Update my_model/tabs/results.py

my_model/tabs/results.py

@@ -1,292 +1,48 @@
-import os
-import altair as alt
-import evaluation_config as config
 import streamlit as st
-from PIL import Image
-import pandas as pd
-import random
+from my_model.results.demo import ResultDemonstrator
+from my_model.config import evaluation_config as config 
 
-
-class ResultDemonstrator:
+def run_demo():
     """
-        A class to demonstrate the results of the Knowledge-Based Visual Question Answering (KB-VQA) model.
-
-        Attributes:
-            main_data (pd.DataFrame): Data loaded from an Excel file containing evaluation results.
-            sample_img_pool (list[str]): List of image file names available for demonstration.
-            model_names (list[str]): List of model names as defined in the configuration.
-            model_configs (list[str]): List of model configurations as defined in the configuration.
+    Run the interactive Streamlit demo for visualizing model evaluation results and analysis.
     """
+    st.set_page_config(page_title="Model Evaluation Results and Analyses",
+                       layout="wide",
+                       initial_sidebar_state="expanded")
+    demo = ResultDemonstrator()  # Instantiate the ResultDemonstrator class
+    
+    col1, col2 = st.columns([1, 4])
+
+    with col1:
+        # User selects the evaluation analysis aspect
+        section_type = st.radio("Select Evaluation Aspect", ["Evaluation Results & Analysis", 'Evaluation Samples'])
+
+        # Only show analysis type if the section type is "Evaluation Results & Analysis"
+        if section_type == "Evaluation Results & Analysis":
+            analysis_type = st.radio("Select Type", ["Main & Ablation Results", "Results per Question Category",
+                                                     "Prompt Length (token count) Impact on Performance"], index=2)
+            if analysis_type == "Prompt Length (token count) Impact on Performance":
+                # Based on the selection, other options appear
+                model_name = st.radio("Select Model Size", config.MODEL_NAMES)
+                score_name = st.radio("Select Score Type", ["VQA Score", "Exact Match"])
+
+        elif section_type == 'Evaluation Samples':
+            samples_button = st.button("Generate Random Samples")
+
+    with col2:
+        if section_type == "Evaluation Results & Analysis":
+            if analysis_type == "Prompt Length (token count) Impact on Performance":
+                for conf in config.MODEL_CONFIGURATIONS:
+                    with st.expander(conf):
+                        demo.plot_token_count_vs_scores(conf, model_name, score_name)
+
+            elif analysis_type == "Main & Ablation Results":
+                demo.display_main_results()
+
+            elif analysis_type == "Results per Question Category":
+                demo.display_ablation_results_per_question_category()
+
+        elif section_type == 'Evaluation Samples':
+            if samples_button:
+                demo.show_samples(3)
 
-    def __init__(self) -> None:
-        """
-        Initializes the ResultDemonstrator class by loading the data from an Excel file.
-        """
-        # Load data
-        self.main_data = pd.read_excel('evaluation_results.xlsx', sheet_name="Main Data")
-        self.sample_img_pool = list(os.listdir("demo"))
-        self.model_names = config.MODEL_NAMES
-        self.model_configs = config.MODEL_CONFIGURATIONS
-
-    @staticmethod
-    def display_table(data: pd.DataFrame) -> None:
-        """
-        Displays a DataFrame using Streamlit's dataframe display function.
-
-        Args:
-            data (pd.DataFrame): The data to display.
-        """
-        st.dataframe(data)
-
-    def calculate_and_append_data(self, data_list: list, score_column: str, model_config: str) -> None:
-        """
-        Calculates mean scores by category and appends them to the data list.
-
-        Args:
-            data_list (list): List to append new data rows.
-            score_column (str): Name of the column to calculate mean scores for.
-            model_config (str): Configuration of the model.
-        """
-        if score_column in self.main_data.columns:
-            category_means = self.main_data.groupby('question_category')[score_column].mean()
-            for category, mean_value in category_means.items():
-                data_list.append({
-                    "Category": category,
-                    "Configuration": model_config,
-                    "Mean Value": round(mean_value * 100, 2)
-                })
-
-    def display_ablation_results_per_question_category(self) -> None:
-        """Displays ablation results per question category for each model configuration."""
