Update app.py

app.py

@@ -40,49 +40,11 @@ monochrome = Monochrome()
 
 auth_token = os.environ['HF_TOKEN']
 
-tokenizer_bin = AutoTokenizer.from_pretrained("AlGe/deberta-v3-large_token", token=auth_token)
-model_bin = AutoModelForTokenClassification.from_pretrained("AlGe/deberta-v3-large_token", token=auth_token)
-tokenizer_bin.model_max_length = 512
-pipe_bin = pipeline("ner", model=model_bin, tokenizer=tokenizer_bin)
-
-tokenizer_ext = AutoTokenizer.from_pretrained("AlGe/deberta-v3-large_AIS-token", token=auth_token)
-model_ext = AutoModelForTokenClassification.from_pretrained("AlGe/deberta-v3-large_AIS-token", token=auth_token)
-tokenizer_ext.model_max_length = 512
-pipe_ext = pipeline("ner", model=model_ext, tokenizer=tokenizer_ext)
-
 model1 = AutoModelForSequenceClassification.from_pretrained("AlGe/deberta-v3-large_Int_segment", num_labels=1, token=auth_token)
 tokenizer1 = AutoTokenizer.from_pretrained("AlGe/deberta-v3-large_Int_segment", token=auth_token)
 
 model2 = AutoModelForSequenceClassification.from_pretrained("AlGe/deberta-v3-large_seq_ext", num_labels=1, token=auth_token)
 
-def process_ner(text: str, pipeline) -> dict:
-    output = pipeline(text)
-    entities = []
-    current_entity = None
-
-    for token in output:
-        entity_type = token['entity'][2:]
-        entity_prefix = token['entity'][:1]
-
-        if current_entity is None or entity_type != current_entity['entity'] or (entity_prefix == 'B' and entity_type == current_entity['entity']):
-            if current_entity is not None:
-                entities.append(current_entity)
-            current_entity = {
-                "entity": entity_type,
-                "start": token['start'],
-                "end": token['end'],
-                "score": token['score'],
-                "tokens": [token['word']]
-            }
-        else:
-            current_entity['end'] = token['end']
-            current_entity['score'] = max(current_entity['score'], token['score'])
-            current_entity['tokens'].append(token['word'])
-
-    if current_entity is not None:
-        entities.append(current_entity)
-
-    return {"text": text, "entities": entities}
 
 def process_classification(text: str, model1, model2, tokenizer1) -> Tuple[str, str, str]:
     inputs1 = tokenizer1(text, max_length=512, return_tensors='pt', truncation=True, padding=True)
@@ -97,110 +59,11 @@ def process_classification(text: str, model1, model2, tokenizer1) -> Tuple[str,
 
     return f"{round(prediction1, 1)}", f"{round(prediction2, 1)}", f"{round(score, 2)}"
 
