Main 1

Beta-L/Influencer_dashboard_design/app.py

@@ -0,0 +1,257 @@
+from flask import Flask, render_template, request, redirect, url_for
+import plotly.express as px
+import pandas as pd
+import mysql.connector
+
+app = Flask(__name__)
+
+
+# MySQL database connection parameters
+db_params = {
+    "host": "sebi-hackathon.mysql.database.azure.com",
+    "user": "mysql",
+    "password": "Betateam-L",
+    "database": "fintech-influencers-claims"
+}
+
+# Create a MySQL database connection
+db_connection = mysql.connector.connect(**db_params)
+db_cursor = db_connection.cursor()
+
+# Fetch data from the table
+query = "SELECT * FROM fintech_influencers"
+db_cursor.execute(query)
+result = db_cursor.fetchall()
+
+# Define column names
+columns = [desc[0] for desc in db_cursor.description]
+
+# Create a DataFrame from the fetched data
+df = pd.DataFrame(result, columns=columns)
+
+# Load your dataset (replace 'your_dataset.csv' with your actual dataset file)
+#df = pd.read_excel('indummm.xlsx')
+
+df['Flagged Claims'] = ''
+
+# Dictionary to store previous feedback
+previous_feedback = {}
+previous_ratings = {}
+
+@app.route('/')
+def index():
+    filtered_data = df  # Initialize with all data
+
+    keywords = request.args.get('keywords', '').split(',')
+    filter_option = request.args.get('filter', 'both')  # Default to 'all' filter
+
+    # Apply filters based on the selected filter option
+    if filter_option == 'claims':
+        filtered_data = df[df['Claim'].str.contains('|'.join(keywords))]
+    elif filter_option == 'influencers':
+        filtered_data = df[df['Name'].str.contains('|'.join(keywords))]
+
+    elif filter_option == 'keywords':
+        filtered_data = df[df['Keywords'].str.contains('|'.join(keywords))]
+
+    elif filter_option == 'both':
+        filtered_data = df[
+            (df['Claim'].str.contains('|'.join(keywords))) |
+            (df['Name'].str.contains('|'.join(keywords))) | 
+            (df['Keywords'].str.contains('|'.join(keywords)))
+        ]
+
+    sorted_data = sorted(filtered_data.to_dict('records'), key=lambda x: x['Credibility_Score'], reverse=True)
+    cards_per_row = 3
+    rows = [sorted_data[i:i+cards_per_row] for i in range(0, len(sorted_data), cards_per_row)]
+    return render_template('index.html', rows=rows)
+
+
+
+@app.route('/profile/<string:influencer_name>')
+def profile(influencer_name):
+    filtered_data = df[df['Name'] == influencer_name]
+
+    if not filtered_data.empty:
+        influencer = filtered_data.iloc[0]
+        influencer_name = influencer['Name']
+        influencer_previous_feedback = previous_feedback.get(influencer_name, [])
+        influencer_previous_ratings = previous_ratings.get(influencer_name, [])
+
+        return render_template(
+            'profile.html',
+            influencer=influencer,
+            previous_feedback=influencer_previous_feedback,
+            previous_ratings=influencer_previous_ratings
+        )
+    else:
+        return "Influencer not found."
+
+
+@app.route('/visualize')
+def visualize():
+    return render_template('visualize.html')
+
+@app.route('/visualize/credibility_bar')
+def visualize_credential_bar():
+    fig = px.bar(df, x='Name', y='Credibility_Score', title='Credibility Score Distribution')
+    return fig.to_html()
+
+@app.route('/visualize/claim_pie')
+def visualize_claim_pie():
+    claim_counts = df['Claim'].value_counts()
+    fig = px.pie(claim_counts, names=claim_counts.index, values=claim_counts.values, title='Pie Chart for Claim Categories')
+    return fig.to_html()
+
+@app.route('/flag/<int:index>')
+def flag(index):
+    df.at[index, 'Flagged'] = True  # Assuming you have a 'Flagged' column in your DataFrame
+    return redirect(url_for('index'))
+
+@app.route('/feedback/<string:influencer_name>', methods=['GET', 'POST'])
+def feedback(influencer_name):
+    influencer = df[df['Name'] == influencer_name].iloc[0]
+
+    if request.method == 'POST':
+        feedback = request.form.get('feedback')
+
+        # Store previous feedback in the dictionary
+        if influencer_name in previous_feedback:
+            previous_feedback[influencer_name].append(feedback)
+        else:
+            previous_feedback[influencer_name] = [feedback]
+
+        # Update the DataFrame with the latest feedback
+        influencer_index = influencer.index[0]  # Get the index of the influencer
+        df.at[influencer_index, 'Feedback'] = feedback
+
+        return redirect(url_for('profile', influencer_name=influencer_name))
+
+    influencer_previous_feedback = previous_feedback.get(influencer_name, [])
+
+    return render_template('feedback_form.html', influencer=influencer, previous_feedback=influencer_previous_feedback)
+
+
+
