Update components/animation_engineer.py

components/animation_engineer.py

@@ -1,8 +1,7 @@
 import streamlit as st
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-import pandas as pd
-import numpy as np
+import time
+from PIL import Image, ImageDraw, ImageFont
+import io
 
 class AnimationEngineer:
     def __init__(self):
@@ -14,105 +13,87 @@ class AnimationEngineer:
             'physiotherapy': '#FFD700',  # Gold
             'cognitive therapy': '#DDA0DD'  # Plum
         }
+        self.frame_width = 600
+        self.frame_height = 400
 
-    def display_care_plan_animation(self, final_plan, patient_data):
+    def display_care_plan_animation(self, final_plan):
         st.write("Visualizing Care Plan...")
         steps = final_plan.split(", ")
         
-        # Create a DataFrame for the care plan steps
-        df = pd.DataFrame({
-            'Step': range(1, len(steps) + 1),
-            'Description': steps,
-            'Value': np.random.randint(50, 100, len(steps))  # Random values for visualization
-        })
-
-        # Create the main figure with subplots
-        fig = make_subplots(
-            rows=2, cols=2,
-            subplot_titles=("Care Plan Progress", "Patient Vitals", "Treatment Effectiveness", "Overall Health Score"),
-            specs=[[{"type": "indicator"}, {"type": "scatter"}],
-                   [{"type": "bar"}, {"type": "indicator"}]]
-        )
-
-        # Care Plan Progress Indicator
-        fig.add_trace(go.Indicator(
-            mode="gauge+number",
-            value=0,
-            title={'text': "Care Plan Progress"},
-            gauge={'axis': {'range': [None, len(steps)]},
-                   'steps': [{'range': [0, len(steps)], 'color': "lightgray"}],
-                   'threshold': {'line': {'color': "red", 'width': 4}, 'thickness': 0.75, 'value': len(steps)}},
-            domain={'row': 0, 'column': 0}
-        ))
-
-        # Patient Vitals Line Chart
-        vitals = np.random.rand(10) * 10 + 90  # Simulated vital signs data
-        fig.add_trace(go.Scatter(
-            x=list(range(10)), y=vitals, mode='lines+markers', name='Vitals',
-            line=dict(color='royalblue', width=2)
-        ), row=1, col=2)
-
-        # Treatment Effectiveness Bar Chart
-        fig.add_trace(go.Bar(
-            x=df['Step'], y=df['Value'], name='Effectiveness',
-            marker_color=[self.colors.get(step.split()[0], '#FFFFFF') for step in df['Description']]
-        ), row=2, col=1)
-
-        # Overall Health Score Indicator
-        fig.add_trace(go.Indicator(
-            mode="gauge+number",
-            value=75,  # Starting value
-            title={'text': "Overall Health Score"},
-            gauge={'axis': {'range': [None, 100]},
-                   'steps': [{'range': [0, 50], 'color': "lightgray"},
-                             {'range': [50, 75], 'color': "gray"}],
-                   'threshold': {'line': {'color': "red", 'width': 4}, 'thickness': 0.75, 'value': 90}},
-            domain={'row': 1, 'column': 1}
-        ))
-
-        # Update layout
-        fig.update_layout(height=800, showlegend=False)
-
-        # Display the initial plot
-        plot_placeholder = st.empty()
-        plot_placeholder.plotly_chart(fig, use_container_width=True)
-
-        # Animate the care plan steps
-        for i in range(len(steps)):
-            # Update Care Plan Progress
-            fig.data[0].value = i + 1
-
-            # Update Patient Vitals
-            new_vital = np.random.rand() * 10 + 90
-            fig.data[1].y = list(fig.data[1].y) + [new_vital]
-            fig.data[1].x = list(range(len(fig.data[1].y)))
-
-            # Update Treatment Effectiveness
-            fig.data[2].marker.color = [self.colors.get(step.split()[0], '#FFFFFF') if idx <= i else 'lightgray' 
-                                        for idx, step in enumerate(df['Description'])]
-
-            # Update Overall Health Score
-            fig.data[3].value = min(100, fig.data[3].value + np.random.randint(-5, 10))
-
-            # Update the plot
-            plot_placeholder.plotly_chart(fig, use_container_width=True)
-            
-            # Add a short pause between updates
-            st.empty().text(f"Step {i+1}: {steps[i]}")
-            st.empty().text(f"Processing...")
-
+        # Create all frames
+        frames = self.create_all_frames(steps)
+        
+        # Create a placeholder for our animation
+        animation_placeholder = st.empty()
+        
+        # Display frames
+        for i, frame in enumerate(frames):
+            animation_placeholder.image(frame)
+            progress = int((i + 1) / len(frames) * 100)
+            st.progress(progress)
+            time.sleep(0.5)  # Adjust speed of animation
+        
         st.success("Care Plan Visualization Complete!")
         st.balloons()
 
