Upload 13 files

api/Overall_recommendation_model.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fda0bfd83ffce414d57735aac204a5d0140aeba667e17d6a646fcdcfdac0c72d
+size 19652

api/api.py

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+from flask import Flask, jsonify, request
+import pickle
+import pandas as pd
+import numpy as np
+
+app = Flask(__name__)
+
+# Load pickled data
+# Open the file using 'with' statement
+with open('popular1.pkl', 'rb') as f:
+    popular_df = pd.read_pickle(f)
+with open('pt1.pkl', 'rb') as fi:
+    pt = pd.read_pickle(fi)
+with open('banquet.pkl', 'rb') as fil:
+    banquets = pd.read_pickle(fil)
+with open('similarity_scores1.pkl', 'rb') as file:
+    similarity_scores = pd.read_pickle(file)
+# Define a route to get recommendations
+@app.route('/recommend/<string:n>',methods=['GET'])
+def recommend(n):
+    # Get user input from request
+   
+
+    # Perform recommendation logic
+    if n not in pt.index:
+       return "Banquet not found in the index"
+
+    index = np.where(pt.index == n)[0][0]
+    similar_items = sorted(enumerate(similarity_scores[index]), key=lambda x: x[1], reverse=True)[1:5]
+
+    data = []
+    for i in similar_items:
+        item = []
+        temp_df = banquets[banquets['Hall-Name'] == pt.index[i[0]]]
+        item.extend(temp_df.drop_duplicates('Hall-Name')['Hall-Name'].values)
+        item.extend(temp_df.drop_duplicates('Hall-Name')['Address'].values)
+        item.extend(temp_df.drop_duplicates('Hall-Name')['Contact'].values)
+        item.extend(temp_df.drop_duplicates('Hall-Name')['Rating'].values)
+        item.extend(temp_df.drop_duplicates('Hall-Name')['Jn12ke src'].values)
+        data.append(item)
+
+    return jsonify({'recommendations': data})
+
+if __name__ == "__main__":
+    app.run(debug=True)

api/banquet.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:765c5e34a0cb69c1532ee84cc46125b8c4396d6ac8d50f1bd582809299b96698
+size 33005

api/banquets.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:98ce9fbae514a1f09efe71a7a98cb6f8737b2da886bfbce108614ec3e7ab5aa6
+size 6359

api/main.py

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+from flask import Flask,jsonify,render_template,request
+import pickle
+import pandas as pd
+import pandas as pd
+import numpy as np
+
+# Open the file using 'with' statement
+with open('popular1.pkl', 'rb') as f:
+    popular_df = pd.read_pickle(f)
+with open('pt1.pkl', 'rb') as fi:
+    pt = pd.read_pickle(fi)
+with open('banquet.pkl', 'rb') as fil:
+    banquets = pd.read_pickle(fil)
+with open('similarity_scores1.pkl', 'rb') as file:
+    similarity_scores = pd.read_pickle(file)
+
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return render_template(
+        'index.html',
+        banquet_img=list(popular_df['Jn12ke src'].values),
+        banquet_name=list(popular_df['Hall-Name'].values),
+        banquet_reviews=list(popular_df['num_ratings'].values),
+        banquet_rating=list(popular_df['Rating_x'].values),
+        
+
+    )
+@app.route('/recommend')
+def recommend_ui():
+    return render_template(
+
+        'Recommend.html'
+    )
+
+@app.route('/banquet',methods=['post'])
+def recommend(): 
+    user_input=request.form.get('user-input')
+
+    if user_input not in pt.index:
+        return "Banquet not found in the index"
+
+    index = np.where(pt.index ==user_input)[0][0]
+    similar_items = sorted(enumerate(similarity_scores[index]), key=lambda x: x[1], reverse=True)[1:5]
+
+    data = []
+    for i in similar_items:
+        item = []
+        temp_df = banquets[banquets['Hall-Name'] == pt.index[i[0]]]
+        item.extend(temp_df.drop_duplicates('Hall-Name')['Hall-Name'].values)
+        item.extend(temp_df.drop_duplicates('Hall-Name')['Address'].values)
+        item.extend(temp_df.drop_duplicates('Hall-Name')['Contact'].values)
+        item.extend(temp_df.drop_duplicates('Hall-Name')['Rating'].values)
+        item.extend(temp_df.drop_duplicates('Hall-Name')['Jn12ke src'].values)
+
+        data.append(item)
+
+    print(data)
+ 
+    return jsonify({'recommendations': data})
+    
+
+if __name__=="__main__":
+    app.run(debug=True)
+  
+ 

