Update app.py

app.py

@@ -1,17 +1,17 @@
+import streamlit as st
 from transformers import pipeline
 
-# Initialize a Zero-Shot reasoning model (T5)
-t5_model = pipeline("text2text-generation", model="t5-large")
+# Load Zero-Shot Classification Model
+zero_shot_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 
-# Initialize GPT-2 for text generation (CoT reasoning alternative)
-gpt_model = pipeline("text-generation", model="gpt2")  # You can replace "gpt2" with another available model like "EleutherAI/gpt-neo-2.7B"
+# Input from the user
+st.title("Zero-Shot Classification")
+text_input = st.text_area("Enter text to classify:", "")
 
-# Simple example of Zero-Shot reasoning with T5
-input_text = "Can you explain why the sky is blue using reasoning?"
-t5_output = t5_model(input_text, max_length=100)
-print("T5 Model Output:", t5_output)
+# Define possible candidate labels
+candidate_labels = ["reasoning", "logic", "problem-solving", "explanation"]
 
-# Example of text generation with GPT-2 (CoT reasoning alternative)
-cot_input = "Explain why the sky is blue step by step."
-gpt_output = gpt_model(cot_input, max_length=150)
-print("GPT-2 Output:", gpt_output)
+# Run the zero-shot model
+if text_input:
+    result = zero_shot_classifier(text_input, candidate_labels)
+    st.write(result)