Update app.py

app.py

@@ -1,164 +1,164 @@
-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>
+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('CO_API_KEY')  # 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>