new update

app.py

@@ -1,9 +1,48 @@
 import streamlit as st
 from transformers import pipeline
 
+# The one with 'sentiment-analysis'
 pipe = pipeline('sentiment-analysis')
 test = st.text_area('enter the text:')
 
 if test:
     out = pipe(test)
-    st.json(out)
+    st.json(out)
+
+# The one with "text-classification"
+pipe = pipeline('text-classification')
+test = st.text_area('enter the text:')
+
+if test:
+    out = pipe(test)
+    st.json(out)
+
+
+
+
+
+
+
+# text generation from youtube vid
+# st.write("And now for something completely different...")
+#
+# default_value = "See how a modern neural network auto-completes your text using HuggingFace"
+# st.write("\n\nThe King of Text Generation, GPT-2 comes in four available sizes, only three of which have been made publicly available.")
+#
+# sent = st.text_area("Text", defalut_value, height=275)
+# max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=30)
+# temperature = st.sidebar.slider("Temperature", value = 1.0, min_value=0.0, max_value=1.0, step=0.05)
+# top_k = st.sidebar.slider("Top-k", min_value=0, max_value=5, value=0)
+# top_p = st.sidebar.slider("top-p", min_value=0.0, max_value=1.0, step=0.05, value=0.9)
+# num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=5, value=1, step=1)
+#
+# encoded_prompt = tokenizer.encode(sent, add_special_tokens=False, return_tensors="pt")
+# if encoded_prompt.size()[-1] == 0:
+#     input_ids = None
+# else:
+#     input_ids = encoded_prompt
+#
+# output_sequences = infer(input_ids, max_length, temperature, top_k, top_p, num_return_sequences)
+#
+# for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
+#     print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")