-    def parse_patient_data(self, patient_data):
-        # This is a placeholder. In a real scenario, you would parse the actual patient data.
-        return {
-            'condition': patient_data.get('condition', 'Unknown'),
-            'age': patient_data.get('age', 50),
-            'gender': patient_data.get('gender', 'Unknown'),
-            'vitals': {
-                'heart_rate': np.random.randint(60, 100),
-                'blood_pressure': f"{np.random.randint(100, 140)}/{np.random.randint(60, 90)}",
-                'temperature': round(np.random.uniform(36.1, 37.2), 1)
-            }
-        }
+    def create_all_frames(self, steps):
+        frames = []
+        
+        # Create intro frame
+        intro_frame = self.create_intro_frame()
+        frames.append(intro_frame)
+        
+        # Create step frames
+        for i, step in enumerate(steps):
+            step_frame = self.create_step_frame(step, i+1, len(steps))
+            frames.append(step_frame)
+        
+        # Create transition frames
+        transition_frames = self.create_transition_frames()
+        frames.extend(transition_frames)
+        
+        # Create final frame
+        final_frame = self.create_final_frame()
+        frames.append(final_frame)
+        
+        return frames
+
+    def create_intro_frame(self):
+        img = Image.new('RGB', (self.frame_width, self.frame_height), color='white')
+        d = ImageDraw.Draw(img)
+        font = ImageFont.load_default()
+        d.text((self.frame_width//2, self.frame_height//2), "Care Plan Animation", fill='black', font=font, anchor="mm")
+        return img
+
+    def create_step_frame(self, step, step_num, total_steps):
+        img = Image.new('RGB', (self.frame_width, self.frame_height), color='white')
+        d = ImageDraw.Draw(img)
+        
+        color = next((self.colors[key] for key in self.colors if key.lower() in step.lower()), '#FFFFFF')
+        
+        d.rectangle([50, 50, self.frame_width-50, self.frame_height-50], fill=color, outline='black')
+        
+        font = ImageFont.load_default()
+        d.text((self.frame_width//2, 70), f"Step {step_num}/{total_steps}", fill='black', font=font, anchor="mm")
+        d.text((self.frame_width//2, self.frame_height//2), step, fill='black', font=font, anchor="mm")
+        
+        return img
+
+    def create_transition_frames(self):
+        frames = []
+        for i in range(10):  # 10 transition frames
+            img = Image.new('RGB', (self.frame_width, self.frame_height), color='white')
+            d = ImageDraw.Draw(img)
+            size = 20 + i * 5  # Growing circle
+            d.ellipse([self.frame_width//2 - size, self.frame_height//2 - size, 
+                       self.frame_width//2 + size, self.frame_height//2 + size], 
+                      fill='red')
+            frames.append(img)
+        return frames
+
+    def create_final_frame(self):
+        img = Image.new('RGB', (self.frame_width, self.frame_height), color='white')
+        d = ImageDraw.Draw(img)
+        font = ImageFont.load_default()
+        d.text((self.frame_width//2, self.frame_height//2), "Care Plan Complete!", fill='black', font=font, anchor="mm")
+        return img