Update app.py

app.py

@@ -76,7 +76,7 @@ def save_as_txt(conversation):
     st.markdown(f"Download [TXT](./{txt_filename})")
 
 def main():
-    st.markdown('<h1>Ask anything from  Texts</h1><p style="font-size: 12; color: gray;"></p>', unsafe_allow_html=True)
+    st.markdown('<h1>Ask anything from Legal Texts</h1><p style="font-size: 12; color: gray;"></p>', unsafe_allow_html=True)
     st.markdown("<h2>Upload documents</h2>", unsafe_allow_html=True)
     uploaded_files = st.file_uploader("Upload one or more documents", type=['pdf', 'docx'], accept_multiple_files=True)
     question = st.text_input("Ask a question based on the documents", key="question_input")
@@ -91,7 +91,7 @@ def main():
         for uploaded_file in uploaded_files:
             paragraphs = search.read_pdf_pdfminer(uploaded_file) if uploaded_file.type == "application/pdf" else search.read_docx(uploaded_file)
             temp_df = pd.DataFrame(
-                [(p.page_num, p.paragraph_num, p.content, search.count_tokens(p.content, search.tokenizer))
+                [(p.page_num, p.paragraph_num, p.content, search.count_tokens(p.content))
                 for p in paragraphs],
                 columns=["page_num", "paragraph_num", "content", "tokens"]
             )
@@ -103,7 +103,7 @@ def main():
         answer = ""
         if question != st.session_state.get("last_question", ""):
             st.text("Searching...")
-            answer = search.answer_query_with_context(question, df, tokenizer, model)
+            answer = search.answer_query_with_context(question, df)
             st.session_state["interactions"].append((question, answer))
             st.write(answer)