Update main.py

main.py

@@ -1,5 +1,4 @@
 import streamlit as st
-import altair as alt
 import os
 from PIL import Image
 import numpy as np
@@ -7,74 +6,120 @@ import pickle
 import tensorflow
 from tensorflow.keras.preprocessing import image
 from tensorflow.keras.layers import GlobalMaxPooling2D
-from tensorflow.keras.applications.resnet50 import ResNet50,preprocess_input
+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
 
-feature_list = np.array(pickle.load(open('embeddings.pkl','rb')))
-filenames = pickle.load(open('filenames.pkl','rb'))
-
-model = ResNet50(weights='imagenet',include_top=False,input_shape=(224,224,3))
-model.trainable = False
-
-model = tensorflow.keras.Sequential([
-    model,
-    GlobalMaxPooling2D()
-])
-
-st.title('Fashion Recommender System')
-
-def save_uploaded_file(uploaded_file):
-    try:
-        with open(os.path.join('uploads',uploaded_file.name),'wb') as f:
-            f.write(uploaded_file.getbuffer())
-        return 1
-    except:
-        return 0
-
-def feature_extraction(img_path,model):
+# 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
 
-def recommend(features,feature_list):
+# 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
 
-# steps
-# file upload -> save
-uploaded_file = st.file_uploader("Choose an image")
-if uploaded_file is not None:
-    if save_uploaded_file(uploaded_file):
-        # display the file
-        display_image = Image.open(uploaded_file)
-        st.image(display_image)
-        # feature extract
-        features = feature_extraction(os.path.join("uploads",uploaded_file.name),model)
-        #st.text(features)
-        # recommendention
-        indices = recommend(features,feature_list)
-        # show
-        col1,col2,col3,col4,col5 = st.beta_columns(5)
+# Function to save uploaded file
+def save_uploaded_file(uploaded_file):
+    try:
+        # Ensure the uploads directory exists
+        if not os.path.exists('uploads'):
+            os.makedirs('uploads')
+        
+        file_path = os.path.join('uploads', uploaded_file.name)
+        with open(file_path, 'wb') as f:
+            f.write(uploaded_file.getbuffer())
+        st.success(f"File saved to {file_path}")
+        return True
+    except Exception as e:
+        st.error(f"Error saving file: {e}")
+        return False
+
+# Function to show dashboard content
+def show_dashboard():
+    st.header("Fashion Recommender System")
+    chatbot = Chatbot()
+    # Load ResNet model for image feature extraction
+    model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+    model.trainable = False
+    model = tensorflow.keras.Sequential([
+        model,
+        GlobalMaxPooling2D()
+    ])
+
+    try:
+        feature_list = np.array(pickle.load(open('embeddings.pkl', 'rb')))
+        filenames = pickle.load(open('filenames.pkl', 'rb'))
+    except Exception as e:
+        st.error(f"Error loading pickle files: {e}")
+        return
+
+    # File upload section
+    uploaded_file = st.file_uploader("Choose an image")
+    if uploaded_file is not None:
+        if save_uploaded_file(uploaded_file):
+            # Display the uploaded image
+            display_image = Image.open(uploaded_file)
+            st.image(display_image)
+
+            # Feature extraction
+            features = feature_extraction(os.path.join("uploads", uploaded_file.name), model)
+
+            # Recommendation
+            indices = recommend(features, feature_list)
+
+            # Display recommended products
+            col1, col2, col3, col4, col5 = st.columns(5)
+            columns = [col1, col2, col3, col4, col5]
+
+            for col, idx in zip(columns, indices[0]):
+                file_path = filenames[idx]
+                try:
+                    if os.path.exists(file_path):
+                        with col:
+                            st.image(file_path)
+                    else:
+                        st.error(f"File does not exist: {file_path}")
+                except Exception as e:
+                    st.error(f"Error opening file {file_path}: {e}")
+        else:
+            st.error("Some error occurred in file upload")
+
+    # Chatbot section
+    user_question = st.text_input("Ask a question:")
+    if user_question:
+        bot_response, recommended_products = chatbot.generate_response(user_question)
+        st.write("Chatbot:", bot_response)
+
+        # Display recommended products
+        for result in recommended_products:
+            pid = result['corpus_id']
+            product_info = chatbot.product_data[pid]
+            st.write("Product Name:", product_info['productDisplayName'])
+            st.write("Category:", product_info['masterCategory'])
+            st.write("Article Type:", product_info['articleType'])
+            st.write("Usage:", product_info['usage'])
+            st.write("Season:", product_info['season'])
+            st.write("Gender:", product_info['gender'])
+            st.image(chatbot.images[pid])
+
+# Main Streamlit app
+def main():
+    # Give title to the app
+    st.title("Fashion Recommender System")
+
+    # Show dashboard content directly
+    show_dashboard()
 
-        with col1:
-            st.image(filenames[indices[0][0]])
-        with col2:
-            st.image(filenames[indices[0][1]])
-        with col3:
-            st.image(filenames[indices[0][2]])
-        with col4:
-            st.image(filenames[indices[0][3]])
-        with col5:
-            st.image(filenames[indices[0][4]])
-    else:
-        st.header("Some error occured in file upload")
+# Run the main app
+if __name__ == "__main__":
+    main()