Update app.py

app.py

@@ -1,5 +1,7 @@
 # Import gradio - app framework
 import gradio as gr
+import os
+import random
 
 # Import pandas datasets, transformers, torch
 import pandas as pd
@@ -32,6 +34,15 @@ samples = dataset.select(range(20))
 #Convert to dataframe
 df = pd.DataFrame(samples)
 
+# Direct to Photos folder
+IMAGE_FOLDER = "Photos"
+
+image_paths = [
+    os.path.join(IMAGE_FOLDER, f)
+    for f in os.listdir(IMAGE_FOLDER)
+    if f.lower().endswith((".jpg", ".jpeg", ".png"))
+]
+
 ## print ("Print to show the 20 images available.")
 ## print ("The app will then select an image for further exploration.")
 ## print(df.head(20))
@@ -50,11 +61,18 @@ trans_model = MarianMTModel.from_pretrained(model_name)
 #Configure captioning function
 def caption_random_image():
 
-    # pick random row
-    sample = df.sample(1).iloc[0]
+    # pick random row - from DF
+    ##sample = df.sample(1).iloc[0]
+
+    # Pick a random image path
+    img_path = random.choice(image_paths)
+
+    # Load into PIL
+    image = Image.open(img_path).convert("RGB")
+
 
-    # 'image' field contains an actual PIL image
-    image = sample["image"]
+    # 'image' field contains an actual PIL image - for DF
+    ##image = sample["image"]
 
     # Unconditional image captioning
     inputs = processor(image, return_tensors="pt")