lint

leptonai · Mar 29, 2024 · 9c9a60d · 9c9a60d
1 parent da6ee36
commit 9c9a60d
Show file tree

Hide file tree

Showing 3 changed files with 12 additions and 18 deletions.
diff --git a/advanced/earning-sage/main.py b/advanced/earning-sage/main.py
@@ -61,11 +61,9 @@ def ui(self):
 
         with blocks:
             gr.Markdown("# 🧙🏼 Earning Report Assistant")
-            gr.Markdown(
-                """
+            gr.Markdown("""
                 This is an earning report assistant built for investors can't make the earning call on time. This sample is using Apple 2023 Q2 report. Feel free to reach out to [email protected] for more advanced features.
-            """
-            )
+            """)
             with gr.Row():
                 chatbot = gr.Chatbot(label="Model")
             with gr.Row():

diff --git a/advanced/hf-stream-llm/photon.py b/advanced/hf-stream-llm/photon.py
@@ -52,11 +52,9 @@ def run(self, text: str, max_new_tokens: int = 100) -> StreamingResponse:
 
         streamer = TextIteratorStreamer(self._tok, skip_prompt=True, timeout=60)
         inputs = self._tok(text, return_tensors="pt").to("cuda")
-        self._generation_queue.put_nowait(
-            (
-                streamer,
-                (),
-                dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens),
-            )
-        )
+        self._generation_queue.put_nowait((
+            streamer,
+            (),
+            dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens),
+        ))
         return streamer
diff --git a/advanced/segment-anything/sam.py b/advanced/segment-anything/sam.py
@@ -229,13 +229,11 @@ def generate_mask(self, url: str) -> PNGResponse:
         # The below rendering code is copied from the segment-anything repo to draw the mask
         # on top of the original image.
         sorted_anns = sorted(masks, key=(lambda x: x["area"]), reverse=True)
-        mask_img = np.ones(
-            (
-                sorted_anns[0]["segmentation"].shape[0],
-                sorted_anns[0]["segmentation"].shape[1],
-                3,
-            )
-        )
+        mask_img = np.ones((
+            sorted_anns[0]["segmentation"].shape[0],
+            sorted_anns[0]["segmentation"].shape[1],
+            3,
+        ))
         for ann in sorted_anns:
             mask_img[ann["segmentation"]] = np.random.random(3)
         alpha = 0.35