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README.md

@@ -1,12 +1,14 @@
----
-title: Text To Image
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+---
+title: Text To Image
+emoji: 👀
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.29.0
+app_file: app.py
+pinned: false
+tags:
+- tool
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,64 +1,4 @@
-import gradio as gr
-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,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", 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(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from transformers import launch_gradio_demo
+from tool import TextToImageTool
+
+launch_gradio_demo(TextToImageTool)

requirements.txt

@@ -1 +1,2 @@
-huggingface_hub==0.25.2
+huggingface_hub
+smolagents

tool.py

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+from smolagents import Tool
+from huggingface_hub import InferenceClient
+
+
+class TextToImageTool(Tool):
+    description = "This tool creates an image according to a prompt, which is a text description."
+    name = "image_generator"
+    inputs = {"prompt": {"type": "string", "description": "The image generator prompt. Don't hesitate to add details in the prompt to make the image look better, like 'high-res, photorealistic', etc."}}
+    output_type = "image"
+    model_sdxl = "black-forest-labs/FLUX.1-schnell"
+    client = InferenceClient(model_sdxl)
+
+
+    def forward(self, prompt):
+        return self.client.text_to_image(prompt)

tool_config.json

@@ -0,0 +1,7 @@
+{
+  "description": "This is a tool that creates an image according to a prompt, which is a text description.",
+  "inputs": "{'prompt': {'type': 'string', 'description': \"The image generator prompt. Don't hesitate to add details in the prompt to make the image look better, like 'high-res, photorealistic', etc.\"}}",
+  "name": "image_generator",
+  "output_type": "image",
+  "tool_class": "tool.TextToImageTool"
+}