Upload 3 files

app.py

@@ -14,7 +14,7 @@ with gr.Blocks(theme="NoCrypt/miku", fill_width=True, css=CSS) as demo:
     gr.Markdown("# Search Hugging Face🤗", elem_classes="title")
     with gr.Column():
         search_result = gr.State(value=HFSearchResult())
-        with gr.Tab("Basic Search"):
+        with gr.Tab("Search"):
             with gr.Group():
                 with gr.Row(equal_height=True):
                     repo_types = gr.CheckboxGroup(label="Repo type", choices=["model", "dataset", "space", "collection"], value=["model", "dataset", "space"])
@@ -67,7 +67,7 @@ with gr.Blocks(theme="NoCrypt/miku", fill_width=True, css=CSS) as demo:
                         with gr.Row(equal_height=True):
                             show_labels = gr.CheckboxGroup(label="Show items", choices=get_labels(), value=get_valid_labels())
                 run_button = gr.Button("Search", variant="primary")
-        with gr.Tab("Find Serverless Inference API enabled models"):
+        with gr.Tab("Inference API models"):
             with gr.Group():
                 with gr.Row(equal_height=True):
                     infer_repo_types = gr.CheckboxGroup(label="Repo type", choices=["model", "dataset", "space"], value=["model"], visible=False)
@@ -78,7 +78,20 @@ with gr.Blocks(theme="NoCrypt/miku", fill_width=True, css=CSS) as demo:
                         infer_gated_status = gr.Radio(label="Gated status", choices=["gated", "non-gated", "all"], value="all")
                         infer_appr_status = gr.CheckboxGroup(label="Approval method", choices=["auto", "manual"], value=["auto", "manual"])
                 infer_run_button = gr.Button("Search", variant="primary")
-        with gr.Tab("Find recommended repos"):
+        with gr.Tab("Collections"):
+            with gr.Group():
+                with gr.Row(equal_height=True):
+                    #rec_repo_id = gr.Textbox(label="Repo ID", info="Input your favorite repo", value="")
+                    col_repo_id = HuggingfaceHubSearch(label="Repo ID or User ID", placeholder="Input your favorite Repo ID or User ID", search_type=["model", "dataset", "space", "user"],
+                                                       sumbit_on_select=False)
+                    col_sort = gr.Dropdown(label="Sort", choices=["last_modified", "trending_score", "likes"], value="likes")
+                with gr.Row(equal_height=True):
+                    col_repo_limit = gr.Number(label="Limit for repos", value=50, step=1, minimum=1, maximum=100)
+                    col_user_limit = gr.Number(label="Limit for users", value=10, step=1, minimum=0, maximum=50)
+                with gr.Accordion("Advanced", open=False):
+                    col_show_labels = gr.CheckboxGroup(label="Show items", choices=get_labels(), value=get_valid_labels())
+            col_run_button = gr.Button("Search", variant="primary")
+        with gr.Tab("Recommended repos"):
             with gr.Group():
                 with gr.Row(equal_height=True):
                     #rec_repo_id = gr.Textbox(label="Repo ID", info="Input your favorite repo", value="")
@@ -92,19 +105,6 @@ with gr.Blocks(theme="NoCrypt/miku", fill_width=True, css=CSS) as demo:
                     with gr.Row(equal_height=True):
                         rec_show_labels = gr.CheckboxGroup(label="Show items", choices=get_labels(), value=get_valid_labels())
             rec_run_button = gr.Button("Search", variant="primary")
-        with gr.Tab("Find collections"):
-            with gr.Group():
-                with gr.Row(equal_height=True):
-                    #rec_repo_id = gr.Textbox(label="Repo ID", info="Input your favorite repo", value="")
-                    col_repo_id = HuggingfaceHubSearch(label="Repo ID or User ID", placeholder="Input your favorite Repo ID or User ID", search_type=["model", "dataset", "space", "user"],
-                                                       sumbit_on_select=False)
-                    col_sort = gr.Dropdown(label="Sort", choices=["last_modified", "trending_score", "likes"], value="likes")
-                with gr.Row(equal_height=True):
-                    col_repo_limit = gr.Number(label="Limit for repos", value=50, step=1, minimum=1, maximum=100)
-                    col_user_limit = gr.Number(label="Limit for users", value=10, step=1, minimum=0, maximum=50)
-                with gr.Accordion("Advanced", open=False):
-                    col_show_labels = gr.CheckboxGroup(label="Show items", choices=get_labels(), value=get_valid_labels())
-            col_run_button = gr.Button("Search", variant="primary")
         with gr.Group():
             with gr.Accordion("Filter", open=False):
                 hide_labels = gr.CheckboxGroup(label="Hide items", choices=[], value=[], visible=False)

hfsearch.py

@@ -21,7 +21,7 @@ TYPES_URL = {"model": "https://huggingface.co/models", "dataset": "https://huggi
 TYPES_DESC = " / ".join([f"[{v}={k}]({TYPES_URL.get(k, 'https://hf.co')})" for k, v in zip(list(TYPES_SHORT.keys()), list(TYPES_SHORT.values()))])
 
