Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import pandas as pd
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 import matplotlib.pyplot as plt
+import spaces
 
 # Load model and tokenizer
 model_name = "ac0hik/Sentiment_Analysis_French"
@@ -11,48 +12,63 @@ model = AutoModelForSequenceClassification.from_pretrained(model_name)
 # Create sentiment analysis pipeline
 classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
 
+
+
 # Function to process a single text input
+@spaces.GPU
 def analyze_text(text):
     result = classifier(text)[0]
     return result
 
-# Function to process a CSV file
+# Function to process a CSV file and update results live
+@spaces.GPU
 def analyze_csv(file):
-    df = pd.read_csv(file)
+    df = pd.read_csv(file.name,nrows=2)
     texts = df['text'].tolist()
-    results = classifier(texts)
-    
-    sentiments = [result['label'] for result in results]
-    df['sentiment'] = sentiments
     
-    pos_count = sentiments.count('positive')
-    neg_count = sentiments.count('negative')
-    neu_count = sentiments.count('neutral')
+    results = []
+    pos_count = neg_count = neu_count = 0
     
-    # Create a pie chart
-    labels = 'Positive', 'Negative', 'Neutral'
-    sizes = [pos_count, neg_count, neu_count]
-    colors = ['#ff9999','#66b3ff','#99ff99']
-    fig, ax = plt.subplots()
-    ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
-    ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
-    
-    return df, fig
+    for text in texts:
+        result = classifier(text)[0]
+        results.append({'text': text, 'sentiment': result['label']})
+        
+        if result['label'] == 'positive':
+            pos_count += 1
+        elif result['label'] == 'negative':
+            neg_count += 1
+        else:
+            neu_count += 1
+        
+        # Create a pie chart
+        labels = 'Positive', 'Negative', 'Neutral'
+        sizes = [pos_count, neg_count, neu_count]
+        colors = ['#ff9999','#66b3ff','#99ff99']
+        fig, ax = plt.subplots()
+        wedges, texts, autotexts = ax.pie(sizes, labels=labels, colors=colors, autopct=lambda pct: "{:.1f}%\n({:d})".format(pct, int(pct/100.*sum(sizes))), startangle=90)
+        ax.axis('equal')
+        
+        # Update results live
+        yield pd.DataFrame(results), fig
 
 # Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("## Sentiment Analysis")
-    text_input = gr.Textbox(label="Enter Text")
-    text_output = gr.JSON(label="Sentiment Analysis Result")
-    text_button = gr.Button("Analyze Text")
-    
-    csv_input = gr.File(label="Upload CSV", file_types=['csv'])
-    csv_output = gr.Dataframe(label="Sentiment Analysis Results")
-    csv_chart = gr.Plot(label="Sentiment Distribution")
-    csv_button = gr.Button("Analyze CSV")
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("## Sentiment Analysis")
+            text_input = gr.Textbox(label="Enter Text")
+            text_output = gr.JSON(label="Sentiment Analysis Result")
+            text_button = gr.Button("Analyze Text")
+            
+            csv_input = gr.File(label="Upload CSV", file_types=['csv'])
+            csv_output = gr.Dataframe(label="Sentiment Analysis Results")
+            csv_button = gr.Button("Analyze CSV")
+        
+        with gr.Column():
+            csv_chart = gr.Plot(label="Sentiment Distribution")
     
     text_button.click(analyze_text, inputs=text_input, outputs=text_output)
     csv_button.click(analyze_csv, inputs=csv_input, outputs=[csv_output, csv_chart])
 
 # Launch the Gradio app
-demo.launch()
+demo.launch()