url download

app.py

@@ -3,6 +3,7 @@ from fastapi.responses import JSONResponse, FileResponse
 from fastapi.middleware.cors import CORSMiddleware
 from PIL import Image
 import io
+from io import BytesIO
 import requests
 import sqlite3
 from pydantic import BaseModel, EmailStr
@@ -220,6 +221,84 @@ async def generate_excel(predictions: List[Prediction]):
     sheet = workbook.active
     sheet.title = "Predictions"
 
+    # Add headers
+    headers = ["Category", "Confidence", "Predicted Brand", "Price", "Image URL", "Details", "Detected Text"]
+    sheet.append(headers)
+
+    # Set header style and alignment
+    for cell in sheet[1]:
+        cell.alignment = Alignment(horizontal="center", vertical="center", wrap_text=True)
+    sheet.row_dimensions[1].height = 30  # Adjust header row height
+
+    # Set column widths based on data type
+    column_widths = {
+        "A": 20,  # Category
+        "B": 15,  # Confidence
+        "C": 40,  # Predicted Brand
+        "D": 15,  # Price
+        "E": 50,  # Image URL
+        "F": 30,  # Details 
+        "G": 30   # Detected Text
+    }
+    for col, width in column_widths.items():
+        sheet.column_dimensions[col].width = width
+
+    # Add prediction rows
+    for idx, prediction in enumerate(predictions):
+        row_index = idx + 2  # Start from the second row
+
+        # Add data to the row
+        sheet.append([
+            prediction.category,
+            prediction.confidence,
+            prediction.predicted_brand,
+            prediction.price,
+            prediction.image_url,
+            prediction.details,
+            prediction.detected_text,
+        ])
+
+        # Adjust row height for multiline text
+        sheet.row_dimensions[row_index].height = 180  # Default height for rows
+
+        # Wrap text in all cells of the row
+        for col_idx in range(1, 8):  # Columns A to G
+            cell = sheet.cell(row=row_index, column=col_idx)
+            cell.alignment = Alignment(wrap_text=True, vertical="top")
+
+        # If image URL is provided, download it
+        if prediction.image_url:
+            try:
+                response = requests.get(prediction.image_url)
+                img = ExcelImage(BytesIO(response.content))
+                img.width, img.height = 160, 160  # Resize image to fit into the cell
+                img_cell = f"G{row_index}"  # Image column
+                sheet.add_image(img, img_cell)
+            except requests.exceptions.RequestException as e:
+                print(f"Error downloading image: {e}")
+                # Optionally add a placeholder image or text
+
+    # Save the Excel file
+    excel_file_path = "/tmp/predictions_with_images.xlsx"
+    workbook.save(excel_file_path)
+
+    # Serve the Excel file as a response
+    return FileResponse(
+        excel_file_path,
+        media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+        filename="predictions_with_images.xlsx"
+    )
+
+
+@app.post("/generate-excel2/")
+async def generate_excel(predictions: List[Prediction]):
+    print('Generate excel called')
+
+    # Create an Excel workbook
+    workbook = Workbook()
+    sheet = workbook.active
+    sheet.title = "Predictions"
+
     # Add headers
     headers = ["Category", "Confidence", "Predicted Brand", "Price", "Image URL", "Details", "Detected Text", ]
     sheet.append(headers)