+@app.route('/flag_claim/<string:influencer_name>/<string:claim>', methods=['POST'])
+def flag_claim(influencer_name, claim):
+    influencer = df[df['Name'] == influencer_name].iloc[0]
+
+    if not isinstance(influencer['Flagged Claims'], list):
+        influencer['Flagged Claims'] = []
+
+    if claim not in influencer['Flagged Claims']:
+        influencer['Flagged Claims'].append(claim)
+
+    # You can perform other flagging actions here if needed
+    # For example, you might want to update a database or a log
+
+    return redirect(url_for('profile', influencer_name=influencer_name))
+
+
+
+
+
+
+
+
+@app.route('/rate/<string:influencer_name>', methods=['GET', 'POST'])
+def rate(influencer_name):
+    influencer = df[df['Name'] == influencer_name].iloc[0]
+
+    if request.method == 'POST':
+        rating = int(request.form.get('rating'))
+
+        # Store previous ratings in the dictionary
+        if influencer_name in previous_ratings:
+            previous_ratings[influencer_name].append(rating)
+        else:
+            previous_ratings[influencer_name] = [rating]
+
+        # Update the DataFrame with the latest rating
+        influencer_index = influencer.index[0]  # Get the index of the influencer
+        df.at[influencer_index, 'Rating'] = rating
+        return redirect(url_for('profile', influencer_name=influencer_name))
+
+    influencer_previous_ratings = previous_ratings.get(influencer_name, [])
+
+    return render_template('rating_form.html', influencer=influencer, previous_ratings=influencer_previous_ratings)
+
+
+@app.route('/compare', methods=['GET', 'POST'])
+def compare():
+    influencers_list = df['Name'].tolist()
+
+    if request.method == 'POST':
+        selected_influencers = request.form.getlist('influencers')
+
+        if len(selected_influencers) <= 3:
+            selected_data = df[df['Name'].isin(selected_influencers)]
+
+            # Create the Credibility Score graph
+            credibility_graph = px.bar(selected_data, x='Name', y='Credibility_Score', title='Comparison of Credibility Scores')
+
+            # Create the Rating graph
+            rating_graph = px.bar(selected_data, x='Name', y='Rating', title='Comparison of Ratings')
+
+            # Convert graphs to HTML
+            credibility_graph = credibility_graph.to_html(full_html=False, include_plotlyjs='cdn')
+            rating_graph = rating_graph.to_html(full_html=False, include_plotlyjs='cdn')
+
+            return render_template(
+                'compare.html',
+                influencers=influencers_list,
+                credibility_graph=credibility_graph,
+                rating_graph=rating_graph
+            )
+
+        else:
+            return "Please select up to 3 influencers for comparison."
+
+    return render_template('compare.html', influencers=influencers_list, credibility_graph='', rating_graph='')
+
+"""def compare():
+    df = pd.DataFrame(result, columns=columns)
+    influencers_list = df['Name'].tolist()
+
+    if request.method == 'POST':
+        selected_influencers = request.form.getlist('influencers')
+        print(selected_influencers)
+
+        if len(selected_influencers) <= 3:
+            selected_data = df[df['Name'].isin(selected_influencers)]
+            print(selected_data)
+            # Create the Credibility Score graph
+            credibility_graph = px.bar(selected_data, x='Name', y='Credibility_Score', title='Comparison of Credibility Scores')
+            credibility_graph = credibility_graph.to_html(full_html=False, include_plotlyjs='cdn')
+
+            # Create the Rating graph
+            rating_graph = px.bar(selected_data, x='Name', y='Rating', title='Comparison of Ratings')
+            rating_graph = rating_graph.to_html(full_html=False, include_plotlyjs='cdn')
+
+            return render_template(
+                'compare.html',
+                influencers=influencers_list,
+                credibility_graph=credibility_graph,
+                rating_graph=rating_graph
+            )
+        else:
+            return "Please select up to 3 influencers for comparison."
+
+    return render_template('compare.html', influencers=influencers_list, credibility_graph='', rating_graph='') """
+
+
+
+@app.route('/visualize/keyword_bar')
+def visualize_keyword_bar():
+    keyword_freq = df['Keywords'].str.split(', ').explode().value_counts()
+    fig = px.bar(x=keyword_freq.index, y=keyword_freq.values, title='Keyword Frequency Bar Chart')
+    return fig.to_html()
+
+@app.route('/visualize/scatter_credential_keywords')
+def visualize_scatter_credential_keywords():
+    fig = px.scatter(df, x='Credibility_Score', y='Keywords', title='Scatter Plot: Credibility vs. Keywords')
+    return fig.to_html()
+
+if __name__ == '__main__':
+    app.run(debug=True)

