Create app.py

app.py

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+from diffusers import StableDiffusionPipeline
+import torch
+import gradio as gr
+
+print("Loading the Stable Diffusion model (this may take a minute)...")
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipeline.to("cuda")
+print("Model loaded successfully!")
+
+def generate_image(prompt, guidance_scale):
+    """
+    Generate an image from a text prompt using Stable Diffusion.
+
+    Args:
+        prompt (str): The text prompt for generating the image.
+        guidance_scale (float): Controls how closely the image matches the prompt.
+
+    Returns:
+        PIL.Image.Image: The generated image.
+    """
+    if not prompt.strip():
+        return "Error: Prompt cannot be empty!"
+    try:
+        image = pipeline(prompt, guidance_scale=guidance_scale).images[0]
+        return image
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🖼 Text-to-Image Generator by Abhinav ")
+    gr.Markdown("Enter a text description below to generate an image.")
+
+    with gr.Row():
+        prompt_input = gr.Textbox(label="Enter Image Prompt", placeholder="e.g., A beautiful place in Kerala", lines=2)
+        guidance_slider = gr.Slider(label="Guidance Scale", minimum=5.0, maximum=15.0, value=7.5, step=0.5)
+
+    generate_button = gr.Button("Generate Image")
+    output_image = gr.Image(label="Generated Image")
+
+    generate_button.click(
+        fn=generate_image,
+        inputs=[prompt_input, guidance_slider],
+        outputs=output_image
+    )
+demo.launch()