merge in master

.gitignore

@@ -38,6 +38,7 @@ demo/files/*.mp4
 *.bak
 workspace.code-workspace
 *.h5
+.vscode/
 
 # log files
 .pnpm-debug.log

codecov.yml

@@ -10,9 +10,7 @@ coverage:
           - "gradio/"
         target: 70%
         threshold: 0.1
-    patch:
-      default:
-        target: 50%  # new contributions should have a coverage at least equal to 50%
+    patch: off
 
 comment: false
 codecov:

gradio.egg-info/PKG-INFO

@@ -1,11 +1,14 @@
-Metadata-Version: 1.0
+Metadata-Version: 2.1
 Name: gradio
-Version: 2.7.0b70
+Version: 2.8.13
 Summary: Python library for easily interacting with trained machine learning models
 Home-page: https://github.com/gradio-app/gradio-UI
 Author: Abubakar Abid, Ali Abid, Ali Abdalla, Dawood Khan, Ahsen Khaliq, Pete Allen, Ömer Faruk Özdemir
 Author-email: team@gradio.app
 License: Apache License 2.0
-Description: UNKNOWN
 Keywords: machine learning,visualization,reproducibility
 Platform: UNKNOWN
+License-File: LICENSE
+
+UNKNOWN
+

gradio/blocks.py

@@ -10,7 +10,7 @@
 
 from gradio import encryptor, networking, queueing, strings, utils
 from gradio.context import Context
-from gradio.process_examples import cache_interface_examples
+from gradio.routes import PredictBody
 
 if TYPE_CHECKING:  # Only import for type checking (is False at runtime).
     from fastapi.applications import FastAPI
@@ -19,7 +19,8 @@
 
 
 class Block:
-    def __init__(self, without_rendering=False):
+    def __init__(self, without_rendering=False, css=None):
+        self.css = css
         if without_rendering:
             return
         self.render()
@@ -103,7 +104,7 @@ def __init__(self, visible: bool = True, css: Optional[Dict[str, str]] = None):
         css: Css rules to apply to block.
         """
         self.children = []
-        self.css = css if css is not None else {}
+        self.css = css
         self.visible = visible
         super().__init__()
 
@@ -378,7 +379,6 @@ def launch(
         height: int = 500,
         width: int = 900,
         encrypt: bool = False,
-        cache_examples: bool = False,
         favicon_path: Optional[str] = None,
         ssl_keyfile: Optional[str] = None,
         ssl_certfile: Optional[str] = None,
@@ -403,7 +403,6 @@ def launch(
         width (int): The width in pixels of the iframe element containing the interface (used if inline=True)
         height (int): The height in pixels of the iframe element containing the interface (used if inline=True)
         encrypt (bool): If True, flagged data will be encrypted by key provided by creator at launch
-        cache_examples (bool): If True, examples outputs will be processed and cached in a folder, and will be used if a user uses an example input.
         favicon_path (str): If a path to a file (.png, .gif, or .ico) is provided, it will be used as the favicon for the web page.
         ssl_keyfile (str): If a path to a file is provided, will use this as the private key file to create a local server running on https.
         ssl_certfile (str): If a path to a file is provided, will use this as the signed certificate for https. Needs to be provided if ssl_keyfile is provided.
@@ -414,7 +413,6 @@ def launch(
         share_url (str): Publicly accessible link to the demo (if share=True, otherwise None)
         """
         self.config = self.get_config_file()
-        self.cache_examples = cache_examples
         if (
             auth
             and not callable(auth)
@@ -443,9 +441,6 @@ def launch(
         config = self.get_config_file()
         self.config = config
 
-        if self.cache_examples:
-            cache_interface_examples(self)
-
         if self.is_running:
             self.server_app.launchable = self
             print(

gradio/external.py

@@ -4,7 +4,7 @@
 
 import requests
 
-from gradio import components
+from gradio import components, utils
 
 
 def get_huggingface_interface(model_name, api_key, alias):
@@ -206,6 +206,13 @@ def encode_to_base64(r: requests.Response) -> str:
             "preprocess": lambda x: {"inputs": x},
             "postprocess": encode_to_base64,
         },
+        "token-classification": {
+            # example model: hf.co/huggingface-course/bert-finetuned-ner
+            "inputs": components.Textbox(label="Input"),
+            "outputs": components.HighlightedText(label="Output"),
+            "preprocess": lambda x: {"inputs": x},
+            "postprocess": lambda r: r,  # Handled as a special case in query_huggingface_api()
+        },
     }
 
