Update app.py

app.py

@@ -4,49 +4,41 @@ from huggingface_hub import InferenceClient
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
+# Create InferenceClients for each model (you may need to adjust based on model endpoints)
+stable_diffusion = InferenceClient("stabilityai/stable-diffusion-3.5-large-turbo")
+dalle_3 = InferenceClient("ehristoforu/dalle-3-xl-v2")
+flux = InferenceClient("black-forest-labs/FLUX.1-dev")
+
+def generate_image(model_choice, prompt, num_images=1):
+    """Function to generate images based on the chosen model."""
+    if model_choice == "Stable Diffusion 3.5 Large Turbo":
+        response = stable_diffusion.text_to_image(prompt, num_images=num_images)
+    elif model_choice == "DALL·E 3 XL":
+        response = dalle_3.text_to_image(prompt, num_images=num_images)
+    elif model_choice == "FLUX.1-dev":
+        response = flux.text_to_image(prompt, num_images=num_images)
+    
+    # Return the generated images (assuming each model returns a URL or image object)
+    return response[0]["image"]  # Adjust as needed based on actual response format
+
+# Create a function to handle user input
+def generate_image_response(prompt, model_choice):
+    image = generate_image(model_choice, prompt)
+    return image
+
+# Define Gradio Interface
+demo = gr.Interface(
+    fn=generate_image_response, 
+    inputs=[
+        gr.Textbox(label="Enter your prompt here"),
+        gr.Dropdown(choices=["Stable Diffusion 3.5 Large Turbo", "DALL·E 3 XL", "FLUX.1-dev"], label="Choose Model", value="Stable Diffusion 3.5 Large Turbo")
+    ],
+    outputs="image",
+    title="DreamXL Image",
+    description="Welcome to DreamXL Image! Choose a model and input your prompt to generate stunning visuals.",
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value="You are a helpful image generation assistant.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(
@@ -59,6 +51,5 @@ demo = gr.ChatInterface(
     ],
 )
 
-
 if __name__ == "__main__":
     demo.launch()