minor fixes

README.md

@@ -36,6 +36,6 @@ The following files are present in this repository:
 
 - `app.py`: The main Streamlit app file to run directly.
 - `requirements.txt`: The list of Python dependencies required by the app.
-- `model.py`: Contains the code for loading and using the pre-trained model.
-- `app_endpoint.py`: Contains api_endpoint for the prediction.
+- `model.py`: Contains the code for loading and using the model.
+- `app_endpoint.py`: Contains FASTAPI endpoint for the prediction.
 - `app_with_fastapi.py`: Contains the code for the Streamlit app with FastAPI endpoint.

app.py

@@ -1,9 +1,8 @@
 from PIL import Image
 import streamlit as st
-import requests
 import model
 
-# Streamlit layout
+
 st.title("CIFAR10 Prediction")
 
 uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

app_with_fastapi.py

@@ -1,7 +1,6 @@
 import requests
 import streamlit as st
 
-# Streamlit layout
 st.title("CIFAR10 Prediction")
 
 HOST = "http://localhost:8000"

model.py

@@ -1,12 +1,14 @@
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 import torch
 
+# Loading processor and model
 processor = AutoImageProcessor.from_pretrained("heyitskim1912/AML_A2_Q4")
 model = AutoModelForImageClassification.from_pretrained("heyitskim1912/AML_A2_Q4")
 
 def predict(image_pil):
     inputs = processor(image_pil, return_tensors="pt")
 
+    #Inference
     with torch.no_grad():
         logits = model(**inputs).logits