create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from transformers import pipeline
+translator = pipeline("translation_en_to_de",model="Helsinki-NLP/opus-mt-en-fr")
+sa = pipeline("sentiment-analysis",model = "MarieAngeA13/Sentiment-Analysis-BERT")
+text_gen = pipeline("text-generation", model="gpt2")
+def translate_to_fr(text):
+  translate = translator(text ,max_length = len(text.split())+5)
+  return translate[0]['translation_text']
+def generate_text(prompt):
+  generated_text = text_gen(prompt, max_length=len(prompt.split()) + 5, num_return_sequences=1, do_sample=True)
+  return generated_text[0]['generated_text']
+def sentiment_analysis(text):
+  sentiment = sa(text)
+  if sentiment[0]['label'] == 'positive':
+    return "Happy"
+  if sentiment[0]['label'] == 'negative':
+    return "Unhappy"
+  if sentiment[0]['label'] == 'neutral':
+    return "Neither happy nor unhappy"
+with gr.Blocks() as demo:
+  gr.Markdown("Text Pipeline :Translation, Text Generation, Sentiment Analysis")
+
+  with gr.Row():
+    translate_btn = gr.Button("Translate")
+    generate_btn = gr.Button("Generate")
+    analyze_btn = gr.Button("Analyze")
+
+  input_text = gr.Textbox(label="Enter text")
+  output_text = gr.Textbox(label="Output")
+
+  translate_btn.click(translate_to_fr, inputs=input_text, outputs=output_text)
+  generate_btn.click(generate_text, inputs=input_text, outputs=output_text)
+  analyze_btn.click(sentiment_analysis, inputs=input_text, outputs=output_text)
+demo.launch()