 RESULT_ITEMS = {
-    "Type": [1, "str", True],
+    "T": [1, "str", True],
     "ID": [2, "markdown", True, "40%"],
     "User": [4, "str", False],
     "Name": [5, "str", False],
@@ -318,7 +318,7 @@ class HFSearchResult():
         elif mode == "PC": self.phone_mode = False
     
     def get_show_labels(self):
-        return ["Type", "ID"] if self.phone_mode else self.show_labels
+        return ["T", "ID"] if self.phone_mode else self.show_labels
 
     def _set(self, data, label: str):
         self.labels.set(label)
@@ -339,7 +339,7 @@ class HFSearchResult():
         elif isinstance(i, PaperInfo): type = "paper"
         elif isinstance(i, Collection): type = "collection"
         else: return
-        self._set(type, "Type")
+        self._set(type, "T")
         self._set("", "Emoji")
         if type in ["space", "model", "dataset"]:
             self._set(i.id, "ID")
@@ -520,18 +520,18 @@ class HFSearchResult():
             if "AllDLs" in columns: sdf = rank_df(sdf, df, "AllDLs")
             if "DLs" in columns: sdf = rank_df(sdf, df, "DLs")
             if "Status" in columns:
-                sdf.loc[df["Status"] == "warm", ["Type", "Status"]] = 'color: orange'
-                sdf.loc[df["Status"] == "cold", ["Type", "Status"]] = 'color: dodgerblue'
+                sdf.loc[df["Status"] == "warm", ["T", "Status"]] = 'color: orange'
+                sdf.loc[df["Status"] == "cold", ["T", "Status"]] = 'color: dodgerblue'
             if "Gated" in columns:
                 sdf.loc[df["Gated"] == "auto", ["Gated"]] = 'color: dodgerblue'
                 sdf.loc[df["Gated"] == "manual", ["Gated"]] = 'color: crimson'
             if "Stage" in columns and "Hardware" in columns:
-                sdf.loc[(df["Stage"] == "RUNNING") & (df["Hardware"] != "zero-a10g") & (df["Hardware"] != "cpu-basic") & (df["Hardware"]), ["Hardware", "Type"]] = 'color: lime'
-                sdf.loc[(df["Stage"] == "RUNNING") & (df["Hardware"] == "zero-a10g"), ["Hardware", "Type"]] = 'color: limegreen'
-                sdf.loc[(df["Type"] == "space") & (df["Stage"] != "RUNNING")] = 'opacity: 0.5'
-                sdf.loc[(df["Type"] == "space") & (df["Stage"] != "RUNNING"), ["Type"]] = 'color: crimson'
+                sdf.loc[(df["Stage"] == "RUNNING") & (df["Hardware"] != "zero-a10g") & (df["Hardware"] != "cpu-basic") & (df["Hardware"]), ["Hardware", "T"]] = 'color: lime'
+                sdf.loc[(df["Stage"] == "RUNNING") & (df["Hardware"] == "zero-a10g"), ["Hardware", "T"]] = 'color: limegreen'
+                sdf.loc[(df["T"] == "space") & (df["Stage"] != "RUNNING")] = 'opacity: 0.5'
+                sdf.loc[(df["T"] == "space") & (df["Stage"] != "RUNNING"), ["T"]] = 'color: crimson'
                 sdf.loc[df["Stage"] == "RUNNING", ["Stage"]] = 'color: lime'
-            if "NFAA" in columns: sdf.loc[df["NFAA"] == "True", ["Type"]] = 'background-color: hotpink'
+            if "NFAA" in columns: sdf.loc[df["NFAA"] == "True", ["T"]] = 'background-color: hotpink'
             show_columns = x.copy().columns
             style_columns = sdf.columns
             drop_columns = [c for c in style_columns if c not in show_columns]
@@ -540,24 +540,24 @@ class HFSearchResult():
         
