Delete app.py

app.py

@@ -1,53 +0,0 @@
-import streamlit as st  #Web App
-from PIL import Image #Image Processing
-import numpy as np #Image Processing 
-from transformers import AutoProcessor, Blip2ForConditionalGeneration
-import torch
-from peft import PeftModel, PeftConfig
-
-
-#title
-st.title("Image Captioner - Caption the images")
-
-st.markdown("Link to the model - [Image-to-Caption-App on 🤗 Spaces](https://huggingface.co/spaces/Shrey23/Image-Captioning)")
-
-#image uploader
-image = st.file_uploader(label = "Upload your image here",type=['png','jpg','jpeg'])
-
-@st.cache_data
-def load_model():
-    peft_model_id = "Shrey23/Image-Captioning"
-    config = PeftConfig.from_pretrained(peft_model_id)
-    model = Blip2ForConditionalGeneration.from_pretrained(config.base_model_name_or_path,  torch_dtype=torch.float16) #, device_map="auto", load_in_8bit=True
-    model = PeftModel.from_pretrained(model, peft_model_id)
-
-    processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
-    return processor, model
-
-
-processor, model = load_model() #load model
-
-    
-
-if image is not None:
-
-    input_image = Image.open(image) #read image
-    st.image(input_image) #display image
-
-    with st.spinner("🤖 AI is at Work! "):
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
-        pixel_values = inputs.pixel_values
-
-
-        generated_ids = model.generate(pixel_values=pixel_values, max_length=25)
-        generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-
-        st.write(generated_caption)
-        
-    st.success("Here you go!")
-    st.balloons()
-else:
-    st.write("Upload an Image")
-
-st.caption("Made with ❤️ by @1littlecoder. Credits to 🤗 Spaces for Hosting this ")