upload app and requirements

app.py

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+from datetime import datetime
+
+import gradio as gr
+import pandas as pd
+from datasets import load_dataset
+
+# Load the dataset
+ds = load_dataset("harshildarji/openlegaldata", "cases", split="main")
+df = pd.DataFrame(ds)
+
+# Precompute additional columns for filtering
+df["state"] = df["court"].apply(
+    lambda x: x.get("state", "Unknown") if isinstance(x, dict) else "Unknown"
+)
+df["court_name"] = df["court"].apply(
+    lambda x: x.get("name", "Unknown") if isinstance(x, dict) else "Unknown"
+)
+
+# Build unique lists for the filters
+state_list = sorted(df["state"].dropna().unique().tolist())
+state_list.insert(0, "All")
+court_list = sorted(df["court_name"].dropna().unique().tolist())
+court_list.insert(0, "All")
+
+
+def filter_cases(state, court, date_from, date_to):
+    """
+    Filter the cases based on state, court name, and date range.
+    Returns a DataFrame with selected columns.
+    """
+    filtered = df.copy()
+    if state and state != "All":
+        filtered = filtered[filtered["state"] == state]
+    if court and court != "All":
+        filtered = filtered[filtered["court_name"] == court]
+    if date_from:
+        try:
+            date_from_dt = datetime.strptime(date_from, "%Y-%m-%d")
+            filtered = filtered[pd.to_datetime(filtered["date"]) >= date_from_dt]
+        except Exception:
+            pass
+    if date_to:
+        try:
+            date_to_dt = datetime.strptime(date_to, "%Y-%m-%d")
+            filtered = filtered[pd.to_datetime(filtered["date"]) <= date_to_dt]
+        except Exception:
+            pass
+
+    return filtered[["id", "file_number", "date", "court_name", "type"]]
+
+
+def get_case_details(case_id):
+    """Return an HTML formatted string with details for a given case id."""
+    try:
+        case_id = int(case_id)
+    except:
+        return "<p style='color:red;'>Invalid case ID</p>"
+    case_row = df[df["id"] == case_id]
+    if case_row.empty:
+        return "<p style='color:red;'>Case not found</p>"
+    case_data = case_row.iloc[0].to_dict()
+
+    html = "<h2>Case Details</h2>"
+    html += f"<p><strong>ID:</strong> {case_data.get('id')}</p>"
+    html += f"<p><strong>File Number:</strong> {case_data.get('file_number')}</p>"
+    html += f"<p><strong>Date:</strong> {case_data.get('date')}</p>"
+
+    # Display court information
+    court_info = case_data.get("court", {})
+    html += f"<p><strong>Court:</strong> {court_info.get('name', 'N/A')} ({court_info.get('state', 'N/A')})</p>"
+    html += f"<p><strong>Type:</strong> {case_data.get('type', 'N/A')}</p>"
+    html += '<hr style="margin: 15px 0;">'
+
+    # Display tenor if available
+    tenors = case_data.get("tenor", [])
+    if tenors:
+        html += "<h3>Tenor</h3>"
+        for t in tenors:
+            html += f"<p>{t}</p>"
+        html += '<hr style="margin: 15px 0;">'
+
+    # Display tatbestand if available
+    tatbestand = case_data.get("tatbestand", [])
+    if tatbestand:
+        html += "<h3>Tatbestand</h3>"
+        for t in tatbestand:
+            html += f"<p>{t}</p>"
+        html += '<hr style="margin: 15px 0;">'
+
+    # Display Gründe if available
+    gründe = case_data.get("gründe", [])
+    if gründe:
+        html += "<h3>Gründe</h3>"
+        for g in gründe:
+            html += f"<p>{g}</p>"
+        html += '<hr style="margin: 15px 0;">'
+
+    # Display Entscheidungsgründe if available
+    entscheidungsgründe = case_data.get("entscheidungsgründe", [])
+    if entscheidungsgründe:
+        html += "<h3>Entscheidungsgründe</h3>"
+        for e in entscheidungsgründe:
+            html += f"<p>{e}</p>"
+        html += '<hr style="margin: 15px 0;">'
+
+    return html
+
+
+# Build multi-tabs interface
+with gr.Blocks(title="German Legal Case Viewer", fill_width=True) as demo:
+    gr.Markdown("# German Legal Case Viewer")
+    gr.Markdown(
+        "Explore case information from the processed [Open Legal Data dataset](https://huggingface.co/datasets/harshildarji/openlegaldata)."
+    )
+
+    with gr.Tabs():
+        # First Tab - Browse and filter cases
+        with gr.Tab("Browse Cases"):
+            gr.Markdown("## Filter Cases")
+            with gr.Row():
+                state_input = gr.Dropdown(
+                    choices=state_list, label="State", value="All"
+                )
+                court_input = gr.Dropdown(
+                    choices=court_list, label="Court", value="All"
+                )
+                date_from_input = gr.Textbox(
+                    label="From Date (YYYY-MM-DD)", placeholder="2022-01-01"
+                )
+                date_to_input = gr.Textbox(
+                    label="To Date (YYYY-MM-DD)", placeholder="2022-12-31"
+                )
+            filter_button = gr.Button("Apply Filters")
+            output_table = gr.DataFrame(label="Cases Overview")
+            filter_button.click(
+                fn=filter_cases,
+                inputs=[state_input, court_input, date_from_input, date_to_input],
+                outputs=output_table,
+            )
+
+        # Second Tab - Show details of a specific case
+        with gr.Tab("Case Details"):
+            with gr.Row():
+                case_id_input = gr.Textbox(
+                    label="Enter Case ID", placeholder="e.g., 346915"
+                )
+                details_button = gr.Button("Get Details")
+            case_details_output = gr.HTML(label="Case Details")
+            details_button.click(
+                fn=get_case_details,
+                inputs=case_id_input,
+                outputs=case_details_output,
+            )
+
+demo.launch()

requirements.txt

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+gradio
+datasets
+pandas