Update app.py

app.py

@@ -5,7 +5,7 @@ import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from easyocr import Reader
 
-# Load the OCR model and text explanation model (gpt-2 as an example)
+# Load the OCR model and text explanation model (GPT-2 as an example)
 ocr_reader = Reader(['en'])
 explainer = AutoModelForSequenceClassification.from_pretrained("gpt2")
 
@@ -16,10 +16,14 @@ def extract_text(image):
 # Define a function to explain the extracted text
 def explain_text(text):
     tokenizer = AutoTokenizer.from_pretrained("gpt2")
-    
+
+    # Convert the text to a string if necessary
+    if not isinstance(text, str):
+        text = str(text)
+
     # Encode the text and convert to PyTorch tensors
-    inputs = tokenizer([text], return_tensors="pt", padding=True, truncation=True, max_length=512)
-    
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+
     input_ids = inputs["input_ids"]
     attention_mask = inputs["attention_mask"]
 
@@ -36,7 +40,7 @@ uploaded_file = st.file_uploader("Upload an image:")
 if uploaded_file is not None:
     # Read the uploaded image
     image = Image.open(uploaded_file)
-    
+
     # Process the image and convert to NumPy array if necessary
     # image = process_image(image)