Create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# List of available models (custom models included)
+model_options = {
+    "Stable Diffusion 2": "stabilityai/stable-diffusion-2",
+    "Stable Diffusion 1.5": "runwayml/stable-diffusion-v1-5",
+    "DALL-E Mini": "dalle-mini/dalle-mini",
+    "FLUX 1.0 (black-forest-labs)": "black-forest-labs/FLUX.1-dev",
+    "Pony Diffusion": "AstraliteHeart/ponydiffusion"
+}
+
+# Initialize Hugging Face Inference Client
+def get_client(model_name):
+    return InferenceClient(model=model_name)
+
+# Function to generate the image based on selected model and prompt
+def generate_image(prompt, model_name):
+    client = get_client(model_options[model_name])
+    response = client.text_to_image(prompt, guidance_scale=7.5)
+    return response
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Text to Image Generator using Hugging Face Inference Client")
+    
+    with gr.Row():
+        with gr.Column():
+            # Dropdown for model selection
+            model_dropdown = gr.Dropdown(
+                label="Select Model",
+                choices=list(model_options.keys()),  # Display model names
+                value="Stable Diffusion 2",  # Default model
+            )
+            
+            # Input for text prompt
+            prompt_input = gr.Textbox(label="Enter your prompt", placeholder="Describe the image you want...")
+            
+            # Button to generate image
+            generate_button = gr.Button("Generate Image")
+        
+        with gr.Column():
+            # Image output
+            image_output = gr.Image(label="Generated Image")
+    
+    # Link the button click to the function
+    generate_button.click(generate_image, inputs=[prompt_input, model_dropdown], outputs=image_output)
+
+# Launch the Gradio app
+demo.launch()