Add application file

app.py

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+import gradio as gr
+import requests
+import os
+import io
+from PIL import Image
+from typing import Optional
+
+
+# Load Hugging Face Token from environment variables
+def load_environment():
+    return os.getenv("HF_TOKEN")
+
+
+# Enhance the text prompt for better photorealistic results
+def craft_realistic_prompt(base_prompt: str) -> str:
+    realistic_modifiers = [
+        "photorealistic", "high resolution", "sharp focus",
+        "professional photography", "natural lighting", "detailed textures"
+    ]
+    return f"{' '.join(realistic_modifiers)}, {base_prompt}, shot on professional camera, 8k resolution"
+
+
+# Query Hugging Face API to generate image from text
+def query_hf_api(prompt: str) -> Optional[bytes]:
+    model_url = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
+    HF_TOKEN = load_environment()
+
+    if not HF_TOKEN:
+        raise ValueError("Hugging Face token not found. Set HF_TOKEN in environment variables.")
+
+    headers = {"Authorization": f"Bearer {HF_TOKEN}", "Content-Type": "application/json"}
+    payload = {
+        "inputs": craft_realistic_prompt(prompt),
+        "parameters": {
+            "negative_prompt": "cartoon, anime, low quality, bad anatomy, blurry, unrealistic, painting, drawing, sketch",
+            "num_inference_steps": 75,
+            "guidance_scale": 8.5,
+        }
+    }
+
+    response = requests.post(model_url, headers=headers, json=payload, timeout=120)
+    response.raise_for_status()
+    return response.content
+
+
+# Generate image from text and return as PIL image
+def generate_image(prompt: str):
+    try:
+        image_bytes = query_hf_api(prompt)
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        return image
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# Modern Gradio UI using Blocks
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎨 Wangoes Text-to-Image Generator")
+    gr.Markdown("Generate stunning AI images from text using Hugging Face models.")
+
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="Enter your prompt",
+                                      placeholder="Describe the image you want to generate...")
+            generate_button = gr.Button("Generate Image", variant="primary")
+
+        with gr.Column():
+            image_output = gr.Image(label="Generated Image", type="pil")
+
+    gr.Examples(
+        examples=[
+            "A futuristic cityscape at sunset with flying cars",
+            "A serene mountain lake with crystal clear water",
+            "A cozy cabin in the woods during winter"
+        ],
+        inputs=prompt_input
+    )
+
+    generate_button.click(fn=generate_image, inputs=prompt_input, outputs=image_output)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+requests
+Pillow
+python-dotenv