Update app.py

app.py

@@ -1,135 +1,32 @@
-
-
-import os
-import pickle
-import torch
-import matplotlib.pyplot as plt
-from langchain_community.document_loaders import TextLoader
-from datasets import load_dataset
-from sentence_transformers import SentenceTransformer, util
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-from transformers import BertModel, BertTokenizer
-from langchain_core.prompts import PromptTemplate
 import streamlit as st
+import os
 from PIL import Image
 import numpy as np
-import tensorflow as tf
+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 sklearn.neighbors import NearestNeighbors
 from numpy.linalg import norm
-
-os.environ['HUGGINGFACEHUB_API_TOKEN'] = "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"
-
-class Chatbot:
-    def __init__(self):
-        self.load_data()
-        self.load_models()
-        self.load_embeddings()
-        self.load_template()
-
-    def load_data(self):
-        self.data = load_dataset("ashraq/fashion-product-images-small", split="train")
-        self.images = self.data["image"]
-        self.product_frame = self.data.remove_columns("image").to_pandas()
-        self.product_data = self.product_frame.reset_index(drop=True).to_dict(orient='index')
-
-    def load_template(self):
-        self.template = """
-        You are a fashion shopping assistant that wants to convert customers based on the information given.
-        Describe season and usage given in the context in your interaction with the customer.
-        Use a bullet list when describing each product.
-        If user ask general question then answer them accordingly, the question may be like when the store will open, where is your store located.
-        Context: {context}
-        User question: {question}
-        Your response: {response}
-        """
-        self.prompt = PromptTemplate.from_template(self.template)
-
-    def load_models(self):
-        self.model = SentenceTransformer('clip-ViT-B-32')
-        self.bert_model_name = "bert-base-uncased"
-        self.bert_model = BertModel.from_pretrained(self.bert_model_name)
-        self.bert_tokenizer = BertTokenizer.from_pretrained(self.bert_model_name)
-        self.gpt2_model_name = "gpt2"
-        self.gpt2_model = GPT2LMHeadModel.from_pretrained(self.gpt2_model_name)
-        self.gpt2_tokenizer = GPT2Tokenizer.from_pretrained(self.gpt2_model_name)
-
-    def load_embeddings(self):
-        if os.path.exists("embeddings_cache.pkl"):
-            with open("embeddings_cache.pkl", "rb") as f:
-                embeddings_cache = pickle.load(f)
-            self.image_embeddings = embeddings_cache["image_embeddings"]
-            self.text_embeddings = embeddings_cache["text_embeddings"]
-        else:
-            self.image_embeddings = self.model.encode([image for image in self.images])
-            self.text_embeddings = self.model.encode(self.product_frame['productDisplayName'])
-            embeddings_cache = {"image_embeddings": self.image_embeddings, "text_embeddings": self.text_embeddings}
-            with open("embeddings_cache.pkl", "wb") as f:
-                pickle.dump(embeddings_cache, f)
-
-    def create_docs(self, results):
-        docs = []
-        for result in results:
-            pid = result['corpus_id']
-            score = result['score']
-            result_string = ''
-            result_string += "Product Name:" + self.product_data[pid]['productDisplayName'] + \
-                             ';' + "Category:" + self.product_data[pid]['masterCategory'] + \
-                             ';' + "Article Type:" + self.product_data[pid]['articleType'] + \
-                             ';' + "Usage:" + self.product_data[pid]['usage'] + \
-                             ';' + "Season:" + self.product_data[pid]['season'] + \
-                             ';' + "Gender:" + self.product_data[pid]['gender']
-            # Assuming text is imported from somewhere else
-            doc = TextLoader(page_content=result_string)
-            doc.metadata['pid'] = str(pid)
-            doc.metadata['score'] = score
-            docs.append(doc)
-        return docs
-
-    def get_results(self, query, embeddings, top_k=10):
-        query_embedding = self.model.encode([query])
-        cos_scores = util.pytorch_cos_sim(query_embedding, embeddings)[0]
-        top_results = torch.topk(cos_scores, k=top_k)
-        indices = top_results.indices.tolist()
-        scores = top_results.values.tolist()
-        results = [{'corpus_id': idx, 'score': score} for idx, score in zip(indices, scores)]
-        return results
-
-    def display_text_and_images(self, results_text):
-        for result in results_text:
-            pid = result['corpus_id']
-            product_info = self.product_data[pid]
-            print("Product Name:", product_info['productDisplayName'])
-            print("Category:", product_info['masterCategory'])
-            print("Article Type:", product_info['articleType'])
-            print("Usage:", product_info['usage'])
-            print("Season:", product_info['season'])
-            print("Gender:", product_info['gender'])
-            print("Score:", result['score'])
-            plt.imshow(self.images[pid])
-            plt.axis('off')
-            plt.show()
-
-    @staticmethod
-    def cos_sim(a, b):
-        a_norm = torch.nn.functional.normalize(a, p=2, dim=1)
-        b_norm = torch.nn.functional.normalize(b, p=2, dim=1)
-        return torch.mm(a_norm.T, b_norm)  # Reshape a_norm to (768, 1)
-
-    def generate_response(self, query):
-        # Process the user query and generate a response
-        results_text = self.get_results(query, self.text_embeddings)
-
-        # Generate chatbot response
-        chatbot_response = "This is a placeholder response from the chatbot."  # Placeholder, replace with actual response
-
-        # Display recommended products
-        self.display_text_and_images(results_text)
-
-        # Return both chatbot response and recommended products
-        return chatbot_response, results_text
+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):
@@ -147,13 +44,13 @@ def show_dashboard():
     # 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 = tensorflow.keras.Sequential([
         model,
         GlobalMaxPooling2D()
     ])
 
     feature_list = np.array(pickle.load(open('embeddings.pkl', 'rb')))
-    # filenames = pickle.load(open('filenames.pkl', 'rb'))  # No longer needed
+    filenames = pickle.load(open('filenames.pkl', 'rb'))
 
     # File upload section
     uploaded_file = st.file_uploader("Choose an image")
@@ -169,19 +66,18 @@ def show_dashboard():
             # Recommendation
             indices = recommend(features, feature_list)
 
-            # Display recommended products using the dataset images
-            st.write("Recommended Products:")
-            cols = st.columns(5)
-            for i, idx in enumerate(indices[0][:5]):
-                with cols[i]:
-                    st.image(chatbot.images[idx])
-                    product_info = chatbot.product_data[idx]
-                    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'])
+            # Display recommended products
+            col1, col2, col3, col4, col5 = st.columns(5)
+            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 occurred in file upload")