Create app.py

app.py

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+!pip install gradio
+!pip install transformers
+
+import gradio as gr # Gradio library to create an interactive interface
+from transformers import pipeline # Transformers libraries which imports pipeline to use Hugging-Face models
+import pandas as pd # Pandas library for data manipulation and analysis
+import matplotlib.pylab as plt # Matplot library for the interactive visualizations
+
+# Initialize the analyzers
+
+# Loads a pretrained model for the Arabic language
+arabic_analyzer = pipeline('sentiment-analysis', model='CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment')
+
+# Loads a pretrained model for the English language
+english_analyzer = pipeline('sentiment-analysis', model='distilbert-base-uncased-finetuned-sst-2-english')
+
+
+# Define function
+
+def sentiment_analysis(language,file):
+  # Select the appropriate analyzer
+  if language == "Arabic":
+    analyzer = arabic_analyzer
+  else:
+    analyzer = english_analyzer
+
+  results = []
+
+  # Read the sentences from the uploaded file
+  with open (file.name,'r') as fn:
+    sentences = fn.readlines()
+
+  # Perform sentiment analysis on each sentence
+  for sentence in sentences:
+    result = analyzer(sentence)
+    result = result[0]
+
+    results.append({
+        "Sentence": sentence,
+        "Label": result['label'],
+        "Score": f"{result['score'] *100:.2f}%"
+    })
+  # Convert the results into a DataFrame
+  df = pd.DataFrame(results)
+  # Ensure every label is lower, if not this will cause a logic error
+  # English labels are Upper but Arabic labels are lower
+  df['Label'] = df['Label'].str.lower()
+  # Take the "Label" column values
+  label_value = df['Label'].value_counts()
+
+  # Pre-Define the plot parameters
+  labels = ['Positive','Neutral','Negative']
+  counts = [label_value.get('positive',0),
+           label_value.get('neutral',0),
+           label_value.get('negative',0)]
+  colors=['green','gray','red']
+
+  # Create a bar plot
+  plt.bar(labels,counts,color=colors)
+  plt.title('Sentiment-Analysis')
+  plt.xlabel("Labels")
+  plt.ylabel("No. of sentences")
+
+
+  return df,plt
+
+
+# Set up the Gradio interface
+
+demo = gr.Interface(
+    fn=sentiment_analysis,
+    inputs=[gr.Dropdown(choices=["Arabic","English"],
+            label="Select a Language",
+            value="Arabic"),
+            gr.File(label="Upload a file")],
+    outputs=[gr.DataFrame(label="Results"),
+             gr.Plot(label="Bar plot")],
+    title="Sentiment-Analysis",
+    description="Gradio interface that allows users to Choose what language the sentences will be and upload a text file containing the sentences to be analyzed, the sentences will be classified and results will be in a formatted table along with a plot sentiment distribution"
+)
+
+demo.launch(debug=True)