Update app.py

app.py

@@ -40,40 +40,19 @@ for uploaded_file in uploaded_files:
         data = pd.Series(text_data, name = 'Text')
         st.dataframe(data)
         frames = [job, data]
-        result1 = pd.concat(frames)
-        st.dataframe(result1)
-
-        
-
+        result = pd.concat(frames)
+        st.dataframe(result)
 
-        model = GLiNER.from_pretrained("urchade/gliner_base")
-        labels = ["person", "country", "organization", "time", "role"]
+        model = GLiNER.from_pretrained("xomad/gliner-model-merge-large-v1.0")
+        labels = ["person", "country", "city", "organization", "date", "money", "percent value", "position"]
         entities = model.predict_entities(text_data, labels)
+        df = pd.DataFrame(entities)
+        st.dataframe(entities)
 
-        entity_dict = {}
-        for label in labels:
-            entity_dict[label] = [entity["text"] for entity in entities if entity["label"] == label]
-
-    
-        data1 = {"Text": text_data, **entity_dict}
-        st.dataframe(data1)
-
-        data = data1.T
-        st.write(data)
         
 
 
-        if data is not None:
-            value_counts1 = data['person'].value_counts()
-                
-            df1 = pd.DataFrame(value_counts1)
-                
-            final_df = df1.reset_index().rename(columns={"index": "label"})
-                
-            
-          
-            fig2 = px.bar(final_df, x="count", y="label", color="label", text_auto=True, title='Occurrences of predicted labels')
-            st.plotly_chart(fig2)
+        
             
         
         vectorizer = TfidfVectorizer()