Update app.py

app.py

@@ -22,34 +22,47 @@ def analyze_text(text):
 
 # Function to process a CSV file and update results live
 @spaces.GPU
+
+# Function to process a single text input
+def analyze_text(text):
+    result = classifier(text)[0]
+    return result
+
+# Function to process a CSV file and update results live
 def analyze_csv(file):
-    df = pd.read_csv(file.name, nrows=100)
+    df = pd.read_csv(file.name)
     texts = df['text'].tolist()
-    
+
     results = []
     pos_count = neg_count = neu_count = 0
-    
+
     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.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
         ax.axis('equal')
-        
+
         # Update results live
-        yield pd.DataFrame(results), fig
+        yield pd.DataFrame(results), fig, pos_count, neg_count, neu_count
+
+# Function to save the DataFrame to a CSV file
+def save_csv(df):
+    file_path = "/mnt/data/sentiment_analysis_results.csv"
+    df.to_csv(file_path, index=False)
+    return file_path
 
 # Gradio interface
 with gr.Blocks() as demo:
@@ -59,16 +72,21 @@ with gr.Blocks() as demo:
             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():
+            gr.Markdown("## csv Result")
+            pos_count_output = gr.Number(label="Positive Count", value=0)
+            neg_count_output = gr.Number(label="Negative Count", value=0)
+            neu_count_output = gr.Number(label="Neutral Count", value=0)
             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])
+    csv_button.click(analyze_csv, inputs=csv_input, outputs=[csv_output, csv_chart, pos_count_output, neg_count_output, neu_count_output])
+    csv_button.click(fn=save_csv, inputs=csv_output, outputs=csv_download)
 
 # Launch the Gradio app
 demo.launch()