         def id_to_md(df: pd.DataFrame, verbose=False):
             columns = list(df.index)
-            if df["Type"] == "collection": id = f'### [{df["User"]}/{df["Name"]}]({df["URL"]}){df["Emoji"]}'
-            elif df["Type"] == "space": id = f'### [{df["Name"]} ({df["ID"]})]({df["URL"]}){df["Emoji"]}'
-            elif df["Type"] == "paper": id = f'### [{df["Name"]} (arxiv:{df["ID"]})]({df["URL"]}){df["Emoji"]}'
+            if df["T"] == "collection": id = f'### [{df["User"]}/{df["Name"]}]({df["URL"]}){df["Emoji"]}'
+            elif df["T"] == "space": id = f'### [{df["Name"]} ({df["ID"]})]({df["URL"]}){df["Emoji"]}'
+            elif df["T"] == "paper": id = f'### [{df["Name"]} (arxiv:{df["ID"]})]({df["URL"]}){df["Emoji"]}'
             else: id = f'### [{df["ID"]}]({df["URL"]}){df["Emoji"]}'
             if verbose:
                 l = []
                 if "NFAA" in columns and df["NFAA"] == "True": l.append('🤐')
                 if "Likes" in columns and df["Likes"] > 0: l.append(f'💕:{df["Likes"]}')
-                if df["Type"] in ["model", "space", "dataset"]:
+                if df["T"] in ["model", "space", "dataset"]:
                     if "Trending" in columns and df["Trending"] > 0: l.append(f'trend:{df["Trending"]}')
-                    if df["Type"] in ["model", "dataset"]:
+                    if df["T"] in ["model", "dataset"]:
                         if "DLs" in columns and df["DLs"] > 0: l.append(f'DL:{df["DLs"]}')
                         if "Gated" in columns and df["Gated"] in ["manual", "auto"]: l.append(f'🔑:{df["Gated"]}')
-                    if df["Type"] == "model":
+                    if df["T"] == "model":
                         if "Status" in columns:
                             if df["Status"] == "warm": l.append(f'inference:🔥')
                             elif df["Status"] == "cold": l.append(f'inference:🧊')
-                    if df["Type"] == "space":
+                    if df["T"] == "space":
                         if "Hardware" in columns and df["Hardware"] in SPACE_HARDWARES and df["Hardware"] != "cpu-basic": l.append(f'{df["Hardware"]}')
                         if "SDK" in columns: l.append(f'{df["SDK"]}')
                         if "Stage" in columns and df["Stage"] in SPACE_STAGES_EMOJI.keys(): l.append(f'{SPACE_STAGES_EMOJI[df["Stage"]]}')
@@ -567,7 +567,7 @@ class HFSearchResult():
         def shorten_type(df: pd.DataFrame, shorten=False):
             if shorten:
                 for k, v in TYPES_SHORT.items():
-                    if df["Type"] == k: return v
+                    if df["T"] == k: return v
 
         def to_emoji(df: pd.DataFrame, label: str, key: str, emoji: str):
             if df[label] == key: return f'{df["Emoji"]}{emoji}' if df["Emoji"] else f' {emoji}'
@@ -582,7 +582,7 @@ class HFSearchResult():
 
         def format_md_df(df: pd.DataFrame, verbose=False):
             df["ID"] = df.apply(id_to_md, axis=1, verbose=verbose)
-            df["Type"] = df.apply(shorten_type, axis=1, shorten=verbose)
+            df["T"] = df.apply(shorten_type, axis=1, shorten=verbose)
             return df
         
         hide_labels = [l for l in labels if l in self.hide_labels or l not in self.get_show_labels()]
@@ -630,7 +630,7 @@ class HFSearchResult():
         if key in SORT_PARAM_TO_ITEM.keys(): key = SORT_PARAM_TO_ITEM[key]
         types = set()
         for i in self.item_list:
-            if "Type" in i.keys(): types.add(i["Type"])
+            if "T" in i.keys(): types.add(i["T"])
         if "paper" in types: return
         if key in ["DLs", "AllDLs"] and ("space" in types or "collection" in types): key = "Likes"
         if not key in self.labels.get()[0]: key = "Likes"
@@ -646,10 +646,11 @@ class HFSearchResult():
         df, labels, label_types = self.get()
         widths = self.labels.get_widths()
         if self.phone_mode:
-            widths["Type"] = "10%"
+            widths["T"] = "10%"
             widths["ID"] = "90%"
         column_widths = [widths[l] for l in labels]
-        if self.phone_mode: labels = None
+        if self.phone_mode:
+            labels = None
         return gr.update(type="pandas", value=df, headers=labels, datatype=label_types, column_widths=column_widths, wrap=True, show_label=False)
 
     def get_gr_hide_labels(self):