Update_app.py

app.py

@@ -1,92 +1,51 @@
+import os
+import pandas as pd
+import numpy as np
+from PIL import Image
 import streamlit as st
 import requests
 import io
 
 
 # Designing the interface
-st.title("Medical Image Captioning")
+st.title("RadiXGPT")
 
-st.sidebar.markdown(
-    """
-    This project features 3 different Medical image captioning models.
-    Two of the use the InceptionV3 architecture to do feature extraction and then generate the captions using an LSTM model.
-    The difference between these two is that the first one uses InceptionV3 trained on ImageNet data and outputs 2048 features.
-    The second one is based on a retrained version of InceptionV3 that uses the CUI data from the ROCO dataset to extract 745 features from the images.
-    The final model is transformer based on...
-    """
-)
 
 with st.spinner('Loading objects ...'):
     from model import *
 
 random_image_id = get_random_image_id()
 
-st.sidebar.title("Select a sample image")
-sample_image_id = st.sidebar.selectbox(
-    "Please choose a sample image",
-    sample_image_ids
-)
-
-st.sidebar.title("Select a model Type")
-model_type = st.sidebar.selectbox(
-    "Please choose a model",
-    ['Pretrained Inception', 'Retrained Inception', 'Transformer']
-)
-
-inception, lstm = fetch_model(model_type)
-word2Index, index2Word, variable_params = fetch_auxiliary_files(model_type)
-max_len = variable_params['max_caption_len']
-
-if st.sidebar.button("Random ROCO (test) images"):
-    random_image_id = get_random_image_id()
-    sample_image_id = "None"
-
-bytes_data = None
-with st.sidebar.form("file-uploader-form", clear_on_submit=True):
-    uploaded_file = st.file_uploader("Choose a file")
-    submitted = st.form_submit_button("Upload")
-    if submitted and uploaded_file is not None:
-        bytes_data = io.BytesIO(uploaded_file.getvalue())
-
-if (bytes_data is None) and submitted:
-
-    st.write("No file is selected to upload")
-
-else:
-
-    image_id = random_image_id
-    if sample_image_id != "None":
-        assert type(sample_image_id) == int
-        image_id = sample_image_id
-
-    sample_name = f"ROCO_{str(image_id).zfill(5)}.jpg"
-    sample_path = os.path.join(sample_dir, sample_name)
-
-    if bytes_data is not None:
-        image = Image.open(bytes_data)
-    elif os.path.isfile(sample_path):
-        image = Image.open(sample_path)
-
-    width, height = 299, 299
-    resized = image.resize(size=(width, height))
-
-    if bytes_data is None:
-        st.markdown(f"ROCO_{str(image_id).zfill(5)}.jpg")
-    show = st.image(resized)
-    show.image(resized, '\n\nSelected Image')
-
-    # For newline
-    st.sidebar.write('\n')
-
-    with st.spinner('Generating image caption ...'):
-        st.header(f'Predicted caption:\n\n')
-
-        preprocessed_img = preprocess_image_inception(resized)
-        features = extract_features(inception, preprocessed_img)
-        caption = generate_caption(lstm, features, max_len, word2Index, index2Word)
-        st.subheader(caption)
-
-    st.sidebar.header("Model predicts: ")
-    st.sidebar.write(f"{caption}")
-
-    image.close()
+sample_name = f"ROCO_{str(random_image_id).zfill(5)}.jpg"
+sample_path = os.path.join(sample_dir, sample_name)
+
+image = Image.open(sample_path)
+width, height = 299, 299
+resized = image.resize(size=(width, height))
+st.markdown(f"ROCO_{str(random_image_id).zfill(5)}.jpg")
+show = st.image(resized)
+show.image(resized, '\n\nSelected Image')
+
+# For newline
+st.sidebar.write('\n')
+
+with st.spinner('Generating image caption ...'):
+    st.header(f'Predicted caption:\n\n')
+
+    preprocessed_img = preprocess_image_inception(resized)
+    features = extract_features(inception, preprocessed_img)
+    
+    # Load the transformer model
+    transformer, _, _ = fetch_model('Transformer')
+    # Fetch the auxiliary files
+    word2Index, index2Word, variable_params = fetch_auxiliary_files('Transformer')
+    max_len = variable_params['max_caption_len']
+    # Generate the caption
+    caption = generate_caption(transformer, features, max_len, word2Index, index2Word)
+    
+    st.subheader(caption)
+
+st.sidebar.header("Model predicts: ")
+st.sidebar.write(f"{caption}")
+
+image.close()