Update app.py

app.py

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+import gradio as gr
+import json
+import requests
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from io import BytesIO
+
+# -------------------------------
+# 1. Configuration and Data Loading
+# -------------------------------
+# URL to the JSON file (the URL below resolves to the raw file)
+DATA_URL = "https://huggingface.co/spaces/alielfilali01/3C3H-HeatMap/resolve/main/files/aragen_v1_results.json"
+
+# Define the metrics order (6 dimensions)
+METRICS = ["Correctness", "Completeness", "Conciseness", "Helpfulness", "Honesty", "Harmlessness"]
+
+def load_data(url=DATA_URL):
+    response = requests.get(url)
+    data = response.json()
+    # 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_data()
+
+# Extract model names for the dropdown based on 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. Heatmap Generation Functions
+# -------------------------------
+def generate_heatmap_image(model_entry):
+    """
+    Given a model entry from the JSON data, this function extracts the 6 metrics,
+    computes a 6x6 similarity matrix using the definition: similarity = 1 - |v_i - v_j|,
+    and returns the heatmap image as bytes.
+    """
+    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 (diagonal remains visible)
+    mask = np.triu(np.ones_like(matrix, dtype=bool), k=1)
+    
+    plt.figure(figsize=(6, 5))
+    ax = 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 figure to a bytes buffer
+    buf = BytesIO()
+    plt.savefig(buf, format="png")
+    plt.close()
+    buf.seek(0)
+    return buf.read()
+
+def generate_heatmaps(selected_model_names):
+    """
+    Filters the global DATA for entries matching the selected model names,
+    generates a heatmap for each one, and returns a list of image bytes.
+    """
+    filtered_entries = [entry for entry in DATA if entry["Meta"]["Model Name"] in selected_model_names]
+    images = []
+    for entry in filtered_entries:
+        img_bytes = generate_heatmap_image(entry)
+        images.append(img_bytes)
+    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")
+    gallery = gr.Gallery(label="Heatmaps").style(grid=[2], height="auto")
+    
+    generate_btn.click(fn=generate_heatmaps, inputs=model_dropdown, outputs=gallery)
+
+# Launch the Gradio app
+demo.launch()