Update app.py

app.py

@@ -1,140 +1,73 @@
 # -*- coding: utf-8 -*-
 """
-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 app.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.
+Gradio Space: Text → Image using Stable Diffusion (Hugging Face Inference API)
+UI designed by Mehak Mazhar
 """
 
 import os
 import io
-import base64
-import random
 import requests
 from PIL import Image
 import gradio as gr
 
 # --- Configuration ---
-MODEL = os.environ.get("FLUX_MODEL", "black-forest-labs/FLUX.1-schnell")
+MODEL = os.environ.get("SD_MODEL", "runwayml/stable-diffusion-v1-5")
 HF_API_URL = f"https://api-inference.huggingface.co/models/{MODEL}"
 
-# --- 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 {}
+# --- API call function ---
+def generate_image(prompt, token, width, height, guidance_scale, steps):
+    if not token:
+        return None, "❌ Please provide a Hugging Face API token."
+
+    headers = {"Authorization": f"Bearer {token}"}
     payload = {
         "inputs": prompt,
-        "options": {"wait_for_model": True},
         "parameters": {
             "width": int(width),
             "height": int(height),
             "guidance_scale": float(guidance_scale),
-            "num_inference_steps": int(steps),
-            "seed": None if seed is None else int(seed),
-        }
+            "num_inference_steps": int(steps)
+        },
+        "options": {"wait_for_model": True}
     }
-    if negative_prompt:
-        payload["parameters"]["negative_prompt"] = negative_prompt
 
-    resp = requests.post(HF_API_URL, headers=headers, json=payload, stream=True, timeout=120)
-    resp.raise_for_status()
+    try:
+        response = requests.post(HF_API_URL, headers=headers, json=payload, timeout=60)
+        response.raise_for_status()
 
-    content_type = resp.headers.get("content-type", "")
-    if "application/json" in content_type:
-        data = resp.json()
-        if isinstance(data, dict):
-            for k in ("image", "images", "generated_images", "artifacts"):
-                if k in data:
-                    imgs = data[k]
-                    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)))
-        for v in data.values():
-            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:
-        return Image.open(io.BytesIO(resp.content))
+        image = Image.open(io.BytesIO(response.content))
+        return image, "✅ Image generated successfully!"
+    except Exception as e:
+        return None, f"⚠️ Error: {str(e)}"
 
 # --- Gradio UI ---
-css = r'''
-body { background: linear-gradient(135deg, #fff7e6 0%, #fffaf0 50%, #fff7fd 100%); }
-.gradio-container { font-family: 'Inter', system-ui, -apple-system, 'Segoe UI', Roboto, 'Helvetica Neue', Arial; }
-.header { display:flex; align-items:center; gap:16px; }
-.logo { width:64px; height:64px; border-radius:14px; box-shadow: 0 6px 18px rgba(0,0,0,0.08); }
-.card { background: rgba(255,255,255,0.9); border-radius:16px; padding:18px; box-shadow: 0 12px 30px rgba(0,0,0,0.06); }
-.footer { text-align:center; font-size:12px; color:#555; margin-top:12px; }
-.footer strong { color:#333; }
-.generator-btn { border-radius: 12px; padding:10px 18px; }
-'''
+css = """
+body { background-color: #fff7e6; }
+h1 { color: #a0522d; font-weight: bold; }
+"""
 
-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>"
-            )
+with gr.Blocks(css=css, title="Stable Diffusion Text-to-Image") as demo:
+    gr.HTML("<h1>Stable Diffusion — designed by Mehak Mazhar</h1>")
 
     with gr.Row():
-        with gr.Column(scale=1, min_width=360):
-            prompt = gr.Textbox(
-                label="Prompt",
-                placeholder="A serene mountain lake at sunset, digital painting, ultra-detailed",
-                lines=4
-            )
-            negative = gr.Textbox(
-                label="Negative prompt (optional)",
-                placeholder="blurry, lowres, text, watermark",
-                lines=2
-            )
-            hf_token = gr.Textbox(
-                label="Hugging Face API Token (optional)",
-                placeholder="Paste your HF token here or set HF_TOKEN env var",
-                type="password"
-            )
-            with gr.Row():
-                width = gr.Dropdown(choices=[256,384,512,768,1024], value=512, label="Width")
-                height = gr.Dropdown(choices=[256,384,512,768,1024], value=512, label="Height")
-            with gr.Row():
-                steps = gr.Slider(10, 150, value=28, step=1, label="Steps")
-                guidance = gr.Slider(1.0, 30.0, value=7.5, step=0.1, label="Guidance scale")
-            with gr.Row():
-                seed = gr.Number(value=None, precision=0, label="Seed (leave blank for random)")
-            gen_btn = gr.Button("Generate", elem_classes="generator-btn")
-            gr.Markdown("**Tip:** Use vivid, descriptive prompts. Try styles like `cinematic lighting`, `digital oil painting`, or `ultra-detailed`.")
-
-        with gr.Column(scale=1, min_width=360):
-            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)
-
-    gr.HTML("<div class='footer'><p><strong>designed by Mehak Mazhar</strong></p></div>")
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", placeholder="A futuristic city at night", lines=3)
+            hf_token = gr.Textbox(label="Hugging Face API Token", placeholder="Enter your HF token", type="password")
+            width = gr.Dropdown([256, 384, 512, 768, 1024], value=512, label="Width")
+            height = gr.Dropdown([256, 384, 512, 768, 1024], value=512, label="Height")
+            guidance = gr.Slider(1.0, 15.0, value=7.5, step=0.1, label="Guidance Scale")
+            steps = gr.Slider(10, 100, value=30, step=1, label="Steps")
+            generate_btn = gr.Button("Generate Image", variant="primary")
 
-    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")
-        if not token:
-            return None, "ERROR: No Hugging Face token provided. Set HF_TOKEN or paste it into the UI."
-        try:
-            if seed_v in (None, "", 0):
-                seed_val = random.randint(0, 2**31 - 1)
-            else:
-                seed_val = int(seed_v)
-            img = call_hf_image_api(prompt_text, token, width_v, height_v, guidance_v, steps_v, seed_val, negative_prompt=negative_text)
-            return [img], f"OK — seed={seed_val}, model={MODEL}"
-        except Exception as e:
-            return None, f"API error: {e}"
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image")
+            status = gr.Textbox(label="Status", interactive=False)
 
-    gen_btn.click(fn=generate_image, inputs=[prompt, negative, hf_token, width, height, steps, guidance, seed], outputs=[gallery, out_log])
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt, hf_token, width, height, guidance, steps],
+        outputs=[output_image, status]
+    )
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", share=False)