import pandas as pd
import gradio as gr
import matplotlib.pyplot as plt
import io
import base64
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
data = pd.read_csv('Blinkit Cart Prediction.csv')
tfidf_vectorizer = TfidfVectorizer(max_features=1000) # Adjust max_features as needed
tfidf_matrix = tfidf_vectorizer.fit_transform(data['Description'])
def recommend_products(user_choice, num_recommendations=10):
user_choice_vector = tfidf_vectorizer.transform([user_choice])
cosine_similarities = linear_kernel(user_choice_vector, tfidf_matrix)
similar_indices = cosine_similarities.argsort()[0][-num_recommendations - 1:-1][::-1]
recommended_products = data.iloc[similar_indices][['ProductID', 'ProductName']]
return recommended_products
input_component = gr.inputs.Textbox(label="Enter Your Choice")
output_component = gr.outputs.HTML(label="Recommended Products")
def recommend_interface(user_choice):
recommended_products = recommend_products(user_choice)
plt.figure(figsize=(10, 6))
plt.bar(recommended_products['ProductName'], range(len(recommended_products)), color='skyblue')
plt.xticks(rotation=45, ha="right")
plt.xlabel("Recommended Products")
plt.ylabel("Ranking")
plt.title("Top Recommended Products")
buffer = io.BytesIO()
plt.savefig(buffer, format="png")
graph_base64 = base64.b64encode(buffer.getvalue()).decode()
plt.close()
graph_html = f''
table_html = recommended_products.to_html(index=False)
result_html = f"