Create app.py

app.py

@@ -0,0 +1,72 @@
+import gradio as gr
+import numpy as np
+import os,requests
+import matplotlib.pyplot as plt
+
+API_URL = "https://api-inference.huggingface.co/models/ahmedrachid/FinancialBERT-Sentiment-Analysis"
+API_TOKEN = os.environ['API_TOKEN']
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+
+def get_chart(score, color):
+    # Create figure and axis
+    fig, ax = plt.subplots(figsize=(3, 3), subplot_kw=dict(aspect="equal"))
+    
+    # Create the pie chart, which looks like a donut
+    wedges, texts = ax.pie([score, 100-score], startangle=90, counterclock=False, colors=[color, '#dddddd'])
+    
+    # Draw a white circle in the center
+    centre_circle = plt.Circle((0,0),0.85,fc='white')
+    fig.gca().add_artist(centre_circle)
+    
+    # Equal aspect ratio ensures that pie is drawn as a circle.
+    ax.axis('equal')  
+    
+    # Add text in the center
+    plt.text(0, 0, f'{score}%', horizontalalignment='center', verticalalignment='center', fontsize=18, color=color)
+    return fig
+
+def query(Statement):
+    response = requests.post(API_URL, headers=headers, json=Statement)
+
+    print(response.json())
+    response_json = response.json()[0]
+    positive_score = 0
+    neutral_score = 0
+    negative_score = 0
+    
+    for entry in response_json:
+        if entry['label'] == 'positive':
+            positive_score = round(entry['score']*100,2)
+        elif entry['label'] == 'neutral':
+            neutral_score = round(entry['score']*100,2)
+        elif entry['label'] == 'negative':
+            negative_score = round(entry['score']*100,2)
+    
+    
+    labels = ['Negative', 'Neutral', 'Positive']
+    values = [negative_score, neutral_score, positive_score ]
+
+    max_score_dict = max(response_json, key=lambda x: x['score'])
+
+    max_label = max_score_dict['label'].capitalize()
+
+    positive_plot = get_chart(positive_score, '#32CD32')    
+    negative_plot = get_chart(negative_score, '#CE2029')
+    neutral_plot = get_chart(neutral_score, '#ADD8E6')
+    return f"Overall sentiment is {max_label}", positive_plot, neutral_plot, negative_plot 
+
+with gr.Blocks() as financial_sentiment_interface:
+    gr.Markdown("# Financial Sentiment Analysis")
+    with gr.Row():
+        with gr.Column():
+            financial_content = gr.Textbox(lines=2, placeholder="Your Financial Content Here...", label="Financial News")
+            submit_btn = gr.Button(value="Submit")
+        sentiment = gr.Textbox(label="Sentiment")
+    with gr.Row():
+        positive_plot = gr.Plot(label="Positive Sentiment")
+        neutral_plot = gr.Plot(label="Neutral Sentiment")
+        negative_plot = gr.Plot(label="Negative Sentiment")   
+
+    submit_btn.click(query, inputs=financial_content, outputs=[sentiment,positive_plot,neutral_plot,negative_plot], api_name="sentiment-analysis")
+
+financial_sentiment_interface.launch()