Create app.py

app.py

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+import torch
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+#analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+analyzer = pipeline("text-classification", model="lxyuan/distilbert-base-multilingual-cased-sentiments-student")
+
+def sentiment_analyzer(review):
+    sentiment = analyzer(review)
+    return sentiment[0]['label']
+
+def sentiment_bar_chart(df):
+    sentiment_counts = df['Sentiment'].value_counts()
+
+    # Create a bar chart
+    fig, ax = plt.subplots()
+    sentiment_counts.plot(kind='pie', ax=ax, autopct='%1.1f%%', color=['green', 'red'])
+    ax.set_title('Reviews')
+    ax.set_xlabel('Stimmung')
+    ax.set_ylabel('Anzahl')
+    # ax.set_xticklabels(['Positive', 'Negative'], rotation=0)
+
+    # Return the figure object
+    return fig
+
+
+def read_reviews_and_analyze_sentiment(file_object):
+    # Load the Excel file into a DataFrame
+    df = pd.read_excel(file_object)
+    print(df.columns)
+    # Check if 'Review' column is in the DataFrame
+    #for col in df.columns:
+        #print(f"col={col}")
+    if 'Reviews' not in df.columns:
+        raise ValueError("Die Excel-Datei muss eine Spalte 'Reviews' enthalten.")
+
+    # Apply the get_sentiment function to each review in the DataFrame
+    df['Sentiment'] = df['Reviews'].apply(sentiment_analyzer)
+    chart_object = sentiment_bar_chart(df)
+    return  chart_object, df
+
+demo = gr.Interface(fn=read_reviews_and_analyze_sentiment,
+                    inputs=[gr.File(file_types=["xlsx"], label="Laden Sie Ihre xls-Review-Datei hoch")],
+                    outputs=[ gr.Plot(label="Stimmungsanalyse"), gr.Dataframe(label="Stimmungen")],
+                    title="Project 3: Stimmung-Analysator",
+                    description="DIESE ANWENDUNG WIRD VERWENDET, UM DIE STIMMUNG AUF DER GRUNDLAGE DER HOCHGELADENEN DATEI ZU ANALYSIEREN.",
+                    allow_flagging="never",
+                    submit_btn="Übermitteln",
+                    clear_btn="Bereinigen",)
+demo.launch()