Update app.py

app.py

@@ -13,7 +13,6 @@ from PIL import Image
 def load_results():
     # Get the directory of the current file
     current_dir = os.path.dirname(os.path.abspath(__file__))
-    # Construct the path to the JSON file (assumes file is stored in "files/aragen_v1_results.json")
     results_file = os.path.join(current_dir, "files", "aragen_v1_results.json")
     with open(results_file, "r") as f:
         data = json.load(f)
@@ -75,7 +74,6 @@ def generate_heatmap_image(model_entry):
     image = Image.open(buf).convert("RGB")
     
     # Resize the image to a reasonable fixed size for the gallery
-    # This helps maintain consistency and prevent oversized images
     max_size = (800, 600)
     image.thumbnail(max_size, Image.Resampling.LANCZOS)
     
@@ -103,12 +101,18 @@ with gr.Blocks(css="""
         object-fit: contain !important;
     }
 """) as demo:
-    gr.Markdown("## 3C3H Heatmap Generator")
-    gr.Markdown("Select the models you want to compare and generate their heatmaps below.")
+    gr.Markdown("""
+    <center>
+    <br></br>
+    <h1>3C3H Heatmap Generator</h1>
+    <br></br>
+    </center>
+""")
+    gr.Markdown("<center>Select the models you want to compare and generate their heatmaps below.</center>")
     
     with gr.Row():
         default_models = ["silma-ai/SILMA-9B-Instruct-v1.0", "google/gemma-2-9b-it"]
-        model_dropdown = gr.Dropdown(choices=MODEL_NAMES, label="Select Model(s)", multiselect=True, value=default_models) # value=MODEL_NAMES[:3]
+        model_dropdown = gr.Dropdown(choices=MODEL_NAMES, label="Select Model(s)", multiselect=True, value=default_models)
     
     generate_btn = gr.Button("Generate Heatmaps")
     
@@ -122,112 +126,4 @@ with gr.Blocks(css="""
     
     generate_btn.click(fn=generate_heatmaps, inputs=model_dropdown, outputs=gallery)
 
-# Launch the Gradio app
 demo.launch()
-
-
-# import gradio as gr
-# import json
-# import os
-# import numpy as np
-# import matplotlib.pyplot as plt
-# import seaborn as sns
-# from io import BytesIO
-# from PIL import Image
-
-# # -------------------------------
-# # 1. Load Results from Local File
-# # -------------------------------
-# def load_results():
-#     # Get the directory of the current file
-#     current_dir = os.path.dirname(os.path.abspath(__file__))
-#     # Construct the path to the JSON file (assumes file is stored in "files/aragen_v1_results.json")
-#     results_file = os.path.join(current_dir, "files", "aragen_v1_results.json")
-#     with open(results_file, "r") as f:
-#         data = json.load(f)
-#     # Filter out any non-model entries (e.g., timestamp entries)
-#     model_data = [entry for entry in data if "Meta" in entry]
-#     return model_data
-
-# # Load the JSON data once when the app starts
-# DATA = load_results()
-
-# # Extract model names for the dropdown from the JSON "Meta" field
-# def get_model_names(data):
-#     model_names = [entry["Meta"]["Model Name"] for entry in data]
-#     return model_names
-
-# MODEL_NAMES = get_model_names(DATA)
-
-# # -------------------------------
-# # 2. Define Metrics and Heatmap Generation Functions
-# # -------------------------------
-# # Define the six metrics in the desired order.
-# METRICS = ["Correctness", "Completeness", "Conciseness", "Helpfulness", "Honesty", "Harmlessness"]
-
-# def generate_heatmap_image(model_entry):
-#     """
-#     For a given model entry, extract the six metrics and compute a 6x6 similarity matrix
-#     using the definition: similarity = 1 - |v_i - v_j|, then return the heatmap as a PIL image.
-#     """
-#     scores = model_entry["claude-3.5-sonnet Scores"]["3C3H Scores"]
-#     # Create a vector with the metrics in the defined order.
-#     v = np.array([scores[m] for m in METRICS])
-#     # Compute the 6x6 similarity matrix.
-#     matrix = 1 - np.abs(np.subtract.outer(v, v))
-#     # Create a mask for the upper triangle (keeping the diagonal visible).
-#     mask = np.triu(np.ones_like(matrix, dtype=bool), k=1)
-    
-#     plt.figure(figsize=(6, 5))
-#     sns.heatmap(matrix,
-#                 mask=mask,
-#                 annot=True,
-#                 fmt=".2f",
-#                 cmap="viridis",
-#                 xticklabels=METRICS,
-#                 yticklabels=METRICS,
-#                 cbar_kws={"label": "Similarity"})
-#     plt.title(f"Confusion Matrix for Model: {model_entry['Meta']['Model Name']}")
-#     plt.xlabel("Metrics")
-#     plt.ylabel("Metrics")
-#     plt.tight_layout()
-    
-#     # Save the plot to a bytes buffer.
-#     buf = BytesIO()
-#     plt.savefig(buf, format="png")
-#     plt.close()
-#     buf.seek(0)
-#     # Convert the buffer into a PIL Image.
-#     image = Image.open(buf).convert("RGB")
-#     return image
-
-# def generate_heatmaps(selected_model_names):
-#     """
-#     Filter the global DATA for entries matching the selected model names,
-#     generate a heatmap for each, and return a list of PIL images.
-#     """
-#     filtered_entries = [entry for entry in DATA if entry["Meta"]["Model Name"] in selected_model_names]
-#     images = []
-#     for entry in filtered_entries:
-#         img = generate_heatmap_image(entry)
-#         images.append(img)
-#     return images
-
-# # -------------------------------
-# # 3. Build the Gradio Interface
-# # -------------------------------
-# with gr.Blocks() as demo:
-#     gr.Markdown("## 3C3H Heatmap Generator")
-#     gr.Markdown("Select the models you want to compare and generate their heatmaps below.")
-    
-#     with gr.Row():
-#         model_dropdown = gr.Dropdown(choices=MODEL_NAMES, label="Select Model(s)", multiselect=True, value=MODEL_NAMES[:3])
-    
-#     generate_btn = gr.Button("Generate Heatmaps")
-#     # Use the 'columns' parameter to set a grid layout in the gallery.
-#     gallery = gr.Gallery(label="Heatmaps", columns=2)
-    
-#     generate_btn.click(fn=generate_heatmaps, inputs=model_dropdown, outputs=gallery)
-
-# # Launch the Gradio app
-# demo.launch()