Delete app.py

app.py

@@ -1,86 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import streamlit as st
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-from transformers import AutoTokenizer, VisionEncoderDecoderModel, ViTFeatureExtractor
-
-# Load the Model
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-
-# Load the feature extractor and tokenizer
-extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-tokeniser = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-
-
-def preprocess_image(image_path):
-    # Load the image using OpenCV
-    image = cv2.imread(image_path)
-
-    # Resize the image while maintaining the aspect ratio
-    resized_image = cv2.resize(image, (224, 224))
-
-    # Add an extra dimension to represent the batch size (assuming a single image)
-    preprocessed_image = np.expand_dims(resized_image, axis=0)
-
-    return preprocessed_image
-
-
-def generate_captions(image):
-    generated_caption = tokeniser.decode(model.generate(extractor(image, return_tensors="pt").pixel_values.to("cpu"))[0])
-    sentence = generated_caption
-    text_to_remove = "<|endoftext|>"
-    generated_caption = sentence.replace(text_to_remove, "")
-    return generated_caption
-
-# Load the pre-trained model and tokenizer
-model_name = "gpt2"
-tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-model = GPT2LMHeadModel.from_pretrained(model_name)
-
-# Define the Streamlit app
-def generate_paragraph(prompt):
-    # Tokenize the prompt
-    input_ids = tokenizer.encode(prompt, return_tensors="pt")
-
-    # Generate the paragraph
-    output = model.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)
-    return paragraph
-
-# Streamlit app
-def main():
-    # Set Streamlit app title and description
-    st.title("Paragraph Generation From Context of an Image")
-    st.subheader("Upload the Image to generate a paragraph.")
-
-    # create file uploader
-    uploaded_file  = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-    # check if file has been uploaded
-    if uploaded_file is not None:
-        # load the image
-        image_open = Image.open(uploaded_file).convert("RGB")
-        
-        img = preprocess_image(image_open)
-
-        # context as prompt
-        prompt = generate_captions(img)
-        st.write("The Context is:", prompt)
-
-        # display the image
-        st.image(uploaded_file)
-    
-    # Generate button
-    if st.button("Generate"):
-        if prompt:
-            generated_paragraph = generate_paragraph(prompt)
-            st.success("Generated Paragraph:")
-            st.write(generated_paragraph)
-        else:
-            st.warning("Please enter a prompt.")
-
-if __name__ == "__main__":
-    main()