Update app.py

app.py

@@ -10,8 +10,8 @@ tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning"
 
 # Load the pre-trained model and tokenizer
 model_name = "gpt2"
-tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer_1 = GPT2Tokenizer.from_pretrained(model_name)
+model_2 = GPT2LMHeadModel.from_pretrained(model_name)
 
 def generate_captions(image):
     image = Image.open(image).convert("RGB")
@@ -24,13 +24,13 @@ def generate_captions(image):
 # Define the Streamlit app
 def generate_paragraph(prompt):
     # Tokenize the prompt
-    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+    input_ids = tokenizer_1.encode(prompt, return_tensors="pt")
 
     # Generate the paragraph
-    output = model.generate(input_ids, max_length=200, num_return_sequences=1, early_stopping=True)
+    output = model_2.generate(input_ids, max_length=200, num_return_sequences=1, early_stopping=True)
 
     # Decode the generated output into text
-    paragraph = tokenizer.decode(output[0], skip_special_tokens=True)
+    paragraph = tokenizer_1.decode(output[0], skip_special_tokens=True)
     return paragraph
     
 # create the Streamlit app