Update app.py

app.py

@@ -1,3 +1,4 @@
+#load all necessary libraries, Don't forget to check the system requirements or dependencies
 import torch
 import numpy as np
 from PIL import Image
@@ -9,6 +10,7 @@ model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-cap
 extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 tokeniser = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 
+# define the function
 def generate_captions(image):
     generated_caption = tokeniser.decode(model.generate(extractor(image, return_tensors="pt").pixel_values.to("cpu"))[0])
     sentence = generated_caption
@@ -21,7 +23,7 @@ model_name = "gpt2"
 tokenizer_2 = GPT2Tokenizer.from_pretrained(model_name)
 model_2 = GPT2LMHeadModel.from_pretrained(model_name)
 
-# Define the Streamlit app
+# Define the Function
 def generate_paragraph(prompt):
     # Tokenize the prompt
     input_ids = tokenizer_2.encode(prompt, return_tensors="pt")
@@ -33,14 +35,14 @@ def generate_paragraph(prompt):
     paragraph = tokenizer_2.decode(output[0], skip_special_tokens=True)
     return paragraph.capitalize()
 
-# Streamlit app
+# Define the 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.")
+    st.title("Have a Picture! Don't Know how to Describe?. Here's Some Help")
+    st.subheader("Upload the Picture to get Catchy Description.")
 
     # create file uploader
-    uploaded_file  = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    uploaded_file  = st.file_uploader("Drag and Drop or Upload the picture", type=["jpg", "jpeg", "png"])
 
     # check if file has been uploaded
     if uploaded_file is not None:
@@ -49,13 +51,12 @@ def main():
         
         # context as prompt
         prompt = generate_captions(image)
-        st.write("The Context is:", prompt)
 
         # display the image
         st.image(uploaded_file)
-    
-        generated_paragraph = generate_paragraph(prompt)
 
+        # generate and display the description
+        generated_paragraph = generate_paragraph(prompt)
         st.write(generated_paragraph)
 
 if __name__ == "__main__":