Update app.py

app.py

@@ -1,16 +1,4 @@
-"""
-Gradio Space: Text → Image using FLUX.1 (Hugging Face Inference API)
-Attractive interface with custom styling and footer label: "designed by Mehak Mazhar"
-
-How to use:
-1. Install dependencies: pip install gradio requests pillow
-2. Get a Hugging Face API token (if you want to use the hosted FLUX.1 models) and either set it as an env var HF_TOKEN or paste it into the 'HF Token' field in the UI.
-3. Run: python gradio_flux_text2img.py
-
-Notes: this script calls the Hugging Face Inference API for the model 'black-forest-labs/FLUX.1-schnell' by default.
-You can change the MODEL variable to any compatible image generation model hosted on Hugging Face or point to your own inference server.
-
-"""
+# -*- coding: utf-8 -*-
 
 import os
 import io
@@ -24,7 +12,7 @@ import gradio as gr
 MODEL = os.environ.get("FLUX_MODEL", "black-forest-labs/FLUX.1-schnell")
 HF_API_URL = f"https://api-inference.huggingface.co/models/{MODEL}"
 
-# A small helper to call the Hugging Face Inference API (image generation)
+# --- Helper to call Hugging Face API ---
 def call_hf_image_api(prompt, token, width, height, guidance_scale, steps, seed, negative_prompt=None):
     headers = {"Authorization": f"Bearer {token}"} if token else {}
     payload = {
@@ -41,30 +29,25 @@ def call_hf_image_api(prompt, token, width, height, guidance_scale, steps, seed,
     if negative_prompt:
         payload["parameters"]["negative_prompt"] = negative_prompt
 
-    # Many HF image models return binary image bytes directly. Some return JSON with base64.
     resp = requests.post(HF_API_URL, headers=headers, json=payload, stream=True, timeout=120)
     resp.raise_for_status()
 
     content_type = resp.headers.get("content-type", "")
     if "application/json" in content_type:
         data = resp.json()
-        # attempt to find a base64 image in JSON response
-        # common key patterns: 'image', 'images', 'generated_images'
         if isinstance(data, dict):
             for k in ("image", "images", "generated_images", "artifacts"):
                 if k in data:
                     imgs = data[k]
-                    if isinstance(imgs, list) and len(imgs) > 0:
+                    if isinstance(imgs, list) and imgs:
                         b64 = imgs[0].get("data") if isinstance(imgs[0], dict) else imgs[0]
                         if isinstance(b64, str):
                             return Image.open(io.BytesIO(base64.b64decode(b64)))
-        # fallback: try to find base64 strings in the JSON
         for v in data.values():
-            if isinstance(v, str) and v.strip().startswith("iVBOR"):  # PNG base64 signature
+            if isinstance(v, str) and v.strip().startswith("iVBOR"):
                 return Image.open(io.BytesIO(base64.b64decode(v)))
         raise ValueError("Could not parse image from JSON response")
     else:
-        # assume raw image bytes
         return Image.open(io.BytesIO(resp.content))
 
 # --- Gradio UI ---
@@ -82,7 +65,14 @@ body { background: linear-gradient(135deg, #fff7e6 0%, #fffaf0 50%, #fff7fd 100%
 with gr.Blocks(css=css, title="Flux Text→Image — designed by Mehak Mazhar") as demo:
     with gr.Row(elem_id="top-row"):
         with gr.Column(scale=1):
-            gr.HTML("<div class='header'><img class='logo' src='https://raw.githubusercontent.com/black-forest-labs/flux/main/logo.png' alt='Flux logo' onerror='this.style.display=\'none\'' + "> <div><h2 style='margin:0'>FLUX.1 Text → Image</h2><p style='margin:0;color:#555;'>Generate high-quality images from text (Hugging Face inference API)</p></div></div>")
+            gr.HTML(
+                "<div class='header'>"
+                "<img class='logo' src='https://raw.githubusercontent.com/black-forest-labs/flux/main/logo.png' "
+                "alt='Flux logo' onerror=\"this.style.display='none'\"> "
+                "<div><h2 style='margin:0'>FLUX.1 Text → Image</h2>"
+                "<p style='margin:0;color:#555;'>Generate high-quality images from text (Hugging Face inference API)</p></div>"
+                "</div>"
+            )
 
     with gr.Row():
         with gr.Column(scale=1, min_width=360):
@@ -104,8 +94,7 @@ with gr.Blocks(css=css, title="Flux Text→Image — designed by Mehak Mazhar")
             gallery = gr.Gallery(label="Generated images", show_label=True, elem_id="gallery").style(grid=[2], height="640px")
             out_log = gr.Textbox(label="Status / Debug log", lines=4, interactive=False)
 
-    # Footer with the requested label
-    gr.HTML("<div class='footer'>\n<p><strong>designed by Mehak Mazhar</strong></p>\n</div>")
+    gr.HTML("<div class='footer'><p><strong>designed by Mehak Mazhar</strong></p></div>")
 
     def generate_image(prompt_text, negative_text, hf_token_text, width_v, height_v, steps_v, guidance_v, seed_v):
         token = hf_token_text.strip() or os.environ.get("HF_TOKEN")
@@ -123,6 +112,5 @@ with gr.Blocks(css=css, title="Flux Text→Image — designed by Mehak Mazhar")
 
     gen_btn.click(fn=generate_image, inputs=[prompt, negative, hf_token, width, height, steps, guidance, seed], outputs=[gallery, out_log])
 
-# Run the app when executed directly
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", share=False)