Update app.py

app.py

@@ -1,30 +1,42 @@
 import streamlit as st
-import os
-from google import genai
-from google.genai import types
-from kaggle_secrets import UserSecretsClient
+from transformers import pipeline
 
+# Set up Streamlit UI
+st.set_page_config(page_title="Text Prompting Demo", layout="centered")
+st.title("🤖 Text Prompting using Transformers")
 
-# Get API key from environment variables
-GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
+# Add model selector
+task_option = st.selectbox(
+    "Select Task",
+    ("Text Generation", "Text Classification", "Question Answering")
+)
 
-# Initialize GenAI client
-client = genai.Client(api_key=GOOGLE_API_KEY)
+# User input
+user_input = st.text_area("Enter your input text", height=150)
 
-# Define functions for different functionalities
-# ... (Include your code snippets here, refactoring as needed) ...
+# Run pipeline based on task
+if st.button("Generate Output"):
+    if not user_input.strip():
+        st.warning("Please enter some text input.")
+    else:
+        if task_option == "Text Generation":
+            generator = pipeline("text-generation", model="gpt2")
+            output = generator(user_input, max_length=50, num_return_sequences=1)
+            st.subheader("Generated Text")
+            st.write(output[0]['generated_text'])
 
-# Example function from your code
-def generate_text(prompt):
-    response = client.models.generate_content(
-        model="gemini-2.0-flash",
-        contents=prompt
-    )
-    return response.text
+        elif task_option == "Text Classification":
+            classifier = pipeline("sentiment-analysis")
+            output = classifier(user_input)
+            st.subheader("Classification Result")
+            st.json(output)
 
-# Streamlit interface
-st.title("My GenAI App")
-user_input = st.text_input("Enter your prompt:")
-if user_input:
-    output = generate_text(user_input)
-    st.write(output)
+        elif task_option == "Question Answering":
+            context = st.text_area("Enter context for question answering", height=150)
+            if not context.strip():
+                st.warning("Please provide context.")
+            else:
+                qa = pipeline("question-answering")
+                result = qa(question=user_input, context=context)
+                st.subheader("Answer")
+                st.write(result['answer'])