b

app.py

@@ -1,29 +1,134 @@
+import time
 import streamlit as st
 import pandas as pd
-import time
-import search  # This imports the modified search module
+import os
+from dotenv import load_dotenv
+import search  # Import the search module
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+from docx import Document
+
+load_dotenv()
+
+st.set_page_config(
+        page_title="DocGPT GT",
+        page_icon="speech_balloon",
+        layout="wide",
+    )
+
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            footer:after {
+	content:'2023'; 
+	visibility: visible;
+	display: block;
+	position: relative;
+	padding: 5px;
+	top: 2px;
+}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+def save_as_pdf(conversation):
+    pdf_filename = "conversation.pdf"
+    c = canvas.Canvas(pdf_filename, pagesize=letter)
+   
+    c.drawString(100, 750, "Conversation:")
+    y_position = 730
+    for q, a in conversation:
+        c.drawString(120, y_position, f"Q: {q}")
+        c.drawString(120, y_position - 20, f"A: {a}")
+        y_position -= 40
+   
+    c.save()
+   
+    st.markdown(f"Download [PDF](./{pdf_filename})")
+
+def save_as_docx(conversation):
+    doc = Document()
+    doc.add_heading('Conversation', 0)
+   
+    for q, a in conversation:
+        doc.add_paragraph(f'Q: {q}')
+        doc.add_paragraph(f'A: {a}')
+   
+    doc_filename = "conversation.docx"
+    doc.save(doc_filename)
+   
+    st.markdown(f"Download [DOCX](./{doc_filename})")
+
+def save_as_xlsx(conversation):
+    df = pd.DataFrame(conversation, columns=["Question", "Answer"])
+    xlsx_filename = "conversation.xlsx"
+    df.to_excel(xlsx_filename, index=False)
+   
+    st.markdown(f"Download [XLSX](./{xlsx_filename})")
+
+def save_as_txt(conversation):
+    txt_filename = "conversation.txt"
+    with open(txt_filename, "w") as txt_file:
+        for q, a in conversation:
+            txt_file.write(f"Q: {q}\nA: {a}\n\n")
+   
+    st.markdown(f"Download [TXT](./{txt_filename})")
 
 def main():
-    st.title("Enhanced Document Query Interface")
-
-    uploaded_files = st.file_uploader("Upload documents", type=['pdf', 'docx'], accept_multiple_files=True)
-    question = st.text_input("What's your question?")
-
-    if st.button("Answer"):
-        if uploaded_files and question:
-            document_texts = []
-            for uploaded_file in uploaded_files:
-                if uploaded_file.type == "application/pdf":
-                    paragraphs = search.read_pdf_pdfminer(uploaded_file)
-                else:  # assuming docx
-                    paragraphs = search.read_docx(uploaded_file)
-                document_texts.extend([para.content for para in paragraphs])
-
-            documents_df = pd.DataFrame(document_texts, columns=['content'])
-            answer = search.answer_question(question, documents_df)
+    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")
+
+    progress = st.progress(0)
+    for i in range(100):
+        progress.progress(i + 1)
+        time.sleep(0.01)
+
+    if uploaded_files:
+        df = pd.DataFrame(columns=["page_num", "paragraph_num", "content", "tokens"])
+        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))
+                for p in paragraphs],
+                columns=["page_num", "paragraph_num", "content", "tokens"]
+            )
+            df = pd.concat([df, temp_df], ignore_index=True)
+
+        if "interactions" not in st.session_state:
+            st.session_state["interactions"] = []
+
+        answer = ""
+        if question != st.session_state.get("last_question", ""):
+            st.text("Searching...")
+            answer = search.answer_query_with_context(question, df)
+            st.session_state["interactions"].append((question, answer))
             st.write(answer)
-        else:
-            st.write("Please upload at least one document and enter a question.")
+
+        st.markdown("### Interaction History")
+        for q, a in st.session_state["interactions"]:
+            st.write(f"**Q:** {q}\n\n**A:** {a}")
+
+        st.session_state["last_question"] = question
+
+        st.markdown("<h2>Sample paragraphs</h2>", unsafe_allow_html=True)
+        sample_size = min(len(df), 5)
+        st.dataframe(df.sample(n=sample_size))  
+
+        if st.button("Save as PDF"):
+            save_as_pdf(st.session_state["interactions"])
+        if st.button("Save as DOCX"):
+            save_as_docx(st.session_state["interactions"])
+        if st.button("Save as XLSX"):
+            save_as_xlsx(st.session_state["interactions"])
+        if st.button("Save as TXT"):
+            save_as_txt(st.session_state["interactions"])
+
+
+    else:
+        st.markdown("<h2>Please upload a document to proceed.</h2>", unsafe_allow_html=True)
 
 if __name__ == "__main__":
     main()