     if p is None or not (p in pipelines):
@@ -228,6 +235,12 @@ def query_huggingface_api(*params):
                     response.status_code
                 )
             )
+        if (
+            p == "token-classification"
+        ):  # Handle as a special case since HF API only returns the named entities and we need the input as well
+            ner_groups = response.json()
+            input_string = params[0]
+            response = utils.format_ner_list(input_string, ner_groups)
         output = pipeline["postprocess"](response)
         return output
 

gradio/flagging.py

@@ -9,7 +9,7 @@
 from typing import TYPE_CHECKING, Any, List, Optional
 
 import gradio as gr
-from gradio import encryptor
+from gradio import encryptor, utils
 
 if TYPE_CHECKING:
     from gradio.components import Component
@@ -87,7 +87,7 @@ def flag(
             )
 
         with open(log_filepath, "a", newline="") as csvfile:
-            writer = csv.writer(csvfile)
+            writer = csv.writer(csvfile, quoting=csv.QUOTE_NONNUMERIC, quotechar="'")
             writer.writerow(csv_data)
 
         with open(log_filepath, "r") as csvfile:
@@ -153,7 +153,7 @@ def replace_flag_at_index(file_content):
             flag_col_index = header.index("flag")
             content[flag_index][flag_col_index] = flag_option
             output = io.StringIO()
-            writer = csv.writer(output)
+            writer = csv.writer(output, quoting=csv.QUOTE_NONNUMERIC, quotechar="'")
             writer.writerows(content)
             return output.getvalue()
 
@@ -169,7 +169,7 @@ def replace_flag_at_index(file_content):
                     if flag_index is not None:
                         file_content = replace_flag_at_index(file_content)
                     output.write(file_content)
-            writer = csv.writer(output)
+            writer = csv.writer(output, quoting=csv.QUOTE_NONNUMERIC, quotechar="'")
             if flag_index is None:
                 if is_new:
                     writer.writerow(headers)
@@ -181,7 +181,9 @@ def replace_flag_at_index(file_content):
         else:
             if flag_index is None:
                 with open(log_filepath, "a", newline="") as csvfile:
-                    writer = csv.writer(csvfile)
+                    writer = csv.writer(
+                        csvfile, quoting=csv.QUOTE_NONNUMERIC, quotechar="'"
+                    )
                     if is_new:
                         writer.writerow(headers)
                     writer.writerow(csv_data)
@@ -291,6 +293,7 @@ def flag(
                 headers = []
 
                 for component, sample in zip(self.components, flag_data):
+                    headers.append(component.label)
                     headers.append(component.label)
                     infos["flagged"]["features"][component.label] = {
                         "dtype": "string",
@@ -316,12 +319,8 @@ def flag(
             # Generate the row corresponding to the flagged sample
             csv_data = []
             for component, sample in zip(self.components, flag_data):
-                filepath = (
-                    component.save_flagged(
-                        self.dataset_dir, component.label, sample, None
-                    )
-                    if sample is not None
-                    else ""
+                filepath = component.save_flagged(
+                    self.dataset_dir, component.label, sample, None
                 )
                 csv_data.append(filepath)
                 if isinstance(component, tuple(file_preview_types)):

gradio/process_examples.py

@@ -54,7 +54,7 @@ def cache_interface_examples(interface: Interface) -> None:
 def load_from_cache(interface: Interface, example_id: int) -> List[Any]:
     """Loads a particular cached example for the interface."""
     with open(CACHE_FILE) as cache:
-        examples = list(csv.reader(cache))
+        examples = list(csv.reader(cache, quotechar="'"))
     example = examples[example_id + 1]  # +1 to adjust for header
     output = []
     for component, cell in zip(interface.output_components, example):

gradio/queueing.py

@@ -7,6 +7,8 @@
 
 import requests
 
+from gradio.routes import QueuePushBody
+
 DB_FILE = "gradio_queue.db"
 
 
@@ -106,8 +108,9 @@ def pop() -> Tuple[int, str, Dict, str]:
     return result[0], result[1], json.loads(result[2]), result[3]
 
