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2d14fa3
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1 Parent(s): 5414405

Update app.py

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Files changed (1) hide show
  1. app.py +10 -3
app.py CHANGED
@@ -5,7 +5,6 @@ import os
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  import re
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  import gradio as gr
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  from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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- from labels_config import custom_label_definitions
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  # --- 1. MODELLERİN HAZIRLANMASI ---
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  ner_pipe = pipeline("ner", model="xlm-roberta-large-finetuned-conll03-english", aggregation_strategy="simple", device=-1)
@@ -127,7 +126,15 @@ def process_ner(text):
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  final_results = []
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  yield "<br>".join(current_logs), []
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-
 
 
 
 
 
 
 
 
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  initial_results = ner_pipe(text)
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@@ -145,7 +152,7 @@ def process_ner(text):
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  wiki_text = get_wiki_summary(word)
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  if wiki_text:
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  current_logs.append(f"<div style='margin-left:20px; border-left:2px solid #007bff; padding-left:10px; color:#555;'>🌐 <b>Wiki:</b> {wiki_text}</div>")
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- new_label, cot_text = refine_label_with_llm(word, text, wiki_text, custom_labels_definitions)
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  current_logs.append(f"<div style='margin-left:20px; border-left:2px solid #28a745; padding-left:10px;'>🧠 <b>CoT Analizi:</b> {cot_text}</div>")
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  res = {"Varlık": word, "İlk Etiket": label, "Nihai Etiket": new_label, "Kaynak": "RAG+LLM"}
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  else:
 
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  import re
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  import gradio as gr
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  from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
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  # --- 1. MODELLERİN HAZIRLANMASI ---
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  ner_pipe = pipeline("ner", model="xlm-roberta-large-finetuned-conll03-english", aggregation_strategy="simple", device=-1)
 
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  final_results = []
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  yield "<br>".join(current_logs), []
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+ custom_labels = {
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+ "MOVIE": {"tanim": "Sinematik eserler.", "anahtar_kelimeler": ["film", "dizi"], "ornekler": ["Inception", "Ezel"]},
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+ "BOOK": {"tanim": "Yazılı edebi eserler.", "anahtar_kelimeler": ["kitap", "roman"], "ornekler": ["Sefiller", "Nutuk"]},
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+ "SOFTWARE": {"tanim": "Dijital sistemler.", "anahtar_kelimeler": ["yazılım", "kod"], "ornekler": ["Linux", "Python"]},
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+ "AWARD": {"tanim": "Resmi ödüller.", "anahtar_kelimeler": ["ödül", "oscar"], "ornekler": ["Nobel", "Altın Portakal"]},
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+ "PRODUCT": {"tanim": "Ticari ürünler.", "anahtar_kelimeler": ["marka", "model"], "ornekler": ["iPhone", "Tesla"]},
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+ "NORP": {"tanim": "Etnik ve dini gruplar.", "anahtar_kelimeler": ["millet", "topluluk"], "ornekler": ["Türkler", "Ruslar"]},
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+ "GPE": {"tanim": "Siyasi bölgeler.", "anahtar_kelimeler": ["ülke", "şehir"], "ornekler": ["Türkiye", "İstanbul"]}
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+ }
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  initial_results = ner_pipe(text)
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  wiki_text = get_wiki_summary(word)
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  if wiki_text:
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  current_logs.append(f"<div style='margin-left:20px; border-left:2px solid #007bff; padding-left:10px; color:#555;'>🌐 <b>Wiki:</b> {wiki_text}</div>")
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+ new_label, cot_text = refine_label_with_llm(word, text, wiki_text, custom_labels)
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  current_logs.append(f"<div style='margin-left:20px; border-left:2px solid #28a745; padding-left:10px;'>🧠 <b>CoT Analizi:</b> {cot_text}</div>")
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  res = {"Varlık": word, "İlk Etiket": label, "Nihai Etiket": new_label, "Kaynak": "RAG+LLM"}
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  else: