Update app.py

app.py

@@ -101,52 +101,59 @@ if st.button("Çalıştır") and input_text:
     elif task == "Metin Analizi":
         output = pipeline_model(input_text)
         
-        # Process entities
-        processed_entities = []
-        for entity in output:
-            word = entity['word']
-            label = entity['entity']
-            score = entity['score']
-            start = entity['start']
-            end = entity['end']
-            processed_entities.append({
-                'word': word,
-                'label': label,
-                'score': score,
-                'start': start,
-                'end': end
-            })
-        
-        # Aggregate entities
-        df = pd.DataFrame(process_entities(processed_entities, input_text))
-        st.subheader("Tanımlanan Varlıklar")
-        st.dataframe(df)
-        
-        # Metni formatla
-        def format_text(text_data, original_text):
-            formatted_text = ""
-            last_end = 0
-            for item in text_data:
-                if item['start'] > last_end:
-                    formatted_text += original_text[last_end:item['start']]
-                word = item['word']
-                label = item['label']
-                score = item['score']
-                if label.startswith('I-PER'):
-                    color = 'blue'
-                elif label.startswith('I-MISC'):
-                    color = 'green'
-                else:
-                    color = 'gray'
-                formatted_text += f"<span style='color:{color}; font-weight: bold;'>{word} ({label}, {score:.2f})</span>"
-                last_end = item['end']
-            if last_end < len(original_text):
-                formatted_text += original_text[last_end:]
-            return formatted_text
+        # Çıktıyı gözlemleme
+        st.write(output)
 
-        formatted_text = format_text(process_entities(processed_entities, input_text), input_text)
-        st.subheader("Analiz Edilen Metin")
-        st.markdown(f"<p>{formatted_text}</p>", unsafe_allow_html=True)
+        # Varlıkları işlemek için uygun formatı kontrol edin
+        if len(output) > 0 and 'entity' in output[0]:
+            # Process entities
+            processed_entities = []
+            for entity in output:
+                word = entity['word']
+                label = entity['entity']
+                score = entity['score']
+                start = entity['start']
+                end = entity['end']
+                processed_entities.append({
+                    'word': word,
+                    'label': label,
+                    'score': score,
+                    'start': start,
+                    'end': end
+                })
+
+            # Aggregate entities
+            df = pd.DataFrame(process_entities(processed_entities, input_text))
+            st.subheader("Tanımlanan Varlıklar")
+            st.dataframe(df)
+            
+            # Metni formatla
+            def format_text(text_data, original_text):
+                formatted_text = ""
+                last_end = 0
+                for item in text_data:
+                    if item['start'] > last_end:
+                        formatted_text += original_text[last_end:item['start']]
+                    word = item['word']
+                    label = item['label']
+                    score = item['score']
+                    if label.startswith('I-PER'):
+                        color = 'blue'
+                    elif label.startswith('I-MISC'):
+                        color = 'green'
+                    else:
+                        color = 'gray'
+                    formatted_text += f"<span style='color:{color}; font-weight: bold;'>{word} ({label}, {score:.2f})</span>"
+                    last_end = item['end']
+                if last_end < len(original_text):
+                    formatted_text += original_text[last_end:]
+                return formatted_text
+
+            formatted_text = format_text(process_entities(processed_entities, input_text), input_text)
+            st.subheader("Analiz Edilen Metin")
+            st.markdown(f"<p>{formatted_text}</p>", unsafe_allow_html=True)
+        else:
+            st.error("Varlık analizi sonucu beklenen formatta değil.")
     elif task == "Metin Oluşturma":
         output = pipeline_model(input_text, max_length=100, num_return_sequences=1)
         st.subheader("Oluşturulan Metin")