Create app.py

app.py

@@ -0,0 +1,50 @@
+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from datasets import load_dataset
+
+dataset = load_dataset('HUPD/hupd',
+    name='sample',
+    data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather", 
+    icpr_label=None,
+    train_filing_start_date='2016-01-01',
+    train_filing_end_date='2016-01-01',
+    val_filing_start_date='2016-01-30',
+    val_filing_end_date='2016-01-31',
+)
+p_number = dataset["validation"]["patent_number"][:10]
+p_abstract = dataset["validation"]["abstract"][:10]
+p_claims = dataset["validation"]["claims"][:10]
+p_decision = dataset["validation"]["decision"][:10]
+
+# Streamlit app
+st.title("Patentability Score")
+selected_model = st.selectbox("App. ID:", p_number)
+
+
+# st.write("Select a patent application ID")
+# text = st.text_input("Text:", "I love you!")
+# # Prepare analysis model, tokenizer and pipeline
+# def get_pipeline(selected_model):
+#     model = AutoModelForSequenceClassification.from_pretrained(selected_model)
+#     tokenizer = AutoTokenizer.from_pretrained(selected_model)
+#     pl = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+#     return pl
+
+# # Load the model and perform sentiment analysis
+# if st.button("Submit"):
+#     with st.spinner("Analyzing sentiment..."):
+#         pl = get_pipeline(selected_model)
+#         result = pl(text)
+#         label = result[0]['label']
+#         if(selected_model == "cardiffnlp/twitter-roberta-base-sentiment"):
+#             if label == "LABEL_0": st.write("Sentiment: Negative")
+#             elif label == "LABEL_1": st.write("Sentiment: Neutral")
+#             elif label == "LABEL_2": st.write("Sentiment: Positive")
+#         elif(selected_model == "textattack/bert-base-uncased-SST-2"):
+#             if label == "LABEL_0": st.write("Sentiment: Negative")
+#             elif label == "LABEL_1": st.write("Sentiment: Positive")
+#         else:
+#             st.write(f"Sentiment: {label}")
+#         st.write(f"Confidence Score: {result[0]['score']:.2f}")
+# else:
+#     st.write("Click 'Submit' for sentiment analysis.")