Change to flask app

app.py

@@ -1,98 +1,85 @@
-import gradio as gr
+from flask import Flask, render_template, request, send_file
 import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np
 import io
+import os
+
+app = Flask(__name__)
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    if request.method == "POST":
+        golden_file = request.files.get("golden_file")
+        test_file = request.files.get("test_file")
+
+        if not golden_file or not test_file:
+            return render_template("index.html", error="Please upload both files.")
 
-def plot_excel_data(golden_data_file, manipulated_data_file):
-    """
-    Reads two Excel files, plots the time-series data, and adds spec limits.
-    """
-    if golden_data_file is None:
-        raise gr.Error("Please upload the 'Golden Data' Excel file.")
-
-    try:
-        # Read first 3 rows to extract limits from the golden data
-        limits_df1 = pd.read_excel(golden_data_file.name, nrows=4)
-        limits_df1 = limits_df1.drop(0)
-
-        # Data (skip first 3 rows)
-        df1 = pd.read_excel(golden_data_file.name)
-        df1 = df1.drop([0, 1, 2, 3])
-        df1 = df1.apply(pd.to_numeric, errors="coerce")
-    except Exception as e:
-        raise gr.Error(f"Error processing 'Golden Data' file: {e}")
-
-    # Build limits dictionary per column
-    ignore_cols = ["SITE_NUM", "PART_ID", "PASSFG", "SOFT_BIN", "T_TIME", "TEST_NUM"]
-    cols_to_plot = [col for col in limits_df1.columns if "_" in col and col not in ignore_cols]
-    limits_df1 = limits_df1.drop(columns=ignore_cols)
-    limits = {
-        col: {"LL": limits_df1.iloc[0][col], "UL": limits_df1.iloc[1][col]}
-        for col in limits_df1.columns
-    }
-
-    # Initialize a second dataframe if a file is provided
-    df2 = None
-    if manipulated_data_file is not None:
         try:
-            df2 = pd.read_excel(manipulated_data_file.name)
+            # --- Read Data ---
+            limits_df1 = pd.read_excel(golden_file, nrows=4)
+            df1 = pd.read_excel(golden_file)
+            df1 = df1.drop([0, 1, 2, 3])
+            df2 = pd.read_excel(test_file)
             df2 = df2.drop([0, 1, 2, 3])
+
+            df1 = df1.apply(pd.to_numeric, errors="coerce")
             df2 = df2.apply(pd.to_numeric, errors="coerce")
-        except Exception as e:
-            raise gr.Error(f"Error processing 'Manipulated Data' file: {e}")
 
-    # Plotting logic
-    n_cols = 3
-    n_rows = (len(df1.columns) + n_cols - 1) // n_cols
+            # --- Build Limits ---
+            limits_df1 = limits_df1.drop([0])
+            ignore_cols = ["SITE_NUM", "PART_ID", "PASSFG", "SOFT_BIN", "T_TIME", "TEST_NUM"]
+            cols_to_plot = [col for col in limits_df1.columns if "_" in col and col not in ignore_cols]
+            limits_df1 = limits_df1.drop(columns=ignore_cols)
+
+            limits = {
+                col: {"LL": limits_df1.iloc[0][col], "UL": limits_df1.iloc[1][col]}
+                for col in limits_df1.columns
+            }
+
+            # --- Plot ---
+            n_cols = 3
+            n_rows = (len(cols_to_plot) + n_cols - 1) // n_cols
+
+            plt.figure(figsize=(n_cols * 5, n_rows * 3.5))
+            for i, col in enumerate(cols_to_plot, 1):
+                plt.subplot(n_rows, n_cols, i)
+                x1 = np.arange(1, len(df1[col]) + 1)
+                y1 = pd.to_numeric(df1[col], errors="coerce").values
+                plt.plot(x1, y1, marker="o", linestyle="-", color="blue", label="Golden")
+
+                if col in df2.columns:
+                    x2 = np.arange(1, len(df2[col]) + 1)
+                    y2 = pd.to_numeric(df2[col], errors="coerce").values
+                    plt.plot(x2, y2, marker="s", linestyle="--", color="red", label="Test")
+
+                if col in limits:
+                    ll, ul = limits[col]["LL"], limits[col]["UL"]
+                    plt.axhline(ll, color="green", linestyle="--", linewidth=2, label="LL")
+                    plt.axhline(ul, color="orange", linestyle="--", linewidth=2, label="UL")
+
+                plt.title(f"{col}")
+                plt.xlabel("Part # (sequence)")
+                plt.ylabel("Value")
+                plt.grid(True, linestyle="--", alpha=0.7)
+                plt.legend(fontsize="small")
+
+            plt.tight_layout()
+
+            # Save plot to in-memory buffer
+            buf = io.BytesIO()
+            plt.savefig(buf, format="png", bbox_inches="tight")
+            buf.seek(0)
+            plt.close()
+
+            return send_file(buf, mimetype="image/png")
+
+        except Exception as e:
+            return render_template("index.html", error=f"Error: {str(e)}")
 
