Update app.py

app.py

@@ -1,54 +1,89 @@
-import gradio as gr
-import streamlit as st
+# import gradio as gr
+# import streamlit as st
+# import torch 
+# import re 
+# from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 
+
+# device='cpu'
+# encoder_checkpoint = "ydshieh/vit-gpt2-coco-en"
+# decoder_checkpoint = "ydshieh/vit-gpt2-coco-en"
+# model_checkpoint = "ydshieh/vit-gpt2-coco-eng"
+# feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
+# tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
+# model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
+
+# def predict(image,max_length=64, num_beams=4):
+#     input_image = Image.open(image)
+#     model.eval()
+#     pixel_values = feature_extractor(images=[input_image], return_tensors="pt").pixel_values
+#     with torch.no_grad():
+#         output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences
+#     preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+#     preds = [pred.strip() for pred in preds]  
+#     return preds[0]
+    
+#   # image = image.convert('RGB')
+#   # image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
+#   # clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
+#   # caption_ids = model.generate(image, max_length = max_length)[0]
+#   # caption_text = clean_text(tokenizer.decode(caption_ids))
+#   # return caption_text 
+
+# # st.title("Image to Text using Lora")
+
+# inputs = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True)
+# output = gr.outputs.Textbox(type="text",label="Captions")
+# description = "NTT Data Bilbao team"
+# title = "Image to Text using Lora"
+
+# interface = gr.Interface(
+#         fn=predict,
+#         description=description,
+#         inputs = inputs,
+#         theme="grass",
+#         outputs=output,
+#         title=title,
+#     )
+# interface.launch(debug=True)
+
 import torch 
 import re 
+import gradio as gr
 from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 
 
-# def greet(name):
-#     return "Hello " + name + "!!"
-
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-# iface.launch()
-
 device='cpu'
-encoder_checkpoint = "ydshieh/vit-gpt2-coco-en"
-decoder_checkpoint = "ydshieh/vit-gpt2-coco-en"
-model_checkpoint = "ydshieh/vit-gpt2-coco-eng"
+encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
+decoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
+model_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
 feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
 tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
 model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
 
 
 def predict(image,max_length=64, num_beams=4):
-    input_image = Image.open(image)
-    model.eval()
-    pixel_values = feature_extractor(images=[input_image], return_tensors="pt").pixel_values
-    with torch.no_grad():
-        output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences
-    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-    preds = [pred.strip() for pred in preds]  
-    return preds[0]
-    
-  # image = image.convert('RGB')
-  # image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
-  # clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
-  # caption_ids = model.generate(image, max_length = max_length)[0]
-  # caption_text = clean_text(tokenizer.decode(caption_ids))
-  # return caption_text 
+  image = image.convert('RGB')
+  image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
+  clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
+  caption_ids = model.generate(image, max_length = max_length)[0]
+  caption_text = clean_text(tokenizer.decode(caption_ids))
+  return caption_text 
+
 
-# st.title("Image to Text using Lora")
 
-inputs = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True)
-output = gr.outputs.Textbox(type="text",label="Captions")
-description = "NTT Data Bilbao team"
-title = "Image to Text using Lora"
+input = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True)
+output = gr.outputs.Textbox(type="auto",label="Captions")
+examples = [f"example{i}.jpg" for i in range(1,7)]
 
+title = "Image Captioning "
+description = "Made by : shreyasdixit.tech"
 interface = gr.Interface(
+            
         fn=predict,
         description=description,
-        inputs = inputs,
+        inputs = input,
         theme="grass",
         outputs=output,
+        examples = examples,
         title=title,
     )
 interface.launch(debug=True)