Update app.py

app.py

@@ -3,31 +3,8 @@ import os
 from PIL import Image
 import numpy as np
 import pickle
-import tensorflow as tf
-from tensorflow.keras.preprocessing import image
-from tensorflow.keras.layers import GlobalMaxPooling2D
-from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
-from sklearn.neighbors import NearestNeighbors
-from numpy.linalg import norm
 from chatbot import Chatbot  # Assuming you have a chatbot module
 
-# Define function for feature extraction
-def feature_extraction(img_path, model):
-    img = image.load_img(img_path, target_size=(224, 224))
-    img_array = image.img_to_array(img)
-    expanded_img_array = np.expand_dims(img_array, axis=0)
-    preprocessed_img = preprocess_input(expanded_img_array)
-    result = model.predict(preprocessed_img).flatten()
-    normalized_result = result / norm(result)
-    return normalized_result
-
-# Define function for recommendation
-def recommend(features, feature_list):
-    neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
-    neighbors.fit(feature_list)
-    distances, indices = neighbors.kneighbors([features])
-    return indices
-
 # Function to save uploaded file
 def save_uploaded_file(uploaded_file):
     try:
@@ -46,17 +23,6 @@ def show_dashboard():
     chatbot = Chatbot()
     chatbot.load_data()
 
-    # Load ResNet model for image feature extraction
-    model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
-    model.trainable = False
-    model = tf.keras.Sequential([
-        model,
-        GlobalMaxPooling2D()
-    ])
-
-    feature_list = np.array(pickle.load(open('embeddings.pkl', 'rb')))
-    filenames = pickle.load(open('filenames.pkl', 'rb'))
-
     # File upload section
     uploaded_file = st.file_uploader("Choose an image")
     if uploaded_file is not None:
@@ -65,24 +31,26 @@ def show_dashboard():
             display_image = Image.open(uploaded_file)
             st.image(display_image)
 
-            # Feature extraction
-            features = feature_extraction(os.path.join("uploads", uploaded_file.name), model)
+            # Generate image caption
+            image_path = os.path.join("uploads", uploaded_file.name)
+            caption = chatbot.generate_image_caption(image_path)
+            st.write("Generated Caption:", caption)
 
-            # Recommendation
-            indices = recommend(features, feature_list)
+            # Use caption to get product recommendations
+            _, recommended_products = chatbot.generate_response(caption)
 
-            # Display recommended products using loaded images from the dataset
+            # Display recommended products
             col1, col2, col3, col4, col5 = st.columns(5)
             with col1:
-                st.image(chatbot.images[indices[0][0]])
+                st.image(chatbot.images[recommended_products[0]['corpus_id']])
             with col2:
-                st.image(chatbot.images[indices[0][1]])
+                st.image(chatbot.images[recommended_products[1]['corpus_id']])
             with col3:
-                st.image(chatbot.images[indices[0][2]])
+                st.image(chatbot.images[recommended_products[2]['corpus_id']])
             with col4:
-                st.image(chatbot.images[indices[0][3]])
+                st.image(chatbot.images[recommended_products[3]['corpus_id']])
             with col5:
-                st.image(chatbot.images[indices[0][4]])
+                st.image(chatbot.images[recommended_products[4]['corpus_id']])
 
         else:
             st.header("Some error occurred in file upload")
@@ -115,4 +83,4 @@ def main():
 
 # Run the main app
 if __name__ == "__main__":
-    main()
+    main()