-
-        score_types = ['vqa', 'vqa_gpt4', 'em', 'em_gpt4']
-        data_lists = {key: [] for key in score_types}
-        column_names = {
-            'vqa': 'vqa_score_{config}',
-            'vqa_gpt4': 'gpt4_vqa_score_{config}',
-            'em': 'exact_match_score_{config}',
-            'em_gpt4': 'gpt4_em_score_{config}'
-        }
-
-        for model_name in config.MODEL_NAMES:
-            for conf in config.MODEL_CONFIGURATIONS:
-                model_config = f"{model_name}_{conf}"
-                for score_type, col_template in column_names.items():
-                    self.calculate_and_append_data(data_lists[score_type],
-                                                   col_template.format(config=model_config),
-                                                   model_config)
-
-        # Process and display results for each score type
-        for score_type, data_list in data_lists.items():
-            df = pd.DataFrame(data_list)
-            results_df = df.pivot(index='Category', columns='Configuration', values='Mean Value').applymap(
-                lambda x: f"{x:.2f}%")
-
-            with st.expander(f"{score_type.upper()} Scores per Question Category and Model Configuration"):
-                self.display_table(results_df)
-
-    def display_main_results(self) -> None:
-        """Displays the main model results from the Scores sheet, these are displayed from the file directly."""
-        main_scores = pd.read_excel('evaluation_results.xlsx', sheet_name="Scores", index_col=0)
-        st.markdown("### Main Model Results (Inclusive of Ablation Experiments)")
-        main_scores.reset_index()
-        self.display_table(main_scores)
-
-    def plot_token_count_vs_scores(self, conf: str, model_name: str, score_name: str = 'VQA Score') -> None:
-        """
-        Plots an interactive scatter plot comparing token counts to VQA or EM scores using Altair.
-
-        Args:
-            conf (str): The configuration name.
-            model_name (str): The name of the model.
-            score_name (str): The type of score to plot.
-        """
-
-        # Construct the full model configuration name
-        model_configuration = f"{model_name}_{conf}"
-
-        # Determine the score column name and legend mapping based on the score type
-        if score_name == 'VQA Score':
-
-            score_column_name = f"vqa_score_{model_configuration}"
-            scores = self.main_data[score_column_name]
-            # Map scores to categories for the legend
-            legend_map = ['Correct' if score == 1 else 'Partially Correct' if round(score, 2) == 0.67 else 'Incorrect' for score in scores]
-            color_scale = alt.Scale(domain=['Correct', 'Partially Correct', 'Incorrect'], range=['green', 'orange', 'red'])
-        else:
-            score_column_name = f"exact_match_score_{model_configuration}"
-            scores = self.main_data[score_column_name]
-            # Map scores to categories for the legend
-            legend_map = ['Correct' if score == 1 else 'Incorrect' for score in scores]
-            color_scale = alt.Scale(domain=['Correct', 'Incorrect'], range=['green', 'red'])
-
-        # Retrieve token counts from the data
-        token_counts = self.main_data[f'tokens_count_{conf}']
-
-        # Create a DataFrame for the scatter plot
-        scatter_data = pd.DataFrame({
-            'Index': range(len(token_counts)),
-            'Token Counts': token_counts,
-            score_name: legend_map
-        })
-
-        # Create an interactive scatter plot using Altair
-        chart = alt.Chart(scatter_data).mark_circle(
-            size=60,
-            fillOpacity=1,  # Sets the fill opacity to maximum
-            strokeWidth=1,  # Adjusts the border width making the circles bolder
-            stroke='black'  # Sets the border color to black
-        ).encode(
-            x=alt.X('Index', scale=alt.Scale(domain=[0, 1020])),
-            y=alt.Y('Token Counts', scale=alt.Scale(domain=[token_counts.min()-200, token_counts.max()+200])),
-            color=alt.Color(score_name, scale=color_scale, legend=alt.Legend(title=score_name)),
-            tooltip=['Index', 'Token Counts', score_name]
-        ).interactive()  # Enables zoom & pan
-
-        chart = chart.properties(
-            title={
-                "text": f"Token Counts vs {score_name} + Score +  ({model_configuration})",
-                "color": "black",  # Optional color
-                "fontSize": 20,  # Optional font size
-                "anchor": "middle",  # Optional anchor position
-                "offset": 0  # Optional offset
-            },
-            width=700,
-            height=500
-        )
-
-        # Display the interactive plot in Streamlit
-        st.altair_chart(chart, use_container_width=True)
-
-    @staticmethod
-    def color_scores(value: float) -> str:
-        """
-        Applies color coding based on the score value.
-
-        Args:
-            value (float): The score value.
-
-        Returns:
-            str: CSS color style based on score value.
-        """
-
-        try:
-            value = float(value)  # Convert to float to handle numerical comparisons
-        except ValueError:
-            return 'color: black;'  # Return black if value is not a number
-
-        if value == 1.0:
-            return 'color: green;'
-        elif value == 0.0:
-            return 'color: red;'
-        elif value == 0.67:
-            return 'color: orange;'
-        return 'color: black;'
-
-    def show_samples(self, num_samples: int = 3) -> None:
-        """
-        Displays random sample images and their associated models answers and evaluations.
-
-        Args:
-            num_samples (int): Number of sample images to display.
