Re-entered working code (Select photo from folder, generate caption, then translate.)

app.py

@@ -2,12 +2,21 @@
 import gradio as gr
 
 
+# Two image datasources are available. 
+# Minor adjustments (add/remove # to deactivate/activate) to switch between datasources.
+# AA comments refer to images in the DataFrame / from Coco database
+# BB comments refer to images stored in local Gradio app folder
+
+
+# Import os and random to support random selection of image (from folder)
+import os
+import random
+
+
 # Import pandas datasets, transformers, torch
 import pandas as pd
-#import torch
-#import tensorflow as tf
-from datasets import load_dataset
 
+from datasets import load_dataset
 
 from transformers import (
     BlipProcessor, 
@@ -18,13 +27,11 @@ from transformers import (
     MarianTokenizer
 )
 
-
-
-
 from PIL import Image
 import torch
 
 
+# AA: Load dataset. Initial image source. 
 # Get merve/coco dataset
 from datasets import load_dataset
 
@@ -41,9 +48,15 @@ samples = dataset.select(range(20))
 df = pd.DataFrame(samples)
 
 
-## print ("Print to show the 20 images available.")
-## print ("The app will then select an image for further exploration.")
-## print(df.head(20))
+# BB: 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"))
+]
 
 
 #Load the image captioning model (Salesforce/blip-image-captioning-large)
@@ -57,20 +70,27 @@ trans_tokenizer = MarianTokenizer.from_pretrained(model_name)
 trans_model = MarianMTModel.from_pretrained(model_name)
 
 
+#Configure captioning function
 
+def caption_random_image():
 
 
+    # AA: pick random row - from DF
+    ##sample = df.sample(1).iloc[0]
+
+
+    # BB: Pick a random image path - image from folder
+    img_path = random.choice(image_paths)
 
-#Configure captioning function
-def caption_random_image():
 
+    # BB: Load into PIL - image from folder - image from folder
+    image = Image.open(img_path).convert("RGB")
 
-    # pick random row
-    sample = df.sample(1).iloc[0]
 
 
-    # 'image' field contains an actual PIL image
-    image = sample["image"]
+
+    # AA: Image - for DF
+    ##image = sample["image"]
 
 
     # Unconditional image captioning
@@ -101,12 +121,8 @@ demo = gr.Interface(
         gr.Textbox(label="Caption (Spanish)")
     ],
     title="Image Captioning (with English to Spanish translation)",
-    description="Selects a random COCO image from 20 samples; generates a BLIP caption; then translates the (English) caption to Spanish."
+    description="Selects a random image (from either the local folder or henryscheible/coco data subset); generates a BLIP caption; then translates the (English) caption to Spanish."
 )
 
 
-
-
-
-
 demo.launch()