Update app103.py

app103.py

@@ -1192,9 +1192,40 @@ if "task_choice" in st.session_state:
                         # Save labeled examples to CSV
                         #new 14/4/2025
                         labeled_examples = []
+                        if classification_type == "Named Entity Recognition (NER)":
+                            labeled_examples = []
+                            for line in response.split('\n'):
+                                if line.strip():
+                                    parts = line.rsplit('Entities:', 1)
+                                    if len(parts) == 2:
+                                        text = parts[0].strip()
+                                        entities = parts[1].strip()
+                                        if text and entities:
+                                            labeled_examples.append({
+                                                'text': text,
+                                                'entities': entities,
+                                                'system_prompt': st.session_state.system_prompt,
+                                                'system_role': st.session_state.system_role,
+                                                'task_type': 'Named Entity Recognition (NER)',
+                                                'Use few-shot example?': 'Yes' if use_few_shot else 'No',
+                                            })
+
+                        #                     #new 22/4/2025
                         # if classification_type == "Named Entity Recognition (NER)":
-                        #     labeled_examples = []
-                        #     for line in response.split('\n'):
+                        #     labeled_examples = [{
+                        #         'ner_output': response.strip(),
+                        #         'system_prompt': st.session_state.system_prompt,
+                        #         'system_role': st.session_state.system_role,
+                        #         'task_type': 'Named Entity Recognition (NER)',
+                        #         'Use few-shot example?': 'Yes' if use_few_shot else 'No',
+                        #     }]
+
+                        # #new 24/4/2025
+                        # labeled_examples = []
+
+                        # if classification_type == "Named Entity Recognition (NER)":
+                        #     # Split response into lines and try to extract the text and entities
+                        #     for line in response.strip().split('\n'):
                         #         if line.strip():
                         #             parts = line.rsplit('Entities:', 1)
                         #             if len(parts) == 2:
@@ -1209,58 +1240,27 @@ if "task_choice" in st.session_state:
                         #                         'task_type': 'Named Entity Recognition (NER)',
                         #                         'Use few-shot example?': 'Yes' if use_few_shot else 'No',
                         #                     })
+                        # ###newnewnewnew
+                        # labeled_examples = []
 
-                        #                     #new 22/4/2025
                         # if classification_type == "Named Entity Recognition (NER)":
-                        #     labeled_examples = [{
+                        #     ner_entities = []
+                        #     for line in response.strip().split('\n'):
+                        #         if line.strip():
+                        #             if '-' in line:
+                        #                 entity_text, entity_type = line.rsplit('-', 1)
+                        #                 ner_entities.append({
+                        #                     'entity': entity_text.strip(),
+                        #                     'label': entity_type.strip()
+                        #                 })
+                        #      labeled_examples = [{
                         #         'ner_output': response.strip(),
+                        #         'entities': ner_entities,
                         #         'system_prompt': st.session_state.system_prompt,
                         #         'system_role': st.session_state.system_role,
                         #         'task_type': 'Named Entity Recognition (NER)',
                         #         'Use few-shot example?': 'Yes' if use_few_shot else 'No',
-                        #     }]
-
-                        #new 24/4/2025
-                        labeled_examples = []
-
-                        if classification_type == "Named Entity Recognition (NER)":
-                            # Split response into lines and try to extract the text and entities
-                            for line in response.strip().split('\n'):
-                                if line.strip():
-                                    parts = line.rsplit('Entities:', 1)
-                                    if len(parts) == 2:
-                                        text = parts[0].strip()
-                                        entities = parts[1].strip()
-                                        if text and entities:
-                                            labeled_examples.append({
-                                                'text': text,
-                                                'entities': entities,
-                                                'system_prompt': st.session_state.system_prompt,
-                                                'system_role': st.session_state.system_role,
-                                                'task_type': 'Named Entity Recognition (NER)',
-                                                'Use few-shot example?': 'Yes' if use_few_shot else 'No',
-                                            })
-                        ###newnewnewnew
-                        labeled_examples = []
-
-                        if classification_type == "Named Entity Recognition (NER)":
-                            ner_entities = []
-                            for line in response.strip().split('\n'):
-                                if line.strip():
-                                    if '-' in line:
-                                        entity_text, entity_type = line.rsplit('-', 1)
-                                        ner_entities.append({
-                                            'entity': entity_text.strip(),
-                                            'label': entity_type.strip()
-                                        })
-                             labeled_examples = [{
-                                'ner_output': response.strip(),
-                                'entities': ner_entities,
-                                'system_prompt': st.session_state.system_prompt,
-                                'system_role': st.session_state.system_role,
-                                'task_type': 'Named Entity Recognition (NER)',
-                                'Use few-shot example?': 'Yes' if use_few_shot else 'No',
-                            }]  
+                        #     }]  
                             
                         
 
@@ -1278,24 +1278,8 @@ if "task_choice" in st.session_state:
                                             ######
                        
