Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from io import BytesIO
+from datetime import datetime
+import opik
+import warnings
+warnings.filterwarnings('ignore')
+
+# --------------------------------------------------------
+# CONFIG
+# --------------------------------------------------------
+OPIK_PROJECT_NAME = 'production-vf-ai'
+OPIK_WORKSPACE_NAME = 'verba-tech-ninja'
+OPIK_API_KEY = "YOUR_OPIK_API_KEY_HERE"   # INSERT YOUR KEY
+
+
+# --------------------------------------------------------
+# INIT OPik CLIENT
+# --------------------------------------------------------
+client = opik.Opik(
+    api_key=OPIK_API_KEY,
+    workspace=OPIK_WORKSPACE_NAME,
+    project_name=OPIK_PROJECT_NAME
+)
+
+
+# --------------------------------------------------------
+# FETCH TRACES
+# --------------------------------------------------------
+def fetch_traces(client_name, start_iso, end_iso):
+    filter_string = (
+        'name contains "analyse_transcript" '
+        f'AND start_time >= "{start_iso}" '
+        f'AND end_time <= "{end_iso}" '
+        f'AND tags contains "{client_name}"'
+    )
+
+    traces = client.search_traces(
+        project_name=OPIK_PROJECT_NAME,
+        filter_string=filter_string,
+        max_results=50000
+    )
+    return list(traces)
+
+
+# --------------------------------------------------------
+# FILTER TRACES
+# --------------------------------------------------------
+def filter_traces(traces):
+    final = []
+    for trace in traces:
+        tags = trace.tags or []
+
+        if "_call_" in tags or "[CAMPAIGN_CONVERSATION]" in tags:
+            continue
+
+        output = trace.output
+        if not output:
+            continue
+
+        category = output.get("category")
+        use_case = output.get("campaign_payload", {}).get("use_case")
+
+        if category != "customer" and use_case is None:
+            final.append(trace)
+
+    return final
+
+
+# --------------------------------------------------------
+# PARSE SPANS
+# --------------------------------------------------------
+def extract_meta(trace):
+    spans = client.search_spans(project_name=OPIK_PROJECT_NAME, trace_id=trace.id)
+    out = []
+
+    for s in spans:
+        if s.name != "chat_completion_parse":
+            continue
+
+        usage = s.metadata.get("usage", {})
+        out.append({
+            "duration": s.duration / 1000,
+            "tier": s.metadata.get("service_tier", "default"),
+            "model": s.metadata.get("model"),
+            "tokens": usage.get("completion_tokens", 0),
+            "error": bool(s.error_info)
+        })
+    return out
+
+
+# --------------------------------------------------------
+# RUN MAIN PIPELINE
+# --------------------------------------------------------
+def run_pipeline(client_name, start_dt, end_dt, metadata_fields):
+
+    start_iso = start_dt + "Z"
+    end_iso = end_dt + "Z"
+
+    traces = fetch_traces(client_name, start_iso, end_iso)
+    traces = filter_traces(traces)
+
+    rows = []
+    for t in traces:
+        rows.extend(extract_meta(t))
+
+    if not rows:
+        return "No data", None, None, None
+
+    # Filter selected metadata fields
+    df = pd.DataFrame(rows)
+    df_filtered = df[metadata_fields]
+
+    # ---------------- Stats -----------------
+    durations = df.loc[~df["error"], "duration"]
+    tokens = df["tokens"]
+
+    stats = {
+        "total_spans": len(df),
+        "errors": int(df["error"].sum()),
+        "error_rate_%": round(100 * df["error"].mean(), 2),
+
+        "mean_latency_sec": round(durations.mean(), 3) if len(durations) else None,
+        "median_latency_sec": round(durations.median(), 3) if len(durations) else None,
+        "p90_latency_sec": round(durations.quantile(0.9), 3) if len(durations) else None,
+        "p95_latency_sec": round(durations.quantile(0.95), 3) if len(durations) else None,
+        "min_latency": round(durations.min(), 3) if len(durations) else None,
+        "max_latency": round(durations.max(), 3) if len(durations) else None,
+
+        "avg_tokens": round(tokens.mean(), 2),
+        "max_tokens": int(tokens.max())
+    }
+
+    # ---------------- Charts -----------------
+    fig1, ax1 = plt.subplots()
+    ax1.hist(df["duration"], bins=30)
+    ax1.set_title("Latency Distribution (seconds)")
+    ax1.set_xlabel("Seconds")
+    ax1.set_ylabel("Frequency")
+
+    fig2, ax2 = plt.subplots()
+    ax2.hist(df["tokens"], bins=25)
+    ax2.set_title("Completion Token Distribution")
+    ax2.set_xlabel("Tokens")
+    ax2.set_ylabel("Frequency")
+
+    # Convert figs to image
+    buf1, buf2 = BytesIO(), BytesIO()
+    fig1.savefig(buf1, format="png")
+    fig2.savefig(buf2, format="png")
+    buf1.seek(0)
+    buf2.seek(0)
+    plt.close(fig1)
+    plt.close(fig2)
+
+    # CSV
+    csv_data = df_filtered.to_csv(index=False)
+
+    return stats, df_filtered, buf1, buf2, csv_data
+
+
+# --------------------------------------------------------
+# GRADIO UI
+# --------------------------------------------------------
+with gr.Blocks(title="Opik Analytics Dashboard") as demo:
+
+    gr.Markdown("# 📊 **Opik Analytics Dashboard** (Gradio)")
+    gr.Markdown("Analyze traces by client, date range, and metadata fields.")
+
+    with gr.Row():
+        client_name = gr.Dropdown(
+            ["fusiongroup", "vita", "staragent", "testclient", "other"],
+            label="Select Client",
+            value="fusiongroup"
+        )
+
+    with gr.Row():
+        start_dt = gr.Textbox(label="Start DateTime UTC (YYYY-MM-DDTHH:MM:SS)", value="2025-11-17T00:00:00")
+        end_dt = gr.Textbox(label="End DateTime UTC (YYYY-MM-DDTHH:MM:SS)", value="2025-11-17T12:00:00")
+
+    metadata_fields = gr.CheckboxGroup(
+        ["duration", "tier", "tokens", "model", "error"],
+        label="Select Metadata Fields",
+        value=["duration", "tier", "tokens"]
+    )
+
+    run_btn = gr.Button("Run Analysis")
+
+    stats_output = gr.JSON(label="📈 Summary Statistics")
+    table_output = gr.DataFrame(label="📄 Raw Data")
+    plot_latency = gr.Image(label="⏱ Latency Distribution")
+    plot_tokens = gr.Image(label="🔢 Token Distribution")
+
+    csv_download = gr.File(label="⬇ Download CSV")
+
+    run_btn.click(
+        fn=run_pipeline,
+        inputs=[client_name, start_dt, end_dt, metadata_fields],
+        outputs=[stats_output, table_output, plot_latency, plot_tokens, csv_download]
+    )
+
+
+demo.launch()