Update app.py

app.py

@@ -1,51 +1,51 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-from peft import PeftModel
-
-# Loading LED IN Model
-base_model = "nsi319/legal-led-base-16384"
-led = AutoModelForSeq2SeqLM.from_pretrained(base_model)
-adapter_model_in = f"Legal-LED_IN_ABS"
-led_in = PeftModel.from_pretrained(led, adapter_model_in)
-led_in_tokenizer = AutoTokenizer.from_pretrained(base_model)
-
-# Generating Summary
-def summarize(model, tokenizer, text):
-    input_tokenized = tokenizer.encode(text, return_tensors='pt', max_length=8192, truncation=True)
-    summary_ids = model.generate(input_tokenized, num_beams=4, length_penalty=0.1, min_length=32, max_length=512)
-    summary = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids][0]
-    return summary
-
-# Reading Txt File
-def read_txt_file(file):
-    text = file.read().decode('utf-8')
-    return text
-
-st.set_page_config(page_title="Legal AI Summarizer", page_icon="img.png")
-title = "Legal AI Summarizer"
-col1, col2 = st.columns([1,7])
-with col1:
-    st.image("img.png")
-with col2: st.title(title)
-st.write("Stuck with long legal documents? Our AI summarizer can help!  Just copy-paste the text or upload a .txt file, and it will give you a quick and easy summary in plain English, so you can understand the key points without all the legalese.")
-
-if "user_text" not in st.session_state:
-    st.session_state.user_text = ""
-
-upload_file = st.file_uploader("Upload a .txt file", type="txt")
-
-if upload_file is not None:
-    user_text = read_txt_file(upload_file)
-else:
-    user_text = st.text_area("Paste your legal document here:", value=st.session_state.user_text, height=300)
-
-if st.button("Generate Summary"):
-    with st.spinner("Generating summary..."):
-        try:
-            summary_text = summarize(led_in, led_in_tokenizer, user_text)
-            st.session_state.user_text = user_text
-            st.write("")
-            st.success(summary_text)
-            print(summary_text)
-        except Exception as e:
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from peft import PeftModel
+
+# Loading LED IN Model
+base_model = "nsi319/legal-led-base-16384"
+led = AutoModelForSeq2SeqLM.from_pretrained(base_model)
+adapter_model_in = f"Legal-LED_IN_ABS"
+led_in = PeftModel.from_pretrained(led, adapter_model_in)
+led_in_tokenizer = AutoTokenizer.from_pretrained(base_model)
+
+# Generating Summary
+def summarize(model, tokenizer, text):
+    input_tokenized = tokenizer.encode(text, return_tensors='pt', max_length=4096, truncation=True)
+    summary_ids = model.generate(input_tokenized, num_beams=4, length_penalty=0.1, min_length=32, max_length=256)
+    summary = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids][0]
+    return summary
+
+# Reading Txt File
+def read_txt_file(file):
+    text = file.read().decode('utf-8')
+    return text
+
+st.set_page_config(page_title="Legal AI Summarizer", page_icon="img.png")
+title = "Legal AI Summarizer"
+col1, col2 = st.columns([1,7])
+with col1:
+    st.image("img.png")
+with col2: st.title(title)
+st.write("Stuck with long legal documents? Our AI summarizer can help!  Just copy-paste the text or upload a .txt file, and it will give you a quick and easy summary in plain English, so you can understand the key points without all the legalese.")
+
+if "user_text" not in st.session_state:
+    st.session_state.user_text = ""
+
+upload_file = st.file_uploader("Upload a .txt file", type="txt")
+
+if upload_file is not None:
+    user_text = read_txt_file(upload_file)
+else:
+    user_text = st.text_area("Paste your legal document here:", value=st.session_state.user_text, height=300)
+
+if st.button("Generate Summary"):
+    with st.spinner("Generating summary..."):
+        try:
+            summary_text = summarize(led_in, led_in_tokenizer, user_text)
+            st.session_state.user_text = user_text
+            st.write("")
+            st.success(summary_text)
+            print(summary_text)
+        except Exception as e:
             st.error(f"An error occurred: {e}")