Delete app.py

app.py

@@ -1,89 +0,0 @@
-import streamlit as st
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-import json
-from tensorflow.keras.applications.vgg16 import VGG16,preprocess_input
-from tensorflow.keras.preprocessing.image import img_to_array
-from tensorflow.keras.preprocessing.text import Tokenizer,tokenizer_from_json
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.models import Model
-
-from keras.models import load_model
-
-# Load the .h5 model
-model = load_model('image_caption.h5')
-
-with open('tokenizer_config.json', 'r') as f:
-    tokenizer_config = json.load(f)
-tokenizer = tokenizer_from_json(tokenizer_config)
-# tokenizer.word_index = eval(tokenizer_config)['word_index']
-
-max_length=35
-# Load pre-trained model
-vgg_model = VGG16()
-vgg_model = Model(inputs=vgg_model.inputs, outputs=vgg_model.layers[-2].output)
-
-# Set Streamlit configurations
-st.set_page_config(page_title="Image Classifier App", layout="wide")
-
-
-# Function to preprocess the input image
-def preprocess_image(image):
-    image = image.convert("RGB")
-    image = image.resize((224, 224))
-    image = img_to_array(image)
-    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
-    image = preprocess_input(image)
-    return image
-
-# Function to make predictions on the input image
-def predict(image):
-    image = preprocess_image(image)
-    feature = vgg_model.predict(image, verbose=0)
-    preds = predict_caption(model, feature, tokenizer, max_length)
-    preds=preds[8:-7]
-    return preds
-
-def idx_word(integer,tok):
-    for word,index in tok.word_index.items():
-        if index== integer:
-            return word
-    return None
-
-def predict_caption(model,image,tok,max_len):
-    in_text="startseq"
-    for i in range(max_len):
-        seq=tok.texts_to_sequences([in_text])[0]
-        seq=pad_sequences([seq],max_len)
-        yhat = model.predict([image, seq], verbose=0)
-        yhat = np.argmax(yhat)
-        word = idx_word(yhat, tok)
-        if word is None:
-            break
-        in_text += " " + word
-        if word == 'endseq':
-            break
-    return in_text
-
-# Streamlit app
-def main():
-    st.title("Image Captioning App")
-    st.write("Upload an image and the app will predict its class.")
-
-    uploaded_image = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
-
-    if uploaded_image is not None:
-        image = Image.open(uploaded_image)
-        st.image(image, caption='Uploaded Image', use_column_width=True)
-        st.write("")
-
-        if st.button("Generate Caption"):
-            with st.spinner("Generating..."):
-                predictions = predict(image)
-
-            st.write(f"Top Caption:{predictions}")
-
-# Run the app
-if __name__ == "__main__":
-    main()