                                             
-                        # else:
-                        #     labeled_examples = []
-                        #     for line in response.split('\n'):
-                        #         if line.strip():
-                        #             parts = line.rsplit('Label:', 1)
-                        #             if len(parts) == 2:
-                        #                 text = parts[0].strip()
-                        #                 label = parts[1].strip()
-                        #                 if text and label:
-                        #                     labeled_examples.append({
-                        #                         'text': text,
-                        #                         'label': label,
-                        #                         'system_prompt': st.session_state.system_prompt,
-                        #                         'system_role': st.session_state.system_role,
-                        #                         'task_type': 'Data Labeling',
-                        #                         'Use few-shot example?': 'Yes' if use_few_shot else 'No',  
-                        #                     })
                         else:
+                            labeled_examples = []
                             for line in response.split('\n'):
                                 if line.strip():
                                     parts = line.rsplit('Label:', 1)
@@ -1311,29 +1295,46 @@ if "task_choice" in st.session_state:
                                                 'task_type': 'Data Labeling',
                                                 'Use few-shot example?': 'Yes' if use_few_shot else 'No',  
                                             })
+                        # else:
+                        #     for line in response.split('\n'):
+                        #         if line.strip():
+                        #             parts = line.rsplit('Label:', 1)
+                        #             if len(parts) == 2:
+                        #                 text = parts[0].strip()
+                        #                 label = parts[1].strip()
+                        #                 if text and label:
+                        #                     labeled_examples.append({
+                        #                         'text': text,
+                        #                         'label': label,
+                        #                         'system_prompt': st.session_state.system_prompt,
+                        #                         'system_role': st.session_state.system_role,
+                        #                         'task_type': 'Data Labeling',
+                        #                         'Use few-shot example?': 'Yes' if use_few_shot else 'No',  
+                        #                     })
                                             
                     
-                       # # Save and provide download options
-                       #  if labeled_examples:
-                       #      # Update session state
-                       #      st.session_state.labeled_examples = labeled_examples
+                       # Save and provide download options
+                        if labeled_examples:
+                            # Update session state
+                            st.session_state.labeled_examples = labeled_examples
                         
-                       #      # Convert to CSV and JSON
-                       #      df = pd.DataFrame(labeled_examples)
+                            # Convert to CSV and JSON
+                            df = pd.DataFrame(labeled_examples)
 
-                        # ########3
-                        # if labeled_examples:
-                        #     df = pd.DataFrame(labeled_examples)
+                        ########3
+                        if labeled_examples:
+                            df = pd.DataFrame(labeled_examples)
                             
-                        #     csv = df.to_csv(index=False).encode('utf-8')
+                            csv = df.to_csv(index=False).encode('utf-8')
                             
-                        #     st.download_button(
-                        #         label="📥 Download Labeled Examples CSV",
-                        #         data=csv,
-                        #         file_name='labeled_examples.csv',
-                        #         mime='text/csv'
-                        #     )
-                            #  "domain": domain,
+                            st.download_button(
+                                label="📥 Download Labeled Examples CSV",
+                                data=csv,
+                                file_name='labeled_examples.csv',
+                                mime='text/csv'
+                            )
+                            
+                            #"domain": domain,
                             #         "labels": labels,
                             #         "used_few_shot": use_few_shot,
                             #         "task_###########
@@ -1354,22 +1355,22 @@ if "task_choice" in st.session_state:
                         if labeled_examples:
                             st.session_state.labeled_examples = labeled_examples
                         
-                            if classification_type == "Named Entity Recognition (NER)":
-                                # Flatten NER entities for CSV
-                                flat_data = []
-                                for example in labeled_examples:
-                                    for ent in example.get('entities', []):
-                                        flat_data.append({
-                                            'entity': ent['entity'],
-                                            'label': ent['label'],
-                                            'system_prompt': example['system_prompt'],
-                                            'system_role': example['system_role'],
-                                            'task_type': example['task_type'],
-                                            'Use few-shot example?': example['Use few-shot example?']
-                                        })
-                                df = pd.DataFrame(flat_data)
-                            else:
-                                df = pd.DataFrame(labeled_examples)
+                            # if classification_type == "Named Entity Recognition (NER)":
+                            #     # Flatten NER entities for CSV
+                            #     flat_data = []
+                            #     for example in labeled_examples:
+                            #         for ent in example.get('entities', []):
+                            #             flat_data.append({
+                            #                 'entity': ent['entity'],
+                            #                 'label': ent['label'],
+                            #                 'system_prompt': example['system_prompt'],
+                            #                 'system_role': example['system_role'],
+                            #                 'task_type': example['task_type'],
+                            #                 'Use few-shot example?': example['Use few-shot example?']
+                            #             })
+                            #     df = pd.DataFrame(flat_data)
+                            # else:
+                            #     df = pd.DataFrame(labeled_examples)
                         
                             # CSV
                             st.session_state.labeled_examples_csv = df.to_csv(index=False).encode('utf-8')