-
-def generate_charts(ner_output_bin: dict, ner_output_ext: dict) -> Tuple[go.Figure, go.Figure, np.ndarray]:
-    entities_bin = [entity['entity'] for entity in ner_output_bin['entities']]
-    entities_ext = [entity['entity'] for entity in ner_output_ext['entities']]
-    
-    # Counting entities for binary classification
-    entity_counts_bin = {entity: entities_bin.count(entity) for entity in set(entities_bin)}
-    bin_labels = list(entity_counts_bin.keys())
-    bin_sizes = list(entity_counts_bin.values())
-    
-    # Counting entities for extended classification
-    entity_counts_ext = {entity: entities_ext.count(entity) for entity in set(entities_ext)}
-    ext_labels = list(entity_counts_ext.keys())
-    ext_sizes = list(entity_counts_ext.values())
-
-    bin_color_map = {
-        "External": "#6ad5bc",
-        "Internal": "#ee8bac"
-    }
-
-    ext_color_map = {
-        "INTemothou": "#FF7F50",  # Coral
-        "INTpercept": "#FF4500",  # OrangeRed
-        "INTtime": "#FF6347",     # Tomato
-        "INTplace": "#FFD700",    # Gold
-        "INTevent": "#FFA500",    # Orange
-        "EXTsemantic": "#4682B4", # SteelBlue
-        "EXTrepetition": "#5F9EA0", # CadetBlue
-        "EXTother": "#00CED1",    # DarkTurquoise
-    }
-
-    bin_colors = [bin_color_map.get(label, "#FFFFFF") for label in bin_labels]
-    ext_colors = [ext_color_map.get(label, "#FFFFFF") for label in ext_labels]
-    
-    # Create pie chart for extended classification
-    fig1 = go.Figure(data=[go.Pie(labels=ext_labels, values=ext_sizes, textinfo='label+percent', hole=.3, marker=dict(colors=ext_colors))])
-    fig1.update_layout(
-        template='plotly_dark',
-        plot_bgcolor='rgba(0,0,0,0)',
-        paper_bgcolor='rgba(0,0,0,0)'
-    )
-    
-    # Create bar chart for binary classification
-    fig2 = go.Figure(data=[go.Bar(x=bin_labels, y=bin_sizes, marker=dict(color=bin_colors))])
-    fig2.update_layout(
-        xaxis_title='Entity Type',
-        yaxis_title='Count',
-        template='plotly_dark',
-        plot_bgcolor='rgba(0,0,0,0)',
-        paper_bgcolor='rgba(0,0,0,0)'
-    )
-    
-    # Generate word cloud
-    wordcloud_image = generate_wordcloud(ner_output_ext['entities'], ext_color_map)
-    
-    return fig1, fig2, wordcloud_image
-
-
-def generate_wordcloud(entities: List[Dict], color_map: Dict[str, str]) -> np.ndarray:
-    token_texts = []
-    token_scores = []
-    token_types = []
-
-    for entity in entities:
-        for token in entity['tokens']:
-            token_texts.append(token)
-            token_scores.append(entity['score'])
-            token_types.append(entity['entity'])
-
-    # Create a dictionary for word cloud
-    word_freq = {text: score for text, score in zip(token_texts, token_scores)}
-
-    def color_func(word, font_size, position, orientation, random_state=None, **kwargs):
-        entity_type = next((t for t, w in zip(token_types, token_texts) if w == word), None)
-        return color_map.get(entity_type, "#FFFFFF")
-
-    wordcloud = WordCloud(width=800, height=400, background_color='#121212', color_func=color_func).generate_from_frequencies(word_freq)
-
-    # Convert to image array
-    plt.figure(figsize=(10, 5))
-    plt.imshow(wordcloud, interpolation='bilinear')
-    plt.axis('off')
-    plt.tight_layout(pad=0)
-
-    # Convert plt to numpy array
-    plt_image = plt.gcf()
-    plt_image.canvas.draw()
-    image_array = np.frombuffer(plt_image.canvas.tostring_rgb(), dtype=np.uint8)
-    image_array = image_array.reshape(plt_image.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-
-    return image_array
-
 @spaces.GPU
 def all(text: str):
-    ner_output_bin = process_ner(text, pipe_bin)
-    ner_output_ext = process_ner(text, pipe_ext)
     classification_output = process_classification(text, model1, model2, tokenizer1)
-    
-    pie_chart, bar_chart, wordcloud_image = generate_charts(ner_output_bin, ner_output_ext)
-    
-    return (ner_output_bin, ner_output_ext, 
-            classification_output[0], classification_output[1], classification_output[2],
-            pie_chart, bar_chart, wordcloud_image)
+
+    return (classification_output[0], classification_output[1], classification_output[2])
 
 examples = [
     ['Bevor ich meinen Hund kaufte bin ich immer alleine durch den Park gelaufen. Gestern war ich aber mit dem Hund losgelaufen. Das Wetter war sehr schön, nicht wie sonst im Winter. Ich weiß nicht genau. Mir fällt sonst nichts dazu ein. Wir trafen auf mehrere Spaziergänger. Ein Mann mit seinem Kind. Das Kind hat ein Eis gegessen.'],
@@ -210,29 +73,9 @@ iface = gr.Interface(
     fn=all,
     inputs=gr.Textbox(lines=5, label="Input Text", placeholder="Write about how your breakfast went or anything else that happened or might happen to you ..."),
     outputs=[
-        gr.HighlightedText(label="Binary Sequence Classification",
-                           color_map={
-                               "External": "#6ad5bcff",
-                               "Internal": "#ee8bacff"}
-                          ),
-        gr.HighlightedText(label="Extended Sequence Classification",
-                           color_map={
-                               "INTemothou": "#FF7F50",  # Coral
-                               "INTpercept": "#FF4500",  # OrangeRed
-                               "INTtime": "#FF6347",     # Tomato
-                               "INTplace": "#FFD700",    # Gold
-                               "INTevent": "#FFA500",    # Orange
-                               "EXTsemantic": "#4682B4", # SteelBlue
-                               "EXTrepetition": "#5F9EA0", # CadetBlue
-                               "EXTother": "#00CED1",    # DarkTurquoise
-                           }
-                          ),
         gr.Label(label="Internal Detail Count"),
         gr.Label(label="External Detail Count"),
         gr.Label(label="Approximated Internal Detail Ratio"),
-        gr.Plot(label="Extended SeqClass Entity Distribution Pie Chart"),
-        gr.Plot(label="Binary SeqClass Entity Count Bar Chart"),
-        gr.Image(label="Entity Word Cloud")
     ],
     title="Scoring Demo",
     description="Autobiographical Memory Analysis: This demo combines two text - and two sequence classification models to showcase our automated Autobiographical Interview scoring method. Submit a narrative to see the results.",