update IGL

README.md

@@ -5,6 +5,6 @@ colorFrom: red
 colorTo: indigo
 sdk: gradio
 sdk_version: 4.42.0
-app_file: app_drive.py
+app_file: app_temp.py
 pinned: false
 ---

app.py

@@ -6,7 +6,6 @@ import datetime
 import os
 
 from lc_base.chain import openai_chain
-from driveapi.drive import upload_chat_to_drive
 
 # global time_diff, model_name, search_type
 time_diff = 0
@@ -51,22 +50,6 @@ def respond(message, chat_history):
     time.sleep(2)
     return " ", chat_history
 
-def save_feedback(feedback):
-    global user_feedback
-    user_feedback = feedback
-
-    curr_date = datetime.datetime.now()
-    file_name = f"chat_{curr_date.day}_{curr_date.month}_{curr_date.hour}_{curr_date.minute}.csv"
-    log_data = [
-        ["Question", "Response", "Model", "Time", "Feedback"],
-        [input_question, model_response, model_name, time_diff, user_feedback]
-    ]
-
-    if user_feedback == "Yes" or  feedback == "No":
-        upload_chat_to_drive(log_data, file_name)
-
-def default_feedback():
-    return "🤔"
 
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="emerald", neutral_hue="slate")) as chat:
     gr.HTML(title)
@@ -87,22 +70,9 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="emerald", neutral_hue="slate"))
             ], inputs=msg, label= "Click on any example to copy in the chatbox"
             )
 
-        with gr.Column():
-            feedback_radio = gr.Radio(
-                choices=["Yes", "No", "🤔"],
-                value=["🤔"],
-                label="Did you like the latest response?",
-                info="Selecting Yes/No will send the following diagnostic data - Question, Response, Time Taken",
-            )
 
     msg.submit(respond, [msg, chatbot], [msg, chatbot])
-    msg.submit(default_feedback, outputs=[feedback_radio])
-
 
-    feedback_radio.change(
-        fn=save_feedback,
-        inputs=[feedback_radio]
-    )
 
     gr.HTML(description)
 

app_temp.py

@@ -0,0 +1,131 @@
+# Application file for Gradio App for OpenAI Model
+
+import gradio as gr
+import time
+import datetime
+import os
+
+from lc_base.chain import openai_chain
+from lc_base.dnd_database import create_dnd_database
+
+############################# Global Params #############################
+
+time_diff = 0
+# model_name="gpt-3.5-turbo-1106" # FOR TESTING
+# model_name = "gpt-4-1106-preview"
+model_name = "gpt-4o-mini-2024-07-18"
+search_type = "stuff"
+input_question = ""
+model_response = ""
+user_feedback = ""
+
+dir = ""
+title = """<h1 align="center">ResearchBuddy</h1>"""
+description = """<br><br><h3 align="center">This is a GPT based Research Buddy to assist in navigating new research topics.</h3>"""
+
+DEFAULT_STATUS = "⬆Upload PDF files"
+
+
+############################# Drag and Drop PDF processing #############################
+def check_pdfs(pdf_files):
+    global db
+    db = create_dnd_database(pdf_files)
+    if not db:
+        return "Please upload PDF files again or submit a drive link containing only PDFs."
+    else:
+        return "Processing Completed - You can start the chat now!"
+
+############################# Chatbot Specific functions #############################
+def user(user_message, history):
+    return "", history + [[user_message, None]]
+
+def respond(message, chat_history):
+
+    global time_diff, model_response, input_question
+
+    question = str(message)
+    chain = openai_chain(inp_dir=dir)
+
+    query = question
+    start_time = time.time()
+
+    output = chain.get_response_from_drive(query=query, database=db, k=10, model_name=model_name, type=search_type)
+    
+    # Update global variables for logging
+    time_diff = time.time() - start_time
+    model_response = output
+    input_question = question
+    bot_message = output
+    chat_history.append((message, bot_message))
+
+    time.sleep(1) # Pause for a second to avoid overloading
+    return " ", chat_history 
+
+
+############################# Gradio Application Block #############################
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="emerald", neutral_hue="slate")) as chat:
+    gr.HTML(title)
+
+    global db
+
+    # PDF Drag and Drop + Drive link Input + Status containers
+    with gr.Row(equal_height=True):
+        with gr.Column():
+            with gr.Row():
+                pdf_files_dnd = gr.File(file_count='multiple', height=250, label="Upload PDF Files")
+            with gr.Row():
+                status_message = gr.Text(label="Status", value=DEFAULT_STATUS, text_align='center')
+            
+
+
+    pdf_files_dnd.change(
+        fn=check_pdfs, 
+        inputs=[pdf_files_dnd], 
+        outputs=[status_message], 
+        preprocess=False, 
+        postprocess=False) # Set preprocess and postprocess to False, to avoid the tmpfile object creation, instead get a Dict
+
+    # Chatbot container
+    chatbot = gr.Chatbot(height=750)
+    msg = gr.Textbox(label="Send a message", placeholder="Send a message",
+                             show_label=False, container=False)  
+    
+    with gr.Row():
+        with gr.Column():
+            clear_history_button = gr.ClearButton(value="Clear Chat History")
+
+        with gr.Column():
+            new_chat_button = gr.ClearButton(value="New Chat")
+
+    # Sample questions
+    with gr.Row():
+        with gr.Column():
+            gr.Examples([
+                ["Explain these documents to me in simpler terms."],
+                ["What does these documents talk about?"],
+                ["Give the key topics covered in these documents in less than 10 words."],
+                ["What are the key findings in these documents?"],
+            ], inputs=msg, label= "Click on any example to copy in the chatbox"
+            )
+
+  
+    # Get a response when a message is submitted to the chatbot
+    msg.submit(
+        fn = respond, 
+        inputs = [msg, chatbot], 
+        outputs = [msg, chatbot],
+        queue = True)
+    
+
+    # Clear the chat history/ New chat
+    clear_history_button.click(lambda: [None, None], outputs=[msg, chatbot])
+    new_chat_button.click(
+        lambda: [None, None, None, None, DEFAULT_STATUS], 
+        outputs=[msg, chatbot, pdf_files_dnd, status_message])
+
+    # Description at the bottom of the application
+    gr.HTML(description)
+
+# Enable queing
+chat.queue()
+chat.launch()

lc_base/dnd_database.py

@@ -1,8 +1,22 @@
-from driveapi.drive import process_pdf
-
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.vectorstores import FAISS 
+import PyPDF2
+
+
+# Function to process a PDF file
+def process_pdf(file_stream):
+    if isinstance(file_stream, dict): # Check if PDF was obtained using Drag and Drop or Drive link
+        file_path = file_stream['name'] # Use 'path' for local testing and 'name' for Gradio
+        pdf_reader = PyPDF2.PdfReader(file_path)
+    else:
+        pdf_reader = PyPDF2.PdfReader(file_stream)
+    text = ""
+    for page_num in range(len(pdf_reader.pages)):
+        page = pdf_reader.pages[page_num]
+        text += page.extract_text()
+    return text
+
 
 def create_dnd_database(file_list):
     raw_text = ''