعة الشمس المشرقة"]
+]
+
+my_model = gr.Interface(
+    fn=generate_poem,  #The primary function that will recives the inputs (language and the starter of the poem)
+    inputs=[
+        gr.Dropdown(["English", "Arabic"], label="Select Language"), #Dropdown menu to select the language, either "English" or "Arabic" for the poem
+        gr.Textbox(label="Enter a sentence")], #Textbox where the user will input a sentence or phrase to generate the poem (starter of the peom)
+
+    outputs=[
+        gr.Textbox(label="Generated Poem", lines=10), # Textbox to display the generated poem
+        gr.Audio(label="Generated Audio", type="numpy"), #Audio output for the generated poem
+        gr.Image(label="Generated Image")], #Display an image generated from the starter of the peom
+
+    examples=examples,  #Predefined examples to guide the user how to use the interface
+    css=custom_css  #Applying CSS Custeom
+)
+my_model.launch()