Update app.py

app.py

@@ -1,6 +1,41 @@
+from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, PreTrainedTokenizerFast
+
+model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+vit_feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
+tokenizer = PreTrainedTokenizerFast.from_pretrained("distilgpt2")
+
+def vit2distilgpt2(img):
+    pixel_values = vit_feature_extractor(images=img, return_tensors="pt").pixel_values
+    encoder_outputs = model.generate(pixel_values.to('cpu'), num_beams=5, num_return_sequences=3)
+    generated_sentences = tokenizer.batch_decode(encoder_outputs, skip_special_tokens=True)
+
+    return generated_sentences
+
 import gradio as gr
 
-gr.load(
-   "models/Salesforce/blip-image-captioning-base",
-   provider="hf-inference",
-).launch()
+inputs = [
+    gr.inputs.Image(type="pil", label="Original Images")
+]
+
+outputs = [
+    gr.outputs.Textbox(label="Caption 1"),
+    
+]
+
+title = "Image Captioning using ViT + GPT2"
+description = "ViT and GPT2 are used to generate Image Caption for the uploaded image. COCO DataSet is used for Training"
+examples = [
+    ["Image1.png"],
+    ["Image2.png"],
+    ["Image3.png"]
+]
+
+gr.Interface(
+    vit2distilgpt2,
+    inputs,
+    outputs,
+    title=title,
+    description=description,
+    examples=examples,
+    theme="huggingface",
+).launch(debug=True, enable_queue=True)