Add Streamlit app for patentability score prediction

app.py

@@ -29,28 +29,86 @@ val_df = pd.DataFrame(dataset_dict['validation'])
 print("Train set columns:", train_df.columns.tolist())
 print("Validation set columns:", val_df.columns.tolist())
 
-# Title and description
-st.title("Milestone Patent 🐨")
-st.write("Select a patent application to evaluate its patentability.")
+# Check if 'patent_number' exists
+if 'patent_number' not in train_df.columns:
+    st.error("Column 'patent_number' not found in the training dataset.")
+else:
+    # Title and description
+    st.title("📜 Milestone Patent Evaluation 🐨")
+    st.write("Select a patent application to evaluate its patentability.")
 
-# Dropdown for patent numbers
-patent_numbers = train_df['patent_number'].unique()
-selected_patent = st.selectbox("Select Patent Number", patent_numbers)
+    # Dropdown for patent numbers
+    patent_numbers = train_df['patent_number'].unique()
+    selected_patent = st.selectbox("Select Patent Number", patent_numbers)
 
-# Retrieve abstract and claims
-if selected_patent:
+    # Retrieve relevant information
     patent_info = train_df[train_df['patent_number'] == selected_patent].iloc[0]
+    title = patent_info['title']
     abstract = patent_info['abstract']
     claims = patent_info['claims']
+    background = patent_info['background']
+    summary = patent_info['summary']
+    description = patent_info['description']
+    cpc_label = patent_info['cpc_label']
+    ipc_label = patent_info['ipc_label']
+    filing_date = patent_info['filing_date']
+    patent_issue_date = patent_info['patent_issue_date']
+    date_published = patent_info['date_published']
+    examiner_id = patent_info['examiner_id']
 
-    # Display the abstract and claims
+    # Display the information
+    st.markdown("### Title")
+    st.markdown(f"**{title}**")
+    
+    st.markdown("---")
+    
+    st.markdown("### Abstract")
     st.text_area("Abstract", abstract, height=150)
+    
+    st.markdown("---")
+    
+    st.markdown("### Claims")
     st.text_area("Claims", claims, height=150)
+    
+    st.markdown("---")
+    
+    st.markdown("### Background")
+    st.text_area("Background", background, height=150)
+    
+    st.markdown("---")
+    
+    st.markdown("### Summary")
+    st.text_area("Summary", summary, height=150)
+    
+    st.markdown("---")
+    
+    st.markdown("### Description")
+    st.text_area("Description", description, height=150)
+    
+    st.markdown("---")
+    
+    st.markdown("### CPC Label")
+    st.markdown(f"**{cpc_label}**")
+    
+    st.markdown("### IPC Label")
+    st.markdown(f"**{ipc_label}**")
+    
+    st.markdown("### Filing Date")
+    st.markdown(f"**{filing_date}**")
+    
+    st.markdown("### Patent Issue Date")
+    st.markdown(f"**{patent_issue_date}**")
+    
+    st.markdown("### Date Published")
+    st.markdown(f"**{date_published}**")
+    
+    st.markdown("### Examiner ID")
+    st.markdown(f"**{examiner_id}**")
 
     # Submit button
     if st.button("Get Patentability Score"):
         # Prepare the input text
-        input_text = f"{abstract} {claims}"
+        input_text = f"{title} {abstract} {claims} {background} {summary} {description}"
         inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
 
         # Get the model prediction
@@ -61,16 +119,4 @@ if selected_patent:
         # Display the patentability score
         decision_labels = ['REJECTED', 'ACCEPTED', 'PENDING', 'CONT-REJECTED', 'CONT-ACCEPTED', 'CONT-PENDING']
         score = decision_labels[predictions.item()]
-        st.write(f"Patentability Score: **{score}**")
-
-# Additional button to evaluate the model on the validation set
-if st.button("Evaluate Model"):
-    eval_logits = []
-    for _, row in val_df.iterrows():
-        input_text = f"{row['abstract']} {row['claims']}"
-        inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding='max_length', max_length=512)
-        with torch.no_grad():
-            logits = model(**inputs).logits
-            eval_logits.append(logits)
-
-    st.write("Evaluation complete.")
+        st.success(f"Patentability Score: **{score}**")