Update app.py

app.py

@@ -6,6 +6,7 @@ import requests
 
 import streamlit
 import spacy
+
 from lxml import etree
 import pandas as pd
 
@@ -27,9 +28,9 @@ ENTITY_COLORS = {
 
 MAP_MODELS = {
     "":"",
-    # "fr_ner4archives_V3_camembert_base": "https://huggingface.co/ner4archives/fr_ner4archives_V3_camembert_base/resolve/main/fr_ner4archives_V3_camembert_base-any-py3-none-any.whl", # Use this only locally (not in HF Spaces)
-    "fr_ner4archives_v3_default": "https://huggingface.co/ner4archives/fr_ner4archives_v3_default/resolve/main/fr_ner4archives_v3_default-any-py3-none-any.whl",
-    "fr_ner4archives_v3_with_vectors":"https://huggingface.co/ner4archives/fr_ner4archives_v3_with_vectors/resolve/main/fr_ner4archives_v3_with_vectors-any-py3-none-any.whl"
+    "fr_ner4archives_V3_camembert_base": "./models_update/fr_ner4archives_V3_camembert_base-0.0.0",
+    "fr_ner4archives_v3_default": "./models_update/fr_ner4archives_v3_default-0.0.0",
+    "fr_ner4archives_v3_with_vectors":"./models_update/fr_ner4archives_v3_with_vectors-0.0.0"
 }
 
 
@@ -132,7 +133,7 @@ def main():
         col2.text_area("Plain Text View (read-only)", value=plain, height=500, disabled=True)
         flag_view = True
         flag_model = False
-
+        nlp = None
         if flag_view:
             streamlit.write("## ⚙️ Configure NER pipeline and options:")
             streamlit.write(
@@ -146,53 +147,17 @@ def main():
             model = option
             model_loaded = None
             if model != "":
-                try:
-                    spacy.load(model)
-                    flag_model = True
-                    streamlit.write(f"{model} is available locally.")
-                except:
-                    placeholder = streamlit.empty()
-                    button = streamlit.button(f"Download model: {model}")
-                    with placeholder.container():
-                        if button:
-                            streamlit.write(f"Download model: {model} in progress...")
-                            p1 = subprocess.Popen(["pip", "install", MAP_MODELS[model]])
-                            o = p1.wait()
-                            if o == 0:
-                                streamlit.write(f"Download model: {model} done.")
-                                flag_model = True
-                                streamlit.write(f"{model} is available locally.")
-                    placeholder.empty()
-
-            if flag_model:
-                #gpu = streamlit.checkbox('Check to use GPU (if available)', value=False)
-                #gpu_icon = "❌"
-                #if gpu:
-                #    spacy.prefer_gpu()
-                #    gpu_icon = "✅️"
-                #else:
-                #    spacy.require_cpu()
-
-                if is_entity_fishing_online():
-                    streamlit.write("Entity-fishing server status: 🟢 (you can use linking feature)")
-                    linking = streamlit.checkbox('Check to apply named entity linking (entity-fishing component)',
-                                                 value=False)
-                    linkingicon = "✅️"
-                    if linking is False:
-                        linkingicon = "❌"
-                else:
-                    streamlit.write("Entity-fishing server status: 🔴 (you can't use linking feature)")
-                    linking = False
-                    linkingicon = "❌"
-                streamlit.write("#### Actual Parameters:")
-                streamlit.write(f'- NER model selected: {option}\n - Linking activated: {linkingicon} - GPU activated: {gpu_icon}')
+                print(MAP_MODELS[model])
+                nlp = spacy.load(MAP_MODELS[model])
+                flag_model = True
+                streamlit.write(f"{model} is available locally.")
 
+        linking = False
         # Launch NER process:
         if flag_model:
             if streamlit.button('Launch'):
                 plain = "\n".join(sentences)
                 with streamlit.spinner('Initialize NER...'):
-                    nlp = spacy.load(model)
                     nlp.max_length = 5000000
                     if linking:
                         nlp.add_pipe('entityfishing',
@@ -266,4 +231,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()