Update tools/plot_generator.py

tools/plot_generator.py

@@ -1,28 +1,91 @@
+import os
+import tempfile
 import pandas as pd
 import plotly.graph_objects as go
 
-def plot_metric_tool(file_path: str, date_col: str, value_col: str):
+
+def plot_metric_tool(
+    file_path: str,
+    date_col: str,
+    value_col: str,
+    output_dir: str = "/tmp",
+    title: str = None,
+    line_width: int = 2,
+    marker_size: int = 6
+):
     """
-    Generic interactive line chart for any numeric metric.
-    Returns a Plotly Figure or error message string.
+    Load CSV or Excel file, parse a time series metric, and return an interactive Plotly Figure.
+    Also saves a high-resolution PNG to a temp directory for static embedding.
+
+    Returns:
+      fig (go.Figure) or error string starting with '❌'.
     """
-    df = pd.read_csv(file_path)
+    # 0) Load data
+    ext = os.path.splitext(file_path)[1].lower()
+    try:
+        if ext in ('.xls', '.xlsx'):
+            df = pd.read_excel(file_path)
+        else:
+            df = pd.read_csv(file_path)
+    except Exception as exc:
+        return f"❌ Failed to load file: {exc}"
 
-    if date_col not in df.columns or value_col not in df.columns:
-        return f"❌ '{date_col}' or '{value_col}' column missing."
+    # 1) Validate columns
+    missing = [c for c in (date_col, value_col) if c not in df.columns]
+    if missing:
+        return f"❌ Missing column(s): {', '.join(missing)}"
 
-    df[date_col] = pd.to_datetime(df[date_col], errors="coerce")
-    if df[date_col].isna().all():
-        return f"❌ '{date_col}' could not be parsed as dates."
+    # 2) Parse date and ensure numeric values
+    try:
+        df[date_col] = pd.to_datetime(df[date_col], errors='coerce')
+    except Exception:
+        return f"❌ Could not parse '{date_col}' as dates."
+    df[value_col] = pd.to_numeric(df[value_col], errors='coerce')
+    df = df.dropna(subset=[date_col, value_col])
+    if df.empty:
+        return f"❌ No valid data after cleaning '{date_col}'/'{value_col}'"
+
+    # 3) Sort and aggregate duplicates
+    df = (
+        df[[date_col, value_col]]
+        .groupby(date_col, as_index=True)
+        .mean()
+        .sort_index()
+    )
 
+    # 4) Create Plotly figure
     fig = go.Figure(
-        go.Scatter(
-            x=df[date_col],
-            y=df[value_col],
-            mode="lines+markers",
-            line=dict(width=2),
-        )
+        data=[
+            go.Scatter(
+                x=df.index,
+                y=df[value_col],
+                mode='lines+markers',
+                line=dict(width=line_width),
+                marker=dict(size=marker_size),
+                name=value_col
+            )
+        ]
     )
-    fig.update_layout(title=f"{value_col} Trend", template="plotly_dark")
-    fig.write_image("metric_plot.png")
-    return fig
+    plot_title = title or f"{value_col} Trend"
+    fig.update_layout(
+        title=plot_title,
+        xaxis_title=date_col,
+        yaxis_title=value_col,
+        template='plotly_dark',
+        hovermode='x unified'
+    )
+
+    # 5) Save static PNG
+    os.makedirs(output_dir, exist_ok=True)
+    tmpfile = tempfile.NamedTemporaryFile(
+        suffix='.png', prefix='trend_', dir=output_dir, delete=False
+    )
+    img_path = tmpfile.name
+    tmpfile.close()
+    try:
+        fig.write_image(img_path, scale=2)
+    except Exception as exc:
+        return f"❌ Failed saving image: {exc}"
+
+    # 6) Return figure and path for embedding
+    return fig, img_path