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import gradio as gr
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import SentenceTransformer
import pandas as pd
import re
encoded_df = pd.read_csv('encoded_df.csv').drop(columns=['Unnamed: 0'])
# Initialize the Sentence Transformer model
model = SentenceTransformer('all-MiniLM-L6-v2')
# Function to preprocess text (if required)
def preprocess_text(text):
# Your text preprocessing logic here (e.g., lowercasing, removing special characters)
text.lower() # Placeholder for text preprocessing
text = re.sub(r'[^a-zA-Z\s]', '', text)
return text
# Function to generate the graphs
def generate_graphs(new_story):
# Preprocess the new story
new_story = preprocess_text(new_story)
global model
# Encode the new story
new_story_vector = model.encode([new_story])[0]
# Calculate the similarity with all existing stories in the knowledge base
knowledge_base_vectors = encoded_df.iloc[:, :-1].values # Exclude 'likesCount'
print(f"New Story Vector Shape: {new_story_vector.shape}")
print(f"Knowledge Base Vector Shape: {knowledge_base_vectors.shape}")
similarities = cosine_similarity([new_story_vector], knowledge_base_vectors)
# Find the indices of the 5 most similar stories
top_5_indices = np.argsort(similarities[0])[::-1][:5] # Sort similarities and get top 5
# Retrieve the LikesCount for the top 5 most similar stories
likes_distribution = encoded_df.iloc[top_5_indices]['likesCount'].values
sorted_likes_distribution = sorted(likes_distribution, reverse=True)
# Create a bar graph for the distribution of the 5 most similar stories
plt.figure(figsize=(10, 6))
sns.barplot(x=[f"Story {i+1}" for i in range(5)], y=sorted_likes_distribution, palette="viridis")
plt.title("LikesCount Distribution for the 5 Most Similar Stories", fontsize=14)
plt.xlabel("Story Similarity (Most Similar to Least)", fontsize=12)
plt.ylabel("LikesCount", fontsize=12)
likes_dist_plot = plt.gcf()
# Plot the similarity distribution for the 5 most similar stories
plt.figure(figsize=(10, 6))
sns.kdeplot([new_story_vector], shade=False, label="New Story", color='blue')
for i in top_5_indices:
most_similar_vector = encoded_df.iloc[i, :-1].values
sns.kdeplot(most_similar_vector, shade=False, label=f"Most Similar Story: {top_5_indices.tolist().index(i)+1}", alpha=0.5)
plt.title("Similarity Distribution of New Story and Top 5 Similar Stories", fontsize=14)
plt.xlabel("Value", fontsize=12)
plt.ylabel("Density", fontsize=12)
plt.legend(title="Stories")
sim_dist_plot = plt.gcf()
return sim_dist_plot,likes_dist_plot
# Gradio interface
def gradio_interface(new_story):
# Generate and return both plots
likes_dist_plot, sim_dist_plot = generate_graphs(new_story)
return likes_dist_plot, sim_dist_plot
# Create the Gradio interface
iface = gr.Interface(
fn=gradio_interface,
inputs=gr.Textbox(label="Enter a story", lines=10, placeholder="Enter the story here..."),
outputs=[gr.Plot(), gr.Plot()],
title="Story Similarity and Likes Distribution",
description="Enter a new story to compare it with the knowledge base and get analytics on similarity and likes distribution of the most similar stories."
)
# Launch the interface
iface.launch(share=True)
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