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import gradio as gr
import plotly.graph_objs as go
def extract_data_and_explanation(text):
speakers_data = {}
current_speaker = None
explanation = ""
for line in text.split('\n'):
line = line.strip()
if line.startswith("-----------------------"):
if current_speaker and explanation:
speakers_data[current_speaker]["explanation"] = explanation.strip()
explanation = ""
current_speaker = None
continue
if line.startswith("Speaker"):
current_speaker = line.strip()
speakers_data[current_speaker] = {}
elif ':' in line and current_speaker:
key, value = line.split(':', 1)
key = key.strip()
value = value.strip()
if key.lower() == "explanation":
explanation += value + " "
else:
try:
speakers_data[current_speaker][key] = float(value)
except ValueError:
speakers_data[current_speaker][key] = value
elif line and current_speaker and not line.startswith("Explanation"):
explanation += line + " "
if current_speaker and explanation:
speakers_data[current_speaker]["explanation"] = explanation.strip()
return speakers_data
def create_bar_chart(data, title):
if not data:
return None
fig = go.Figure(data=[go.Bar(
x=list(data.keys()),
y=list(data.values()),
text=list(data.values()),
textposition='auto',
marker_color=['red', 'green', 'blue', 'yellow', 'purple', 'orange', 'pink', 'cyan', 'magenta', 'brown'][:len(data)]
)])
fig.update_layout(title=title, xaxis_title="Traits", yaxis_title="Score")
fig.update_xaxes(tickangle=45)
return fig
def create_radar_chart(data, title):
if not data:
return None
values = [data.get('Avoidance', 0), data.get('Self', 0), data.get('Anxiety', 0), data.get('Others', 0)]
fig = go.Figure(data=go.Scatterpolar(
r=values,
theta=['Avoidance', 'Self', 'Anxiety', 'Others'],
fill='toself'
))
fig.update_layout(
polar=dict(
radialaxis=dict(visible=True, range=[0, max(values + [10])])
),
showlegend=False,
title=title
)
return fig
def update_visibility_and_charts(status, exec_time, lang, transcription, attachments, bigfive, personalities):
outputs = [
gr.update(value=status, visible=True),
gr.update(value=exec_time, visible=True),
gr.update(value=lang, visible=True),
gr.update(value=transcription, visible=True),
]
all_analyses = [
("Attachments", attachments),
("Big Five", bigfive),
("Personalities", personalities)
]
all_speakers = set()
for _, analysis_text in all_analyses:
all_speakers.update(extract_data_and_explanation(analysis_text).keys())
for speaker_index, speaker in enumerate(sorted(all_speakers)[:3]): # Limit to 3 speakers
speaker_outputs = []
for analysis_type, analysis_text in all_analyses:
speakers_data = extract_data_and_explanation(analysis_text)
data = speakers_data.get(speaker, {})
if data:
if analysis_type == "Attachments":
chart_data = {k: v for k, v in data.items() if k in ["Secured", "Anxious-Preoccupied", "Dismissive-Avoidant", "Fearful-Avoidant"] and isinstance(v, (int, float))}
speaker_outputs.append(gr.update(value=create_bar_chart(chart_data, f"{analysis_type} Analysis - {speaker}") if chart_data else None, visible=bool(chart_data)))
radar_data = {k: v for k, v in data.items() if k in ["Anxiety", "Avoidance", "Self", "Others"] and isinstance(v, (int, float))}
speaker_outputs.append(gr.update(value=create_radar_chart(radar_data, f"Anxiety-Avoidance-Self-Others - {speaker}") if radar_data else None, visible=bool(radar_data)))
else:
chart_data = {k: v for k, v in data.items() if k not in ["explanation"] and isinstance(v, (int, float))}
speaker_outputs.append(gr.update(value=create_bar_chart(chart_data, f"{analysis_type} Analysis - {speaker}") if chart_data else None, visible=bool(chart_data)))
speaker_outputs.append(gr.update(visible=False)) # Placeholder for consistency with Attachments
explanation = data.get("explanation", "No explanation provided.")
speaker_outputs.append(gr.update(value=explanation, visible=True, label=f"{analysis_type} Explanation - {speaker}"))
else:
speaker_outputs.extend([gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)])
outputs.extend(speaker_outputs)
# Hide unused speaker components
for _ in range(3 - len(all_speakers)):
outputs.extend([gr.update(visible=False)] * 9) # 3 analyses * (2 charts + 1 explanation)
return outputs |