Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+# Load the sentiment analysis model and tokenizer
+model_name = "mmr44/fine-tuned-hupd-model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Create a sentiment analysis pipeline
+sentiment_analysis = pipeline('text-classification', model=model, tokenizer=tokenizer)
+
+# Load dataset to get patent numbers
+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',
+)
+
+train_set = dataset_dict['train']
+# Convert to DataFrame to get patent numbers
+train_df = train_set.to_pandas()
+patent_numbers = train_df['patent_number'].unique().tolist()
+
+# Create a dropdown menu for patent application numbers
+st.title("Patent Application Sentiment Analysis")
+
+application_number = st.selectbox(
+    "Select Patent Application Number",
+    patent_numbers  # Populate dropdown with patent numbers from the dataset
+)
+
+# Show abstract and claims
+selected_patent = train_df[train_df['patent_number'] == application_number].iloc[0]
+abstract_text = st.text_area("Abstract", selected_patent['abstract'])
+claims_text = st.text_area("Claims", selected_patent['claims'])
+
+# Function to truncate text
+def truncate_text(text, tokenizer, max_length=512):
+    tokens = tokenizer.encode(text, add_special_tokens=False, truncation=True, max_length=max_length)
+    return tokenizer.decode(tokens, skip_special_tokens=True)
+
+# Submit button
+if st.button("Submit"):
+    # Prepare the text for analysis
+    text_to_analyze = f"Abstract: {abstract_text} Claims: {claims_text}"
+    
+    # Truncate the text if it's too long
+    truncated_text = truncate_text(text_to_analyze, tokenizer)
+
+    # Perform sentiment analysis only if the text is non-empty
+    if truncated_text.strip():
+        inputs = tokenizer(truncated_text, return_tensors="pt", max_length=512, truncation=True)
+        outputs = model(**inputs)
+        probs = outputs.logits.softmax(dim=-1)
+        labels = sentiment_analysis.model.config.id2label
+        label = labels[probs.argmax().item()]
+        score = probs.max().item()
+        
+        # Display the result
+        st.write(f"Sentiment: {label}, Score: {score}")
+    else:
+        st.write("The text is too short for analysis.")