Create app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import torch
+from transformers import pipeline
+from PIL import Image
+import numpy as np
+from diffusers import DiffusionPipeline
+
+# Ensure PyTorch runs on GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load translation model (Arabic to English) on GPU
+translator = pipeline("translation_ar_to_en", model="Helsinki-NLP/opus-mt-ar-en", device=0 if torch.cuda.is_available() else -1)
+
+# Load image generation model
+pipe = DiffusionPipeline.from_pretrained("sairajg/Text_To_Image")
+
+
+
+def translate_and_generate(arabic_text):
+
+    translated_text = translator(arabic_text)[0]['translation_text']
+    result = pipe(translated_text).images[0]
+
+    return result, translated_text
+
+    try:
+
+        # Ensure we get the actual image
+        # if isinstance(result, tuple):
+        #     result = result[0]  # Extract first element
+        # if isinstance(result, torch.Tensor):
+        #     result = result.cpu().numpy()  # Convert to NumPy array
+        # if isinstance(result, np.ndarray):
+        #     result = Image.fromarray((result * 255).astype(np.uint8))  # Ensure proper pixel range
+        # elif not isinstance(result, Image.Image):
+        #     raise ValueError(f"Unexpected output type: {type(result)}")
+
+        debug_info += f"Translated Prompt: {translated_text}\nResult Type: {type(result)}"
+        return result, debug_info
+    except Exception as e:
+        return None, debug_info
+
+
+with gr.Blocks() as interface:
+    gr.Markdown("### Arabic to Image Generator ")
+
+    text_input = gr.Textbox(label="Enter Arabic Prompt:", placeholder="اكتب هنا...")
+    generate_button = gr.Button("Generate Image ")
+    image_output = gr.Image(label="Generated Image")
+    text_output = gr.Textbox(label="Debug Output")
+
+    generate_button.click(translate_and_generate, inputs=text_input, outputs=[image_output, text_output])
+
+interface.launch()