Create app.py

app.py

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+## app.py:
+import torch
+import gradio as gr
+from diffusers import StableDiffusionPipeline
+import requests
+from io import BytesIO
+import os
+from PIL import Image
+
+
+
+def translate_text(text, target_language='en'):
+    API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-ar-en"
+    headers = {"Authorization": f"Bearer {os.getenv('API_TOKEN')}"}
+    response = requests.post(API_URL, headers=headers, json=text)
+
+    if response.status_code == 200:
+        return response.json()[0]['translation_text']
+
+    else:
+        print("Failed to translate text:", response.text)
+        return text  # Return the original text if translation fails
+
+# Function to post data to an API and return response
+def query(payload, API_URL, headers):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.content
+
+# Function to generate images based on prompts using the Hugging Face API
+def generate_image(prompt, model_choice, translate=False):
+    if translate:
+        prompt = translate_text(prompt, target_language='en')  # Assuming you want to translate to English
+    model_urls = {
+        "Stable Diffusion v1.5": "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5",
+
+    }
+    API_URL = model_urls[model_choice]
+
+    headers = {"Authorization": f"Bearer {os.getenv('API_TOKEN')}"}
+    payload = {"inputs": prompt}
+    data = query(payload, API_URL, headers)
+    try:
+        # Load the image from byte data
+        image = Image.open(BytesIO(data))
+        # Resize the image
+        image = image.resize((400, 400))
+        # Convert the image object back to bytes for Gradio output
+        buf = BytesIO()
+        image.save(buf, format='PNG')
+        buf.seek(0)
+        return image
+
+    except Exception as e:
+        print("Error processing the image:", e)
+        return None  # Return None or an appropriate error message/image
+
+# Set up environment variable correctly
+API_TOKEN = os.getenv("API_TOKEN")
+
+# Styling with custom CSS
+css = """
+body {background-color: #f0f2f5;}
+.gradio-app {background-color: #ffffff; border-radius: 12px; box-shadow: 0 0 12px rgba(0,0,0,0.1);}
+button {color: white; background-color: #106BA3; border: none; border-radius: 5px;}
+"""
+
+# Define interface
+title = "نموذج توليد الصور"
+description = "اكتب وصف للصورة التي تود من النظام التوليدي انشاءها. على سبيل المثال: 'قطة ترتدي قبعة في مشهد شتوي'."
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.components.Textbox(lines=2, placeholder="Enter the description of the image here..."),
+        gr.components.Dropdown(choices=["Stable Diffusion v1.5",], label="Choose Model", value='Stable Diffusion v1.5'),
+        gr.components.Checkbox(label="Translate The Text Before Generating Image", value=False)
+    ],
+    outputs=gr.components.Image(),
+    title=title,
+    description=description,
+    theme="default",
+    css=css
+)
+# Launch the interface
+iface.launch()