-        """
-
-        # Sample images from the pool
-        target_imgs = random.sample(self.sample_img_pool, num_samples)
-        # Generate model configurations
-        model_configs = [f"{model_name}_{conf}" for model_name in self.model_names for conf in self.model_configs]
-        # Define column names for scores dynamically
-        column_names = {
-            'vqa': 'vqa_score_{config}',
-            'vqa_gpt4': 'gpt4_vqa_score_{config}',
-            'em': 'exact_match_score_{config}',
-            'em_gpt4': 'gpt4_em_score_{config}'
-        }
-
-        for img_filename in target_imgs:
-            image_data = self.main_data[self.main_data['image_filename'] == img_filename]
-            im = Image.open(f"demo/{img_filename}")
-            col1, col2 = st.columns([1, 2])  # to display images side by side with their data.
-            # Create a container for each image
-            with st.container():
-                st.write("-------------------------------")
-                with col1:
-                    st.image(im, use_column_width=True)
-                    with st.expander('Show Caption'):
-                        st.text(image_data.iloc[0]['caption'])
-                    with st.expander('Show DETIC Objects'):
-                        st.text(image_data.iloc[0]['objects_detic_trimmed'])
-                    with st.expander('Show YOLOv5 Objects'):
-                        st.text(image_data.iloc[0]['objects_yolov5'])
-                with col2:
-                    if not image_data.empty:
-                        st.write(f"**Question: {image_data.iloc[0]['question']}**")
-                        st.write(f"**Ground Truth Answers:** {image_data.iloc[0]['raw_answers']}")
-
-                        # Initialize an empty DataFrame for summary data
-                        summary_data = pd.DataFrame(
-                            columns=['Model Configuration', 'Answer', 'VQA Score', 'VQA Score (GPT-4)', 'EM Score',
-                                     'EM Score (GPT-4)'])
-
-                        for config in model_configs:
-                            # Collect data for each model configuration
-                            row_data = {
-                                'Model Configuration': config,
-                                'Answer': image_data.iloc[0].get(f'{config}', '-')
-                            }
-                            for score_type, score_template in column_names.items():
-                                score_col = score_template.format(config=config)
-                                score_value = image_data.iloc[0].get(score_col, '-')
-                                if pd.notna(score_value) and not isinstance(score_value, str):
-                                    # Format score to two decimals if it's a valid number
-                                    score_value = f"{float(score_value):.2f}"
-                                row_data[score_type.replace('_', ' ').title()] = score_value
-
-                            # Convert row data to a DataFrame and concatenate it
-                            rd = pd.DataFrame([row_data])
-                            rd.columns = summary_data.columns
-                            summary_data = pd.concat([summary_data, rd], axis=0, ignore_index=True)
-
-                        # Apply styling to DataFrame for score coloring
-                        styled_summary = summary_data.style.applymap(self.color_scores,
-                                                                     subset=['VQA Score', 'VQA Score (GPT-4)',
-                                                                             'EM Score',
-                                                                             'EM Score (GPT-4)'])
-                        st.markdown(styled_summary.to_html(escape=False, index=False), unsafe_allow_html=True)
-                    else:
-                        st.write("No data available for this image.")
-
-    def run_demo(self):
-        """
-        Run the interactive Streamlit demo for visualizing model evaluation results and analysis.
-        """
-
-        col1, col2 = st.columns([1, 4])
-        with col1:
-            # User selects the evaluation analysis aspect
-            section_type = st.radio("Select Evaluation Aspect", ["Evaluation Results & Analysis", 'Evaluation Samples'])
-
-            # Only show analysis type if the section type is "Evaluation Results & Analysis"
-            if section_type == "Evaluation Results & Analysis":
-                analysis_type = st.radio("Select Type", ["Main & Ablation Results", "Results per Question Category",
-                                                         "Prompt Length (token count) Impact on Performance"], index=2)
-                if analysis_type == "Prompt Length (token count) Impact on Performance":
-                    # Based on the selection, other options appear
-                    model_name = st.radio("Select Model Size", self.model_names)
-                    score_name = st.radio("Select Score Type", ["VQA Score", "Exact Match"])
-
-            elif section_type == 'Evaluation Samples':
-                samples_button = st.button("Generate Random Samples")
-        with col2:
-            if section_type == "Evaluation Results & Analysis":
-                if analysis_type == "Prompt Length (token count) Impact on Performance":
-                    for conf in self.model_configs:
-                        with st.expander(conf):
-                            self.plot_token_count_vs_scores(conf, model_name, score_name)
-                elif analysis_type == "Main & Ablation Results":
-                    self.display_main_results()
-                elif analysis_type == "Results per Question Category":
-                    self.display_ablation_results_per_question_category()
-            elif section_type == 'Evaluation Samples':
-                if samples_button:
-                    self.show_samples(3)