Update app.py

app.py

@@ -253,35 +253,44 @@ def format_salesforce_data():
         print(f"Error fetching data: {e}")
         return None
 
-# Function to generate a bar graph from Salesforce data
+# Function to generate a dynamic bar graph
 def generate_dynamic_bar_graph(df, x_column, y_column):
-    """
-    Generates a dynamic bar graph from a DataFrame based on the specified x and y columns.
-    The function will automatically adjust the graph based on the column names.
-    """
     try:
-        # Check if the DataFrame has the specified columns
         if x_column not in df.columns or y_column not in df.columns:
             return "Error: Specified columns not found in the DataFrame."
-        
-        # Plot the bar graph
-        fig, ax = plt.subplots(figsize=(12, 8))
-        df.plot(kind='bar', x=x_column, y=y_column, ax=ax, legend=False)
-        ax.set_title(f"{y_column} Distribution by {x_column}")
-        ax.set_xlabel(x_column)
-        ax.set_ylabel(y_column)
-        plt.xticks(rotation=45, ha="right", fontsize=10)
+
+        # Create a bar graph with simple visualization
+        fig, ax = plt.subplots(figsize=(10, 6))
+        ax.bar(df[x_column], df[y_column], color='royalblue', edgecolor='black')
+        ax.set_title(f"{y_column} by {x_column}", fontsize=14, fontweight='bold')
+        ax.set_xlabel(x_column, fontsize=12)
+        ax.set_ylabel(y_column, fontsize=12)
+        plt.xticks(rotation=30, ha="right", fontsize=10)
+        plt.grid(axis='y', linestyle='--', alpha=0.7)
         plt.tight_layout()
 
-        # Save the graph to a buffer
+        # Save and return image
         buffer = BytesIO()
         plt.savefig(buffer, format="png")
         buffer.seek(0)
-        img = Image.open(buffer)
-        return img
+        return Image.open(buffer)
     except Exception as e:
         return f"Error generating graph: {e}"
 
+# Function to fetch Salesforce data and return table and graph
+def generate_salesforce_data():
+    df = pd.DataFrame({
+        "Category": ["A", "B", "C", "D"],
+        "Stock": [150, 200, 100, 80]
+    })  # Simulated data; replace with real Salesforce data
+
+    if df is not None:
+        table_component = df.to_html(index=False)
+        bar_graph_image = generate_dynamic_bar_graph(df, "Category", "Stock")
+        return table_component, bar_graph_image
+    return "<p>No Data Available</p>", None
+
+
 # Unified function to handle image processing and Salesforce interaction
 def process_image(image, mode, entry_type, quantity):
     extracted_text = extract_text(image)