Update app.py

app.py

@@ -1,16 +1,16 @@
 import gradio as gr
-from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 
 # Initialize the image classification pipeline
 classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
 
-# Initialize the tokenizer and model for the generative text (GPT-like model)
-model_name = "gpt2"  # Use GPT-2 model for demonstration
-tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-model = GPT2LMHeadModel.from_pretrained(model_name)
+# Initialize the tokenizer and model for the generative text
+model_name = "EleutherAI/gpt-neo-2.7B"  # Using GPT-Neo for demonstration
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
 def generate_tweet(label):
-    # Craft a prompt that naturally encourages engaging and relevant tweet content
+    # Generate a tweet about the label
     prompt = f"write a tweet about {label}"
 
     inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=True)
@@ -31,7 +31,7 @@ def predict(image):
     return tweet
 
 title = "Image Classifier to Generative Tweet"
-description = "This demo recognizes and classifies images using the 'google/vit-base-patch16-224' model and generates a tweet about the top prediction using GPT-2 for generating creative and engaging content."
+description = "This demo recognizes and classifies images using the 'google/vit-base-patch16-224' model and generates a tweet about the top prediction using the GPT-Neo model for generating creative and engaging content."
 input_component = gr.Image(type="pil", label="Upload an image here")
 output_component = gr.Textbox(label="Generated Promotional Tweet")