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import streamlit as st
import faiss
import numpy as np
import pandas as pd
import cohere
from datetime import datetime
import os
from google_play_scraper import app
from dotenv import load_dotenv
load_dotenv()
# Initialize Cohere client
cohere_api_key = os.getenv('api') # Replace with your Cohere API key
co = cohere.Client(cohere_api_key)
# Load the FAISS index from a file
index = faiss.read_index("faiss_index.bin")
# Load the DataFrame
csv_file_path = r'C:\Users\Dell\3D Objects\NLP\gg\nowgg_embeddings.csv' # Replace with the path to your CSV file
test3 = pd.read_csv(csv_file_path)
# Function to get embedding for a query using Cohere
def get_query_embedding(query):
response = co.embed(texts=[query])
return np.array(response.embeddings[0][:250]).astype('float32')
# Function to perform similarity search
def search_similar(query, k=5):
query_embedding = get_query_embedding(query).reshape(1, -1)
distances, indices = index.search(query_embedding, k)
results = []
for idx in indices[0]:
product_id = test3.iloc[idx]['product_id']
# Fetch app details from Google Play Store
app_details = app(product_id)
result = {
'title': test3.iloc[idx]['title'],
'product_id': test3.iloc[idx]['product_id'],
'description': test3.iloc[idx]['final_description'],
'link': test3.iloc[idx]['link'],
#'icon':app_details["icon"]
'video':app_details["video"]
}
results.append(result)
return results
# Function to save feedback
def save_feedback(query, feedback):
feedback_data = {
'timestamp': [datetime.now().strftime("%Y-%m-%d %H:%M:%S")],
'query': [query],
'feedback': [feedback]
}
feedback_df = pd.DataFrame(feedback_data)
feedback_df.to_csv('feedback.csv', mode='a', header=False, index=False)
path=r"C:\Users\Dell\3D Objects\NLP\game.jpg"
# HTML & CSS for the app
st.markdown("""
<style>
body {
font-family: 'Arial', sans-serif;
}
.title {
font-size: 2.5em;
color: #4CAF50;
text-align: center;
margin-bottom: 20px;
}
.query-input {
text-align: center;
margin-bottom: 20px;
}
.result-card {
background-color: #f9f9f9;
border-radius: 10px;
padding: 20px;
margin-bottom: 20px;
box-shadow: 0 2px 5px rgba(0,0,0,0.1);
}
.result-title {
font-size: 1.5em;
color: #333;
margin-bottom: 10px;
}
.result-productid {
font-size: 1.0em;
color: #333;
margin-bottom: 5px;
}
.result-link {
color: #0066cc;
text-decoration: none;
}
.result-link:hover {
text-decoration: underline;
}
.feedback-section {
margin-top: 40px;
text-align: center;
}
.feedback-textarea {
width: 100%;
padding: 10px;
border-radius: 5px;
border: 1px solid #ccc;
margin-bottom: 20px;
}
.submit-btn {
background-color: #4CAF50;
color: white;
padding: 10px 20px;
border: none;
border-radius: 5px;
cursor: pointer;
}
.submit-btn:hover {
background-color: #45a049;
}
</style>
""", unsafe_allow_html=True)
# Streamlit app
st.markdown('<div class="title">Game Recommendation System</div>', unsafe_allow_html=True)
query = st.text_input("Enter your query:", key="query_input", placeholder="Type something...")
if query:
top_k_results = search_similar(query)
st.write('<div class="query-input">Top recommendations:</div>', unsafe_allow_html=True)
for result in top_k_results:
#img=result["product_id"]
st.markdown(f"""
<div class="result-card">
<div class="result-title">{result['title']}</div>
<div><a class="result-link" href="{result['link']}">Link</a></div>
</div>
""", unsafe_allow_html=True)
st.video(result['video'])
#video_url=result['video'] # Display the image
st.markdown('<div class="feedback-section">################ Feedback #####################</div>', unsafe_allow_html=True)
feedback = st.text_area("Please provide your feedback here:", key="feedback_textarea", height=100)
if st.button("Submit Feedback", key="submit_feedback"):
save_feedback(query, feedback)
st.write("Thank you for your feedback!")
# Run the app with:
# streamlit run hello.py
#<div class="result-description">**Description**: {result['description']}</div>
#<div class="result-productid">**Product-id**{result['product_id']}</div>
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