removed unnecessary files

milestone-2.py

@@ -1,26 +1,26 @@
-import streamlit as st
-from transformers import (AutoTokenizer, TFAutoModelForSequenceClassification,
-                          pipeline)
-
-st.title("CS-GY-6613 Project Milestone 2")
-model_choices = (
-    "distilbert-base-uncased-finetuned-sst-2-english",
-    "j-hartmann/emotion-english-distilroberta-base",
-    "joeddav/distilbert-base-uncased-go-emotions-student",
-)
-
-with st.form("Input Form"):
-    text = st.text_area("Write your text here:", "CS-GY-6613 is a great course!")
-    model_name = st.selectbox("Select a model:", model_choices)
-    submitted = st.form_submit_button("Submit")
-
-if submitted:
-    model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
-    res = classifier(text)
-    label = res[0]["label"].upper()
-    score = res[0]["score"]
-    st.markdown(
-        f"This text was classified as **{label}** with a confidence score of **{score}**."
-    )
+import streamlit as st
+from transformers import (AutoTokenizer, TFAutoModelForSequenceClassification,
+                          pipeline)
+
+st.title("CS-GY-6613 Project Milestone 2")
+model_choices = (
+    "distilbert-base-uncased-finetuned-sst-2-english",
+    "j-hartmann/emotion-english-distilroberta-base",
+    "joeddav/distilbert-base-uncased-go-emotions-student",
+)
+
+with st.form("Input Form"):
+    text = st.text_area("Write your text here:", "CS-GY-6613 is a great course!")
+    model_name = st.selectbox("Select a model:", model_choices)
+    submitted = st.form_submit_button("Submit")
+
+if submitted:
+    model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+    res = classifier(text)
+    label = res[0]["label"].upper()
+    score = res[0]["score"]
+    st.markdown(
+        f"This text was classified as **{label}** with a confidence score of **{score}**."
+    )

milestone-3.py

@@ -1,72 +1,72 @@
-import streamlit as st
-import torch
-from datasets import load_dataset
-from transformers import AutoTokenizer 
-from transformers import AutoModelForSequenceClassification
-from transformers import pipeline
-
-# Load HUPD dataset
-dataset_dict = 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='2016-01-21',
-    val_filing_start_date='2016-01-22',
-    val_filing_end_date='2016-01-31',
-)
-
-# Process data
-filtered_dataset = dataset_dict['validation'].filter(lambda e: e['decision'] == 'ACCEPTED' or e['decision'] == 'REJECTED')
-dataset = filtered_dataset.shuffle(seed=42).select(range(20))
-dataset = dataset.sort("patent_number")
-
-
-# Create pipeline using model trainned on Colab
-model = torch.load("/workspaces/cs-gy-6613-project/patent_classification(1).pt", map_location=torch.device('cpu'))
-tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
-classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
-
-
-
-def load_patent():
-    selected_application = dataset.select([applications[st.session_state.id]])
-    st.session_state.abstract = selected_application['abstract'][0]
-    st.session_state.claims = selected_application['claims'][0]
-    st.session_state.title = selected_application['title'][0]
-
-
-
-st.title("CS-GY-6613 Project Milestone 3")
-
-# List patent numbers for select box
-applications = {}
-for ds_index, example in enumerate(dataset):
-    applications.update({example['patent_number']: ds_index })
-st.selectbox("Select a patent application:", applications, on_change=load_patent, key="id")
-
-# Application title displayed for additional context only, not used with model
-st.text_area("Title", key="title", value=dataset[0]['title'], height=50)
-
-# Classifier input form
-with st.form('Input Form'):
-    abstract = st.text_area("Abstract", key="abstract", value=dataset[0]['abstract'], height=200)
-    claims = st.text_area("Claims", key="claims", value=dataset[0]['abstract'], height=200)
-    submitted = st.form_submit_button("Get Patentability Score")
-
-if submitted:
-    selected_application = dataset.select([applications[st.session_state.id]])
-    res = classifier(abstract, claims)
-    if res[0]["label"] == 'LABEL_0':
-        pred = "ACCEPTED"
-    elif res[0]["label"] == 'LABEL_1': 
-        pred = "REJECTED"
-    score = res[0]["score"]
-    label = selected_application['decision'][0]
-    result = st.markdown("This text was classified as  **{}** with a confidence score of **{}**.".format(pred, score))
-    check = st.markdown("Actual Label: **{}**.".format(label))
-
-
-
-
-
+import streamlit as st
+import torch
+from datasets import load_dataset
+from transformers import AutoTokenizer 
+from transformers import AutoModelForSequenceClassification
+from transformers import pipeline
+
+# Load HUPD dataset
+dataset_dict = 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='2016-01-21',
+    val_filing_start_date='2016-01-22',
+    val_filing_end_date='2016-01-31',
+)
+
+# Process data
+filtered_dataset = dataset_dict['validation'].filter(lambda e: e['decision'] == 'ACCEPTED' or e['decision'] == 'REJECTED')
+dataset = filtered_dataset.shuffle(seed=42).select(range(20))
+dataset = dataset.sort("patent_number")
+
+
+# Create pipeline using model trainned on Colab
+model = torch.load("/workspaces/cs-gy-6613-project/patent_classification(1).pt", map_location=torch.device('cpu'))
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+
+
+def load_patent():
+    selected_application = dataset.select([applications[st.session_state.id]])
+    st.session_state.abstract = selected_application['abstract'][0]
+    st.session_state.claims = selected_application['claims'][0]
+    st.session_state.title = selected_application['title'][0]
+
+
+
+st.title("CS-GY-6613 Project Milestone 3")
+
+# List patent numbers for select box
+applications = {}
+for ds_index, example in enumerate(dataset):
+    applications.update({example['patent_number']: ds_index })
+st.selectbox("Select a patent application:", applications, on_change=load_patent, key="id")
+
+# Application title displayed for additional context only, not used with model
+st.text_area("Title", key="title", value=dataset[0]['title'], height=50)
+
+# Classifier input form
+with st.form('Input Form'):
+    abstract = st.text_area("Abstract", key="abstract", value=dataset[0]['abstract'], height=200)
+    claims = st.text_area("Claims", key="claims", value=dataset[0]['abstract'], height=200)
+    submitted = st.form_submit_button("Get Patentability Score")
+
+if submitted:
+    selected_application = dataset.select([applications[st.session_state.id]])
+    res = classifier(abstract, claims)
+    if res[0]["label"] == 'LABEL_0':
+        pred = "ACCEPTED"
+    elif res[0]["label"] == 'LABEL_1': 
+        pred = "REJECTED"
+    score = res[0]["score"]
+    label = selected_application['decision'][0]
+    result = st.markdown("This text was classified as  **{}** with a confidence score of **{}**.".format(pred, score))
+    check = st.markdown("Actual Label: **{}**.".format(label))
+
+
+
+
+