 
-def push(input_data: Dict, action: str) -> Tuple[str, int]:
-    input_data = json.dumps(input_data)
+def push(body: QueuePushBody) -> Tuple[str, int]:
+    action = body.action
+    input_data = json.dumps({"data": body.data})
     hash = generate_hash()
     conn = sqlite3.connect(DB_FILE)
     c = conn.cursor()

gradio/routes.py

@@ -9,7 +9,8 @@
 import secrets
 import traceback
 import urllib
-from typing import Any, List, Optional, Type
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Type
 
 import orjson
 import pkg_resources
@@ -21,6 +22,7 @@
 from fastapi.security import OAuth2PasswordRequestForm
 from fastapi.templating import Jinja2Templates
 from jinja2.exceptions import TemplateNotFound
+from pydantic import BaseModel
 from starlette.responses import RedirectResponse
 
 from gradio import encryptor, queueing, utils
@@ -49,6 +51,43 @@ def render(self, content: Any) -> bytes:
 templates = Jinja2Templates(directory=STATIC_TEMPLATE_LIB)
 
 
+###########
+# Data Models
+###########
+
+
+class PredictBody(BaseModel):
+    session_hash: Optional[str]
+    example_id: Optional[int]
+    data: List[Any]
+    state: Optional[Any]
+    fn_index: Optional[int]
+
+
+class FlagData(BaseModel):
+    input_data: List[Any]
+    output_data: List[Any]
+    flag_option: Optional[str]
+    flag_index: Optional[int]
+
+
+class FlagBody(BaseModel):
+    data: FlagData
+
+
+class InterpretBody(BaseModel):
+    data: List[Any]
+
+
+class QueueStatusBody(BaseModel):
+    hash: str
+
+
+class QueuePushBody(BaseModel):
+    action: str
+    data: Any
+
+
 ###########
 # Auth
 ###########
@@ -166,7 +205,8 @@ def file(path):
                 io.BytesIO(file_data), attachment_filename=os.path.basename(path)
             )
         else:
-            return FileResponse(safe_join(app.cwd, path))
+            if Path(app.cwd).resolve() in Path(path).resolve().parents:
+                return FileResponse(Path(path).resolve())
 
     @app.get("/api", response_class=HTMLResponse)  # Needed for Spaces
     @app.get("/api/", response_class=HTMLResponse)
@@ -229,49 +269,14 @@ async def predict(request: Request, username: str = Depends(get_current_user)):
                 raise error
         return output
 
-    @app.post("/api/flag/", dependencies=[Depends(login_check)])
-    async def flag(request: Request, username: str = Depends(get_current_user)):
-        if app.blocks.analytics_enabled:
-            await utils.log_feature_analytics(app.blocks.ip_address, "flag")
-        body = await request.json()
-        data = body["data"]
-        await run_in_threadpool(
-            app.blocks.flagging_callback.flag,
-            app.blocks,
-            data["input_data"],
-            data["output_data"],
-            flag_option=data.get("flag_option"),
-            flag_index=data.get("flag_index"),
-            username=username,
-        )
-        return {"success": True}
-
-    @app.post("/api/interpret/", dependencies=[Depends(login_check)])
-    async def interpret(request: Request):
-        if app.blocks.analytics_enabled:
-            await utils.log_feature_analytics(app.blocks.ip_address, "interpret")
-        body = await request.json()
-        raw_input = body["data"]
-        interpretation_scores, alternative_outputs = await run_in_threadpool(
-            app.blocks.interpret, raw_input
-        )
-        return {
-            "interpretation_scores": interpretation_scores,
-            "alternative_outputs": alternative_outputs,
-        }
-
     @app.post("/api/queue/push/", dependencies=[Depends(login_check)])
-    async def queue_push(request: Request):
-        body = await request.json()
-        action = body["action"]
-        job_hash, queue_position = queueing.push(body, action)
+    async def queue_push(body: QueuePushBody):
+        job_hash, queue_position = queueing.push(body)
         return {"hash": job_hash, "queue_position": queue_position}
 
     @app.post("/api/queue/status/", dependencies=[Depends(login_check)])
-    async def queue_status(request: Request):
-        body = await request.json()
-        hash = body["hash"]
-        status, data = queueing.get_status(hash)
+    async def queue_status(body: QueueStatusBody):
+        status, data = queueing.get_status(body.hash)
         return {"status": status, "data": data}
 
     return app