-    fig, axes = plt.subplots(n_rows, n_cols, figsize=(n_cols * 5, n_rows * 3.5))
-    if n_rows * n_cols > len(df1.columns):
-        # Flatten axes array for easy iteration, then turn off unused subplots
-        for i in range(len(df1.columns), n_rows * n_cols):
-            axes.flatten()[i].axis('off')
+    return render_template("index.html")
     
-    for i, col in enumerate(cols_to_plot):
-        ax = axes.flatten()[i] if n_rows > 1 else axes[i]
-        
-        # Golden data (Old)
-        x1 = np.arange(1, len(df1[col]) + 1)
-        y1 = pd.to_numeric(df1[col], errors="coerce").values
-        ax.plot(x1, y1, marker="o", linestyle="-", color="blue", label="Old")
-
-        # New data (if provided)
-        if df2 is not None and col in df2.columns:
-            x2 = np.arange(1, len(df2[col]) + 1)
-            y2 = pd.to_numeric(df2[col], errors="coerce").values
-            ax.plot(x2, y2, marker="s", linestyle="--", color="red", label="New")
-
-        # Spec limits
-        if col in limits:
-            ll, ul = limits[col]["LL"], limits[col]["UL"]
-            ax.axhline(ll, color="green", linestyle="--", linewidth=2, label="LL")
-            ax.axhline(ul, color="orange", linestyle="--", linewidth=2, label="UL")
-
-        ax.set_title(f"{col}")
-        ax.set_xlabel("Part # (sequence)")
-        ax.set_ylabel("Value")
-        ax.set_xticks(x1)
-        ax.grid(True, linestyle="--", alpha=0.7)
-        ax.legend()
-
-    plt.tight_layout()
-    return fig
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=plot_excel_data,
-    inputs=[
-        gr.File(label="Upload IPM_Golden_Data.xlsx (Required)"),
-        gr.File(label="Upload IPM_Golden_Data_Manipulated.xlsx (Optional)"),
-    ],
-    outputs=gr.Plot(label="Comparison Plots"),
-    title="Time-Series Data Comparison",
-    description="Upload two Excel files to compare time-series data and visualize specification limits. The first file (Golden Data) is required and will be used to extract the limits."
-)
 
 if __name__ == "__main__":
-    iface.launch()
+    app.run(host="0.0.0.0", port=7860, debug=True)

requirements.txt

@@ -1,4 +1,4 @@
-gradio
+flask
 pandas
 matplotlib
 numpy

templates/index.html

@@ -0,0 +1,55 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>IPM Golden vs Test Data Visualizer</title>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            background: #f2f2f2;
+            text-align: center;
+            padding: 40px;
+        }
+        form {
+            background: white;
+            padding: 20px;
+            border-radius: 10px;
+            display: inline-block;
+        }
+        input[type=file] {
+            margin: 10px;
+        }
+        button {
+            background-color: #0078d7;
+            color: white;
+            border: none;
+            padding: 10px 20px;
+            border-radius: 5px;
+            cursor: pointer;
+        }
+        button:hover {
+            background-color: #005fa3;
+        }
+        .error {
+            color: red;
+        }
+    </style>
+</head>
+<body>
+    <h2>📊 IPM Golden vs Test Data Visualizer</h2>
+    <p>Upload your Golden and Test Excel files to compare results visually.</p>
+
+    {% if error %}
+    <p class="error">{{ error }}</p>
+    {% endif %}
+
+    <form method="POST" enctype="multipart/form-data">
+        <label>Golden Data (.xlsx):</label><br>
+        <input type="file" name="golden_file" accept=".xlsx" required><br><br>
+
+        <label>Test Data (.xlsx):</label><br>
+        <input type="file" name="test_file" accept=".xlsx" required><br><br>
+
+        <button type="submit">Generate Plot</button>
+    </form>
+</body>
+</html>