up

app.py

@@ -22,7 +22,7 @@ from transformers.models.auto.modeling_auto import (
 
 audio_models = list(MODEL_FOR_CTC_MAPPING_NAMES.keys()) + list(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES.keys()) + list(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.keys())
 
-vision_models = list(MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.keys()) + list(MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES.keys()) + list(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES.keys()) + list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES.keys()) + list(MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES.keys()) + list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES.keys())
+vision_models = ["clip"] + list(MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.keys()) + list(MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES.keys()) + list(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES.keys()) + list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES.keys()) + list(MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES.keys()) + list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES.keys())
 
 today = datetime.date.today()
 year, week, _ = today.isocalendar()
@@ -33,6 +33,10 @@ DATASET_REPO_URL = (
 DATA_FILENAME = f"data_{week}_{year}.csv"
 DATA_FILE = os.path.join("data", DATA_FILENAME)
 
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+print("is none?", HF_TOKEN is None)
+
 
 def retrieve_model_stats():
     hf_api = HfApi()
@@ -86,10 +90,10 @@ def retrieve_model_stats():
     return result
 
 
-repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL)
+repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN)
 
 if not os.path.isfile(DATA_FILE):
-    print("Create datafile...")
+    st.title("You are the first this week!!! Please wait until the new data is generated and written")
     result = retrieve_model_stats()
 
     if not os.path.isfile(DATA_FILE):
@@ -107,7 +111,8 @@ int_downloads = np.array(
     [int(x.replace(",", "")) for x in dataframe["num_downloads"].values]
 )
 
-st.title(f"Transformers stats for year {year} and week {week}")
+st.title(f"Stats for year {year} and week {week}")
+
 # print top 20 downloads
 source = pd.DataFrame(
     {
@@ -144,15 +149,15 @@ bar_chart = (
 st.title("Bottom 20 downloads last 30 days")
 st.altair_chart(bar_chart, use_container_width=True)
 
-# print audio
-df_audio = dataframe[dataframe["modality"] == "audio"]
-audio_int_downloads = np.array(
-    [int(x.replace(",", "")) for x in df_audio["num_downloads"].values]
+# print vision
+df_vision = dataframe[dataframe["modality"] == "vision"]
+vision_int_downloads = np.array(
+    [int(x.replace(",", "")) for x in df_vision["num_downloads"].values]
 )
 source = pd.DataFrame(
     {
-        "Number of total downloads": audio_int_downloads,
-        "Model architecture name": df_audio["model_names"].values,
+        "Number of total downloads": vision_int_downloads,
+        "Model architecture name": df_vision["model_names"].values,
     }
 )
 bar_chart = (
@@ -163,18 +168,18 @@ bar_chart = (
         x=alt.X("Model architecture name", sort=None),
     )
 )
-st.title("Audio downloads last 30 days")
+st.title("Vision downloads last 30 days")
 st.altair_chart(bar_chart, use_container_width=True)
 
-# print vision
-df_vision = dataframe[dataframe["modality"] == "vision"]
-vision_int_downloads = np.array(
-    [int(x.replace(",", "")) for x in df_vision["num_downloads"].values]
+# print audio
+df_audio = dataframe[dataframe["modality"] == "audio"]
+audio_int_downloads = np.array(
+    [int(x.replace(",", "")) for x in df_audio["num_downloads"].values]
 )
 source = pd.DataFrame(
     {
-        "Number of total downloads": vision_int_downloads,
-        "Model architecture name": df_vision["model_names"].values,
+        "Number of total downloads": audio_int_downloads,
+        "Model architecture name": df_audio["model_names"].values,
     }
 )
 bar_chart = (
@@ -185,16 +190,15 @@ bar_chart = (
         x=alt.X("Model architecture name", sort=None),
     )
 )
-st.title("Vision downloads last 30 days")
+st.title("Audio downloads last 30 days")
 st.altair_chart(bar_chart, use_container_width=True)
 
-
 # print all stats
 st.title("All stats last 30 days")
 st.table(dataframe)
 
-st.title("All audio stats last 30 days")
-st.table(dataframe[dataframe["modality"] == "audio"])
-
-st.title("All vision stats last 30 days")
+st.title("Vision stats last 30 days")
 st.table(dataframe[dataframe["modality"] == "vision"])
+
+st.title("Audio stats last 30 days")
+st.table(dataframe[dataframe["modality"] == "audio"])