api/popular.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5ba59d9e149e95ade83f87d6c0e0b721710ade00c42fce47bb20bc9c32df6c66
+size 2201

api/popular1.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:40eb61872ad5ace6d5744ae65f97b43c024b3ec521f38f4ca1fb18717b5538c2
+size 3201

api/pt.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5cd60305a7bdf81c07d84c29c3ad815231b8e1f3d723611f7414ea4fbcfbb11f
+size 26000

api/pt1.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5cd60305a7bdf81c07d84c29c3ad815231b8e1f3d723611f7414ea4fbcfbb11f
+size 26000

api/similarity_scores.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5b37be1b672da2d9031d82a2cb8ced97a0065ff3a62f1be91fc7b366a9d6fb38
+size 48824

api/similarity_scores1.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5b37be1b672da2d9031d82a2cb8ced97a0065ff3a62f1be91fc7b366a9d6fb38
+size 48824

api/templates/Recommend.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Banquet Recommender</title>
+    <!-- Latest compiled and minified CSS -->
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
+</head>
+<body style="background-color: black;">
+
+<nav class="navbar">
+    <p class="navbar-brand" style="color: aliceblue;">Banquet Recommender</p>
+    <ul>
+        <li><a href="/">Home</a></li>
+        <li><a href="/recommend">Recommend</a></li>
+    </ul>
+</nav>
+
+<div class="container">
+    <div class="row">
+        <div class="col-md-12">
+            <h1 class="text-white" style="font-size: 50px;">Recommend Banquet</h1>
+        <form action="/banquet" method="post">
+            <input name="user-input" type="text" class="form-control form-control"><br>
+        <input  type="submit" class="btn btn-lg btn-warning">
+        </form>
+        </div>
+{% if data %}
+{% for i in data %}
+<div class="col-md-3" style="margin-top: 50px;">
+    <div class="card">
+        <div class="card-body">
+            <img class="card-img-top" src="{{i[4]}}", style="max-width: 100%; max-height: 200px;">>
+           <h3 style="color: #f9f8f8 "> Name: {{i[0]}}</h2>
+           <h4 style="color: #f9f8f8" > Address: {{i[1]}}</h4>
+           <h4 style="color: #f9f8f8 ">Phone No:{{i[2]}}</h4>
+           <h4 style="color: #f9f8f8" > Rating: {{i[3]}}</h4>
+        </div>
+    </div>
+</div>
+{% endfor %}
+{% endif %}
+
+    </div>
+</div>
+
+</body>
+</html>

api/templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Banquet Recommender</title>
+    <!-- Latest compiled and minified CSS -->
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
+</head>
+<body style="background-color: black;">
+
+<nav class="navbar">
+    <p class="navbar-brand" style="color: aliceblue;">Banquet Recommender</p>
+    <ul class="nav navbar-nav">
+        <li><a href="/">Home</a></li>
+        <li><a href="/recommend">Recommend</a></li>
+    </ul>
+</nav>
+
+<div class="container">
+    <div class="row">
+        <div class="col-md-12">
+            <h1 class="text-white" style="font-size: 50px;">Top 50 Halls</h1>
+        </div>
+
+        {% for i in range(banquet_name|length) %}
+        <div class="col-md-3" style="margin-top: 50px;">
+            <div class="card">
+                <div class="card-body">
+                    <img class="card-img-top" src="{{ banquet_img[i] }}", style="max-width: 100%; max-height: 200px;">
+                    <h1>{{ banquet_name[i] }}</h1>
+                    <h4>Reviews: {{ banquet_reviews[i] }}</h4>
+                    <h4>Rating: {{ banquet_rating[i] }}</h4>
+                </div>
+            </div>
+        </div>
+        {% endfor %}
+    </div>
+</div>
+
+</body>
+</html>