Update app.py

app.py

@@ -8,9 +8,53 @@ os.environ["KERAS_BACKEND"] = "tensorflow"
 print("loading file")
 model = keras.saving.load_model("hf://kim1688/casting_defect_resnet50")
 
+def upload_images(image_paths):
+  docs.clear()
+  df = pd.DataFrame(columns=["Index", "File", "Result"])
+  for i in range(len(image_paths)):
+    df.loc[i] = [str(i+1), image_paths[i].split("/")[-1], predict(image_paths[i])]
+    docs.append([str(i+1), image_paths[i].split("/")[-1], predict(image_paths[i])])
+  return [df, gr.Button(visible=True), gr.DownloadButton(label="Download report", visible=True)]
+
+# Function to preprocess image and predict
+def predict(image_path):
+  img = keras.utils.load_img(image_path, target_size=(300, 300))
+  img_array = keras.utils.img_to_array(img)
+  img_array = keras.ops.expand_dims(img_array, 0)
+  prediction = model.predict(img_array)
+  class_names = ["Defective", "Ok"]  # Class 0: def, Class 1: ok
+  predicted_class = class_names[1] if prediction > 0.5 else class_names[0]
+  return predicted_class
+
+def generate_docs():
+  document = Document()
+  document.add_heading("Casting Report", 0)
+  table = document.add_table(rows=1, cols=3)
+  hdr_cells = table.rows[0].cells
+  hdr_cells[0].text = "Index"
+  hdr_cells[1].text = "File"
+  hdr_cells[2].text = "Result"
+  for i in range(len(docs)):
+    row_cells = table.add_row().cells
+    row_cells[0].text = docs[i][0]
+    row_cells[1].text = docs[i][1]
+    row_cells[2].text = docs[i][2]
+  document.save("casting_report.docx")
+  return [gr.UploadButton(visible=True), gr.DownloadButton(visible=True)]
+
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        f = gr.File(file_count="multiple", file_types=[".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff"])
+        u = gr.Button("Upload files", visible=True)
+        d = gr.DownloadButton("Download report", visible=True)
+    r = gr.DataFrame(headers=["Index", "File", "Result"])
 
+    u.click(upload_images, f, [r, u, d])
+    d.click(generate_docs, None, [u, d])
 
 
+"""
 def upload_images(image_paths):
   df = pd.DataFrame(columns=["Index", "File", "Result"])
   for i in range(len(image_paths)):
@@ -32,5 +76,6 @@ demo = gr.Interface(
     gr.File(file_count="multiple", file_types=[".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff"]), 
     gr.DataFrame(headers=["Index", "File", "Result"])
 )
+"""
 
 demo.launch(share=True, debug=True)