Update app.py

app.py

@@ -1,64 +1,37 @@
 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()
+import torch
+from transformers import DalleBartProcessor, DalleBartForConditionalGeneration
+from PIL import Image
+import numpy as np
+import cv2
+
+# Load model and processor
+processor = DalleBartProcessor.from_pretrained("dalle-mini/dalle-mini/mega-1-fp16")
+model = DalleBartForConditionalGeneration.from_pretrained("dalle-mini/dalle-mini/mega-1-fp16")
+
+# Force CPU
+model.to("cpu")
+
+def image_to_sketch(pil_img):
+    img = np.array(pil_img.convert("L"))
+    inv = 255 - img
+    blur = cv2.GaussianBlur(inv, (21, 21), 0)
+    sketch = cv2.divide(img, 255 - blur, scale=256)
+    return Image.fromarray(sketch)
+
+def generate_sketch(prompt):
+    prompt = prompt + ", pencil sketch, line drawing, black and white, minimal"
+    inputs = processor([prompt], return_tensors="pt").to("cpu")
+
+    output_ids = model.generate(**inputs, max_length=256)
+    image = processor.decode(output_ids[0], output_type=Image)
+
+    sketch = image_to_sketch(image)
+    return sketch
+
+gr.Interface(
+    fn=generate_sketch,
+    inputs=gr.Textbox(placeholder="Enter a description, e.g. 'a cat on a skateboard'"),
+    outputs="image",
+    title="Text to Sketch Generator"
+).launch()