feat(scraper): support streaming

npiai/tools/web/scraper/__test__/bardeen.py

@@ -1,16 +1,17 @@
 import asyncio
+import json
+
 from npiai.tools.web.scraper import Scraper
 from npiai.utils.test_utils import DebugContext
 
 
 async def main():
     async with Scraper(headless=False, batch_size=10) as scraper:
-        await scraper.summarize(
+        stream = scraper.summarize_stream(
             ctx=DebugContext(),
             url="https://www.bardeen.ai/playbooks",
             ancestor_selector=".playbook_list",
             items_selector=".playbook_list .playbook_item-link",
-            output_file=".cache/bardeen.csv",
             output_columns=[
                 {
                     "name": "Apps Involved",
@@ -32,6 +33,9 @@ async def main():
             limit=42,
         )
 
+        async for items in stream:
+            print("Chunk:", json.dumps(items, indent=2))
+
 
 if __name__ == "__main__":
     asyncio.run(main())

npiai/tools/web/scraper/__test__/column_inference.py

@@ -20,16 +20,18 @@ async def main():
 
         print("Inferred columns:", json.dumps(columns, indent=2))
 
-        await scraper.summarize(
+        stream = scraper.summarize_stream(
             ctx=DebugContext(),
             url=url,
             ancestor_selector=ancestor_selector,
             items_selector=items_selector,
             output_columns=columns,
-            output_file=".cache/bardeen.csv",
             limit=10,
         )
 
+        async for items in stream:
+            print("Chunk:", json.dumps(items, indent=2))
+
 
 if __name__ == "__main__":
     asyncio.run(main())

npiai/tools/web/scraper/app.py

@@ -81,6 +81,65 @@ def from_context(cls, ctx: Context) -> "Scraper":
             )
         return cls()
 
+    async def summarize_stream(
+        self,
+        ctx: Context,
+        url: str,
+        output_columns: List[Column],
+        ancestor_selector: str | None = None,
+        items_selector: str | None = None,
+        pagination_button_selector: str | None = None,
+        limit: int = -1,
+    ):
+        if limit == 0:
+            return
+
+        await self.playwright.page.goto(url)
+
+        if not ancestor_selector:
+            ancestor_selector = "body"
+
+        count = 0
+
+        while True:
+            remaining = min(self._batch_size, limit - count) if limit != -1 else -1
+
+            md = await self._get_md(
+                ctx=ctx,
+                ancestor_selector=ancestor_selector,
+                items_selector=items_selector,
+                limit=remaining,
+            )
+
+            if not md:
+                break
+
+            items = await self._llm_summarize(ctx, md, output_columns)
+
+            await ctx.send_debug_message(f"[{self.name}] Summarized {len(items)} items")
+
+            if not items:
+                break
+
+            items_slice = items[:remaining] if limit != -1 else items
+            count += len(items_slice)
+
+            yield items_slice
+
+            await ctx.send_debug_message(
+                f"[{self.name}] Summarized {count} items in total"
+            )
+
+            if limit != -1 and count >= limit:
+                break
+
+            await self._load_more(
+                ctx,
+                ancestor_selector,
+                items_selector,
+                pagination_button_selector,
+            )
+
     @function
     async def summarize(
         self,
@@ -106,17 +165,6 @@ async def summarize(
             output_file: The file path to save the output. If None, the output is saved to 'scraper_output.json'.
             limit: The maximum number of items to summarize. If -1, all items are summarized.
         """
-        if limit == 0:
-            return "No items to summarize"
-
-        await self.playwright.page.goto(url)
-
-        if not ancestor_selector:
-            ancestor_selector = "body"
-
-        if not output_file:
-            output_file = "scraper_output.csv"
-
         os.makedirs(os.path.dirname(output_file), exist_ok=True)
 
         with open(output_file, "w", newline="") as f:
@@ -127,48 +175,21 @@ async def summarize(
 
             count = 0
 
-            while True:
-                remaining = min(self._batch_size, limit - count) if limit != -1 else -1
-
-                md = await self._get_md(
-                    ctx=ctx,
-                    ancestor_selector=ancestor_selector,
-                    items_selector=items_selector,
-                    limit=remaining,
-                )
-
-                if not md:
-                    break
-
-                items = await self._llm_summarize(ctx, md, output_columns)
-
-                await ctx.send_debug_message(
-                    f"[{self.name}] Summarized {len(items)} items"
-                )
-
-                if not items:
-                    break
+            stream = self.summarize_stream(
+                ctx,
+                url,
+                output_columns,
+                ancestor_selector,
+                items_selector,
+                pagination_button_selector,
+                limit,
+            )
 
-                items_slice = items[:remaining] if limit != -1 else items
-                writer.writerows(items_slice)
+            async for items in stream:
+                writer.writerows(items)
+                count += len(items)
                 f.flush()
 
-                count += len(items_slice)
-
-                await ctx.send_debug_message(
-                    f"[{self.name}] Summarized {count} items in total"
-                )
-
-                if limit != -1 and count >= limit:
-                    break
-
-                await self._load_more(
-                    ctx,
-                    ancestor_selector,
-                    items_selector,
-                    pagination_button_selector,
-                )
-
         return f"Saved {count} items to {output_file}"
 
     @function