* add conversion from pdf to latex-like (.mmd) format with nougat

* change vector_db_manager.py to handle .mmd
* add "conversion" tab
* add math mode checkbox in maintab

front_end/flagged/log.csv

@@ -1,9 +0,0 @@
-PDF File,output,flag,username,timestamp
-flagged\PDF File\bc51931986aa4ff95e72\integration.pdf,"'
-    <iframe
-      src=""http://localhost:8000/pdfs/integration.pdf""
-      :width=""1000px""
-      :height=""2000px""
-      frameborder=""0""
-    ></iframe>
-    ",,,2024-04-15 02:34:48.167890

front_end/main.py

@@ -7,6 +7,7 @@ from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
 
 from backend.vector_db_manager import VectorDbManager
 from backend.inference import InferenceInstance
+from backend.pdf_to_mmd import pdf_to_mmd
 import time
 
 
@@ -22,8 +23,6 @@ port = get_accessible_port()
 
 
 # Launch a simple HTTP server to serve the PDF files
-
-
 def start_server():
     command = ['python', '-m', 'http.server', f"{port}"]
     # Set the working directory to the documents folder to serve the PDF files
@@ -57,12 +56,19 @@ def bot(history):
     global user_message_global, doc_path
 
     if doc_path != "":
-        print("FOUND DOC_PATH")
-        vector_db_manager.create_vector_store_from_pdf(doc_path)
+        print(f"FOUND DOC_PATH {doc_path}")
+        doc_extension = doc_path.split(".")[-1]
+        if doc_extension == "mmd":
+            vector_db_manager.create_vector_store_from_latex(Path(doc_path))
+        elif doc_extension == "pdf":
+            vector_db_manager.create_vector_store_from_pdf(doc_path)
+        else:
+            print(f"Unsupported extension: {doc_extension}")
     else:
         print("NOT FOUND DOC_PATH")
 
-    bot_message = inference_instance.get_next_token(user_message_global, doc_path.split("\\")[-1])
+    doc_name = Path(doc_path).stem + ".mmd" if math_checkbox.value else Path(doc_path).name
+    bot_message = inference_instance.get_next_token(user_message_global, doc_name)
     history[-1][1] = ""
     for message in bot_message:
         history[-1][1] = message
@@ -70,11 +76,21 @@ def bot(history):
         yield history
 
 
-def update_path(p):
+def update_path(p, checked):
     """Update the global variable doc_path with the selected PDF path"""
+    print("Updating path")
     global doc_path
-    doc_path = str(p)
-    print(f"Selected PDF path: {doc_path}")
+    name = Path(p).name
+    stem = Path(p).stem
+    if checked:
+        if not (Path(r"../documents/mmds") / (stem + ".mmd")).exists():
+            print(f"Converting {name} to MMD")
+            pdf_to_mmd(r"../documents/pdfs/" + name)
+        print(f"Selected DOC path: {stem}.mmd")
+        doc_path = r"../documents/mmds/" + stem + ".mmd"
+    else:
+        print(f"Selected DOC path: {name}")
+        doc_path = str(p)
 
 
 def pdf_viewer(pdf_file):
@@ -107,6 +123,7 @@ with gr.Blocks() as main_tab:
         with gr.Row():
             with gr.Column(scale=12):
                 file_input = gr.File(label="Select a PDF file")
+                math_checkbox = gr.Checkbox(label="Enable math mode (your pdf file will be converted to some latex-like format for the chatbot to understand it better)")
 
     with gr.Column():
         with gr.Group():
@@ -117,7 +134,7 @@ with gr.Blocks() as main_tab:
             )
 
     file_input.change(pdf_viewer, inputs=file_input, outputs=pdf_output)
-    file_input.upload(update_path, inputs=file_input)
+    file_input.upload(update_path, inputs=[file_input, math_checkbox])
 
 
 # Define options tab
@@ -129,6 +146,21 @@ with gr.Blocks() as options_tab:
                 gr.Textbox(label="Options", scale=2)
 
 
-app = gr.TabbedInterface([main_tab, options_tab], ["Main", "Options"])
+# Define conversion tab
+with gr.Blocks() as conversion_tab:
+    with gr.Column():
+        file_input = gr.File(label="Select a PDF file to convert to MMD")
+        html_output = gr.HTML(label="Output")
+
+    def upload_func(file_input):
+        name = Path(file_input).name
+        file_path = fr"../documents/pdfs/{name}"
+        pdf_to_mmd(file_path)
+
+
+    file_input.upload(upload_func, inputs=file_input)
+
+
+app = gr.TabbedInterface([main_tab, options_tab, conversion_tab], ["Main", "Options", "Conversion"])
 app.queue()
 app.launch()