added streamlit frontend application

frontend.py

@@ -0,0 +1,70 @@
+import os
+import io
+import cv2
+import requests
+import numpy as np
+import streamlit as st
+
+from PIL import Image
+from skimage.io import imread
+
+def infer() -> None:
+    st.title("IAM Handwriting recognition app")
+
+    image_file_buffer = st.sidebar.file_uploader("Select input image", type=["png"])
+    # read the image
+    if image_file_buffer is not None:
+        image = Image.open(image_file_buffer)
+        image_array = np.array(image)
+        st.image(image_array, caption=f"Input image: {image_file_buffer.name}")
+    else:
+        st.write("Input image: not selected")
+
+    # run inference when the option is invoked by the user
+    infer_button = st.sidebar.button("Run inference")
+    if infer_button:
+        files = {"image_file": (image_file_buffer.name, image_file_buffer.getvalue())}
+
+        # if the deployment is on local machine
+        response = requests.post(
+            "https://abhishekrs4-handwriting-recognition.hf.space/predict",
+            files=files,
+        )
+
+        # if the deployment is on hugging face
+        # response = requests.post(
+        #     "http://127.0.0.1:7860/predict",
+        #     files=files,
+        # )
+
+        st.write("The following is the prediction")
+        st.write(response.json())
+    return
+
+def app_info() -> None:
+    st.title("App info")
+    st.markdown("_Task - IAM Handwriting recognition_")
+    st.markdown("_Project repo - [https://github.com/AbhishekRS4/Handwriting_Recognition](https://github.com/AbhishekRS4/Handwriting_Recognition)_")
+    st.markdown("_Dataset - [IAM dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database)_")
+    st.header("Brief description of the project")
+    st.write("The IAM dataset contains images of handwritten text in English language.")
+    st.write("A custom architecture is modeled for the recognition task.")
+    st.write("The best performing model has been used for the deployed application.")
+    return
+
+app_modes = {
+    "App Info" : app_info,
+    "IAM Handwriting Recognition Inference App": infer,
+}
+
+def start_app() -> None:
+    selected_mode = st.sidebar.selectbox("Select mode", list(app_modes.keys()))
+    app_modes[selected_mode]()
+    return
+
+def main() -> None:
+    start_app()
+    return
+
+if __name__ == "__main__":
+    main()