test

app.py

@@ -7,6 +7,9 @@ from langdetect import detect
 # Load a summarization model
 summarizer = pipeline("summarization")
 
+# Load a text generation model from Hugging Face
+text_generator = pipeline("text-generation", model="gpt2")
+
 def text_analysis(text):
     # Analyze text: word count, character count, language detection, and readability
     words = re.findall(r'\w+', text.lower())
@@ -31,7 +34,12 @@ def text_summarization(text):
     summary = summarizer(text, max_length=130, min_length=30, do_sample=False)[0]['summary_text']
     return summary
 
-# Define interfaces for text analysis and text summarization
+def generate_text(prompt):
+    # Generate text using the loaded Hugging Face model
+    generated_text = text_generator(prompt, max_length=50, num_return_sequences=1)[0]['generated_text']
+    return generated_text
+
+# Define interfaces for text analysis, text summarization, and text generation
 text_analysis_interface = gr.Interface(fn=text_analysis, 
                                        inputs=gr.Textbox(lines=4, placeholder="Type something here..."), 
                                        outputs=gr.JSON(label="Text Analysis"))
@@ -40,10 +48,12 @@ text_summarization_interface = gr.Interface(fn=text_summarization,
                                             inputs=gr.Textbox(lines=4, placeholder="Type something here..."), 
                                             outputs="text")
 
-# Launch the text analysis interface
-if __name__ == "__main__":
-    text_analysis_interface.launch()
+text_generation_interface = gr.Interface(fn=generate_text, 
+                                         inputs=gr.Textbox(lines=4, placeholder="Type a prompt..."), 
+                                         outputs="text")
 
-# Launch the text summarization interface
+# Launch the interfaces
 if __name__ == "__main__":
+    text_analysis_interface.launch()
     text_summarization_interface.launch()
+    text_generation_interface.launch()