Create app.py

app.py

@@ -0,0 +1,55 @@
+import streamlit as st
+from transformers import pipeline
+
+# Initialize pipelines for each NLP task
+ner = pipeline("ner")
+qa = pipeline("question-answering")
+text_gen = pipeline("text-generation")
+summarization = pipeline("summarization")
+
+def main():
+  """
+  This function builds the Streamlit app with user input for NLP tasks.
+  """
+
+  # Title and description for the app
+  st.title("Multi-Task NLP App")
+  st.write("Perform various NLP tasks on your text input.")
+
+  # Text input field
+  user_input = st.text_input("Enter Text Here:")
+
+  # Select task from dropdown menu
+  selected_task = st.selectbox("Choose NLP Task:", ["NER", "QA", "Text Generation", "Text Summarization"])
+
+  # Perform NLP task based on selection
+  if user_input and selected_task:
+    if selected_task == "NER":
+      analysis = ner(user_input)
+      st.write("**Named Entities:**")
+      for entity in analysis:
+          st.write(f"- {entity['word']} ({entity['entity_group']})")
+    elif selected_task == "QA":
+      # Provide context (optional) for QA
+      context = st.text_input("Enter Context (Optional):", "")
+      if context:
+          analysis = qa(question="Your question", context=context, padding="max_length")
+      else:
+          analysis = qa(question="Your question", context=user_input, padding="max_length")
+      st.write("**Answer:**", analysis['answer'])
+    elif selected_task == "Text Generation":
+      # Choose generation task from another dropdown
+      generation_task = st.selectbox("Choose Generation Task:", ["Text summarization (short)", "Poem", "Code"])
+      if generation_task == "Text summarization (short)":
+          analysis = summarization(user_input, max_length=50, truncation=True)
+      else:
+          # Experiment with different prompts and max_length for creative text generation
+          prompt = st.text_input("Enter Prompt (Optional):", "")
+          analysis = text_gen(prompt if prompt else user_input, max_length=50, truncation=True)
+      st.write("**Generated Text:**", analysis[0]['generated_text'])
+    else:
+      analysis = summarization(user_input, max_length=100, truncation=True)
+      st.write("**Summary:**", analysis[0]['summary_text'])
+
+if __name__ == "__main__":
+  main()