Beta-L/Influencer_dashboard_design/compare.html

@@ -0,0 +1,37 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Compare Influencers</title>
+    <link rel="stylesheet" type="text/css" href="{{ url_for('static', filename='styles.css') }}">
+</head>
+<body>
+    <div class="dashboard-header">
+        <h1>Compare Influencers</h1>
+    </div>
+
+    <div class="dashboard-content">
+        <h2>Select up to 3 influencers for comparison:</h2>
+        <form action="{{ url_for('compare') }}" method="POST">
+            <select name="influencers" multiple size="3">
+                {% for influencer in influencers %}
+                <option value="{{ influencer }}">{{ influencer }}</option>
+                {% endfor %}
+            </select>
+            <button type="submit">Compare</button>
+        </form>
+    </div>
+
+    {% if credibility_graph %}
+    <div class="graphs">
+        <div class="graph">
+            <h3>Comparison of Credibility Scores</h3>
+            {{ credibility_graph|safe }}
+        </div>
+        <div class="graph">
+            <h3>Comparison of Ratings</h3>
+            {{ rating_graph|safe }}
+        </div>
+    </div>
+    {% endif %}
+</body>
+</html>

Beta-L/Influencer_dashboard_design/feedback_form.html

@@ -0,0 +1,24 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Feedback</title>
+</head>
+<body>
+    <h1>Feedback for Influencer: {{ influencer.Name }}</h1>
+    <form method="POST">
+        <label for="feedback">Feedback:</label><br>
+        <textarea id="feedback" name="feedback" rows="4" cols="50"></textarea><br>
+        <button type="submit">Submit Feedback</button>
+    </form>
+
+    <div>
+        <h2>Previous Feedback:</h2>
+        <ul>
+            {% for feedback_entry in previous_feedback %}
+            <li>{{ feedback_entry }}</li>
+            {% endfor %}
+        </ul>
+    </div>
+
+</body>
+</html>