Update app.py

app.py

@@ -1,52 +1,53 @@
 import gradio as gr
+import pandas as pd
 import json
-from collections import defaultdict
 
-# Load and group data by prompt
-def load_data(file_path):
-    prompt_dict = defaultdict(list)
-    with open(file_path, "r") as f:
+def load_data(path: str):
+    records = []
+    with open(path, "r", encoding="utf-8") as f:
         for line in f:
-            row = json.loads(line)
-            prompt = row["prompt"]
-            model = row["model"]
-            response = row["model_response"]
-            prompt_dict[prompt].append((model, response))
-    return prompt_dict
-
-DATA_PATH = "all_10_responses.jsonl"
-prompt_to_responses = load_data(DATA_PATH)
-prompt_list = sorted(prompt_to_responses.keys())
-
-# Gradio interface
-def display_responses(selected_prompt):
-    entries = prompt_to_responses[selected_prompt]
-    accordions = []
-    for model, response in sorted(entries):
-        with gr.Accordion(label=model, open=False):
-            gr.Markdown(f"""```text\n{response}\n```""")
-    return
-
-with gr.Blocks(css=".gr-accordion .label {font-weight: bold}") as demo:
-    gr.Markdown("## 🔍 Prompt Viewer: Compare Model Responses")
-    selected_prompt = gr.Dropdown(choices=prompt_list, label="Select a Prompt", interactive=True)
-
-    with gr.Column() as output_column:
-        prompt_output = gr.Markdown()
-        response_group = gr.Group()
-
-    def update_output(prompt):
-        prompt_output.update(f"### Prompt\n```\n{prompt}\n```")
-        response_group.children.clear()
-        entries = prompt_to_responses[prompt]
-        for model, response in sorted(entries):
-            response_group.children.append(
-                gr.Accordion(label=model, open=False, children=[
-                    gr.Markdown(f"""```text\n{response}\n```""")
-                ])
-            )
-
-    selected_prompt.change(fn=update_output, inputs=[selected_prompt])
-
-if __name__ == "__main__":
-    demo.launch()
+            obj = json.loads(line)
+            records.append({
+                "prompt": obj.get("Prompt", "").strip(),
+                "model": obj.get("model", "").strip(),
+                "response": obj.get("model response", "").strip()
+            })
+    return pd.DataFrame(records)
+
+# Load once
+df = load_data("all_10_responses.jsonl")
+all_prompts = sorted(df["prompt"].unique().tolist())
+all_models = sorted(df["model"].unique().tolist())
+
+def get_model_responses(chosen_prompt, chosen_models):
+    subset = df[
+        (df["prompt"] == chosen_prompt) &
+        (df["model"].isin(chosen_models))
+    ].sort_values("model")
+    # Return a list of tuples: [(model_name, response), …]
+    return [(row["model"], row["response"]) for _, row in subset.iterrows()]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Model Responses Explorer")
+
+    with gr.Row():
+        prompt_dd = gr.Dropdown(all_prompts, label="Select a Prompt")
+        models_dd = gr.CheckboxGroup(all_models, value=all_models, label="Select Models")
+
+    with gr.Column():
+        output_acc = gr.Accordion(label="Responses", open=False)
+
+    def update_accordion(prompt, models):
+        items = get_model_responses(prompt, models)
+        # Gradio accordion expects a single gr.Column or gr.Text
+        # We’ll return a single Markdown string combining all models
+        md = ""
+        for m, r in items:
+            md += f"### {m}\n\n{r}\n---\n"
+        return md
+
+    accordion_text = gr.Markdown()
+    prompt_dd.change(fn=update_accordion, inputs=[prompt_dd, models_dd], outputs=accordion_text)
+    models_dd.change(fn=update_accordion, inputs=[prompt_dd, models_dd], outputs=accordion_text)
+
+    demo.launch()