Update app.py

app.py

@@ -1,3 +1,72 @@
+import os
 import gradio as gr
+from gradio import FlaggingCallback
+from gradio.components import IOComponent
 
-gr.Interface.load("huggingface/deprem-ml/deprem-ner").launch()
+from transformers import pipeline
+
+from typing import List, Optional, Any
+
+import argilla as rg
+
+nlp = pipeline("ner", model="deprem-ml/deprem-ner")
+
+examples = [
+  ["Lütfen yardım Akevler mahallesi Rüzgar sokak Tuncay apartmanı zemin kat Antakya akrabalarım göçük altında #hatay #Afad"]
+]
+
+def create_record(input_text):
+    # Making the prediction
+    predictions = nlp(input_text, aggregation_strategy="first")
+    
+    # Creating the predicted entities as a list of tuples (entity, start_char, end_char, score)
+    prediction = [(pred["entity_group"], pred["start"], pred["end"], pred["score"]) for pred in predictions]
+    
+    # Create word tokens
+    batch_encoding = nlp.tokenizer(input_text)
+    word_ids = sorted(set(batch_encoding.word_ids()) - {None})
+    words = []
+    for word_id in word_ids:
+        char_span = batch_encoding.word_to_chars(word_id)
+        words.append(input_text[char_span.start:char_span.end])
+    
+    # Building a TokenClassificationRecord
+    record = rg.TokenClassificationRecord(
+        text=input_text,
+        tokens=words,
+        prediction=prediction,
+        prediction_agent="deprem-ml/deprem-ner",
+    )
+    print(record)
+    return record
+
+class ArgillaLogger(FlaggingCallback):
+    def __init__(self, api_url, api_key, dataset_name):
+        rg.init(api_url=api_url, api_key=api_key)
+        self.dataset_name = dataset_name
+    def setup(self, components: List[IOComponent], flagging_dir: str):
+        pass
+    def flag(
+        self,
+        flag_data: List[Any],
+        flag_option: Optional[str] = None,
+        flag_index: Optional[int] = None,
+        username: Optional[str] = None,
+    ) -> int:
+        text = flag_data[0]
+        inference = flag_data[1]
+        rg.log(name=self.dataset_name, records=create_record(text))
+
+
+        
+gr.Interface.load(
+    "models/deprem-ml/deprem-ner",
+    examples=examples,
+    allow_flagging="manual",
+    flagging_callback=ArgillaLogger(
+        api_url="https://dvilasuero-argilla-template-1-3.hf.space", 
+        api_key="team.apikey", 
+        dataset_name="ner-flags"
+    ),
+    flaggging_options=["Correct", "Incorrect", "Ambiguous"]
+).launch()