first classification try

app.py

@@ -1,7 +1,64 @@
 import gradio as gr
+import numpy as np
+import pickle
+from sentence_transformers import SentenceTransformer
 
-def greet(name):
-    return ("Hello " + name + "!!", "clustering tbd")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs=["text", "text"])
-iface.launch()
+#css_code='body {background-image:url("https://picsum.photos/seed/picsum/200/300");} div.gradio-container {background: white;}'
+
+
+categories = ["Censorship","Development","Digital Activism","Disaster","Economics & Business","Education","Environment","Governance","Health","History","Humanitarian Response","International Relations","Law","Media & Journalism","Migration & Immigration","Politics","Protest","Religion","Sport","Travel","War & Conflict","Technology_Science","Women&Gender_LGBTQ+_Youth","Freedom_of_Speech_Human_Rights","Literature_Arts&Culture"]
+model = SentenceTransformer('sentence-transformers/LaBSE')
+with open('models/MLP_classifier_average_en.pkl', 'rb') as f:
+    classifier = pickle.load(f)
+
+def get_embedding(text):
+    if text is None:
+        text = ""
+    return model.encode(text)
+
+def get_categories(y_pred):
+    indices = []
+    for idx, value in enumerate(y_pred):
+        if value == 1:
+            indices.append(idx)
+    cats = [categories[i] for i in indices]
+    return cats
+
+def generate_output(article):
+    paragraphs = article.split("\n")
+    embdds = []
+    for par in paragraphs:
+        embdds.append(get_embedding(par))
+    embedding = np.average(embdds, axis=0)
+
+    #y_pred = classifier.predict_proba(embedding.reshape(1, 768))
+    y_pred = classifier.predict(embedding.reshape(1, 768))
+    y_pred = y_pred.flatten()
+    classes = get_categories(y_pred)
+
+    return (classes, "clustering tbd")
+
+# with gr.Blocks() as demo:
+#     with gr.Row():
+#         # column for input
+#         with gr.Column():
+#             input_text = gr.Textbox(lines=6, placeholder="Insert text of the article here...", label="Article"),
+#             submit_button = gr.Button("Submit")
+#             clear_button = gr.Button("Clear") 
+
+#         # column for output
+#         with gr.Column():
+#             output_classification = gr.Textbox(lines=1, label="Article category")
+#             output_topic_discovery = gr.Textbox(lines=5, label="Topic discovery")
+
+    #submit_button.click(generate_output, inputs=input_text, outputs=[output_classification, output_topic_discovery])
+demo = gr.Interface(fn=generate_output, 
+    inputs=gr.Textbox(lines=6, placeholder="Insert text of the article here...", label="Article"), 
+    outputs=[gr.Textbox(lines=1, label="Category"), gr.Textbox(lines=5, label="Topic discovery")], 
+    title="Article classification & topic discovery demo",
+    flagging_options=["Incorrect"],
+    theme=gr.themes.Base())
+    #css=css_code)
+
+demo.launch()

flagged/log.csv

@@ -0,0 +1,4 @@
+name,output 0,output 1,flag,username,timestamp
+test,Hello test!!,clustering tbd,,,2023-04-13 09:59:56.971579
+,,,,,2023-04-14 18:44:46.346018
+,,,Incorrect,,2023-04-14 18:48:06.029759

models/MLP_classifier_average_en.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ee563cb660f18a2d58d4b8790f02b68bc33e6c98c90cf890d1191a27c5b25a9
+size 354644961