Update app.py

app.py

@@ -1,10 +1,44 @@
+# import streamlit as st
+# from transformers import pipeline
+
+# pipe = pipeline('sentiment-analysis')
+# text= st.text_area('enter some text')
+
+# if st.button('Submit'):
+#     if text:
+#         out = pipe(text)
+#         st.write(out)
+
 import streamlit as st
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# Load the GPT tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2")
+model = AutoModelForCausalLM.from_pretrained("sshleifer/tiny-gpt2")
+
+# Load the sentiment analysis pipeline
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+# Text input field
+user_input = st.text_area("Enter your prompt:")
+
+if st.button("Submit"):
+    if user_input:
+        # Generate text using the GPT model
+        inputs = tokenizer(user_input, return_tensors="pt")
+        generated_ids = model.generate(**inputs)
+        generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+        # Perform sentiment analysis on both original and generated text
+        original_sentiment = sentiment_pipeline(user_input)
+        generated_sentiment = sentiment_pipeline(generated_text)
+
+        # Display the results
+        st.write("Original Text:")
+        st.write(user_input)
+        st.write("Original Text Sentiment:", original_sentiment)
 
-pipe = pipeline('sentiment-analysis')
-text= st.text_area('enter some text')
+        st.write("Generated Text:")
+        st.write(generated_text)
+        st.write("Generated Text Sentiment:", generated_sentiment)
 
-if st.button('Submit'):
-    if text:
-        out = pipe(text)
-        st.write(out)