Update Gpt4oDemo.py

Gpt4oDemo.py

@@ -1,402 +1,412 @@
-import gradio as gr
-import plotly.graph_objs as go
-import numpy as np
-import time
-from openai import OpenAI
-import os
-from hardCodedData import *
-from Helper import *
-import cv2
-from moviepy.editor import VideoFileClip
-import time
-import base64
-import whisperx
-import gc 
-from moviepy.editor import VideoFileClip
-from dotenv import load_dotenv
-
-load_dotenv()
-
-'''
-Model Information 
-GPT4o
-'''
-
-import openai
-api_key = os.getenv("OPENAI_API_KEY")
-client = openai.OpenAI(
-    api_key=api_key,
-    base_url="https://openai.gateway.salt-lab.org/v1",
-)
-MODEL="gpt-4o"
-
-# Whisperx config
-device = "cpu" 
-batch_size = 16 # reduce if low on GPU mem
-compute_type = "int8" # change to "int8" if low on GPU mem (may reduce accuracy)
-model = whisperx.load_model("large-v2", device, compute_type=compute_type)
-
-'''
-Video
-'''
-video_file = None
-audio_path=None
-base64Frames = []
-transcript=""
-
-def process_video(video_path, seconds_per_frame=2):
-    global base64Frames, audio_path
-    base_video_path, _ = os.path.splitext(video_path)
-
-    video = cv2.VideoCapture(video_path)
-    total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
-    fps = video.get(cv2.CAP_PROP_FPS)
-    frames_to_skip = int(fps * seconds_per_frame)
-    curr_frame=0
-
-    while curr_frame < total_frames - 1:
-        video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame)
-        success, frame = video.read()
-        if not success:
-            break
-        _, buffer = cv2.imencode(".jpg", frame)
-        base64Frames.append(base64.b64encode(buffer).decode("utf-8"))
-        curr_frame += frames_to_skip
-    video.release()
-
-    audio_path = "./TEST.mp3"
-    clip = VideoFileClip(video_path)
-    clip.audio.write_audiofile(audio_path, bitrate="32k")
-    clip.audio.close()
-    clip.close()
-    # transcribe_video(audio_path)
-    print(f"Extracted {len(base64Frames)} frames")
-    print(f"Extracted audio to {audio_path}")
-    return base64Frames, audio_path
-
-chat_history = []
-# chat_history.append({
-#             "role": "system",
-#             "content": (
-#                     """
-#                         You are an assistant chatbot for a Speech Language Pathologist (SLP). 
-#                         Your task is to help analyze a provided video of a therapy session and answer questions accurately. 
-#                         Provide timestamps for specific events or behaviors mentioned. Conclude each response with possible follow-up questions.
-
-#                         Follow these steps:
-
-#                         1.	Suggest to the user to ask, “To get started, you can try asking me how many people there are in the video.”
-#                         2.  Detect how many people are in the video.
-#                         2.	Suggest to the user to tell you the names of the people in the video, starting from left to right.
-#                         3.	After receiving the names, respond with, “Ok thank you! Now you can ask me any questions about this video.”
-#                         4.	If the user asks about a behavior, respond with, “My understanding of this behavior is [xxx - AI generated output]. Is this a behavior that you want to track? If it is, please define this behavior and tell me more about it so I can analyze it more accurately according to your practice.”
-#                         5.	If you receive names, confirm that these are the names of the people from left to right.
-#                     """
-#             )
-#         })
-
-def transcribe_video(filename):
-    global transcript
-    if not audio_path:
-        raise ValueError("Audio path is None")
-    print(audio_path)
-    audio = whisperx.load_audio(audio_path)
-    result = model.transcribe(audio, batch_size=batch_size)
-
-    model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
-    result = whisperx.align(result["segments"], model_a, metadata, audio, device, return_char_alignments=False)
-
-
-    hf_auth_token = os.getenv("HF_AUTH_TOKEN")
-    diarize_model = whisperx.DiarizationPipeline(use_auth_token=hf_auth_token, device=device)
-
-    diarize_segments = diarize_model(audio)
-
-    dia_result = whisperx.assign_word_speakers(diarize_segments, result)
-
-    for res in dia_result["segments"]:
-        # transcript += "Speaker: " + str(res.get("speaker", None)) + "\n"
-        transcript += "Dialogue: " + str(res["text"].lstrip()) + "\n"
-        transcript += "start: " + str(int(res["start"])) + "\n"
-        transcript += "end: " + str(int(res["end"])) + "\n"
-        transcript += "\n"
-
-    return transcript
-
-
-def handle_video(video=None):
-    global video_file, base64Frames, audio_path, chat_history, transcript
-    
-    if video is None:
-        # Load example video
-        video = "./TEST.mp4"
-   
-    base64Frames, audio_path = process_video(video_path=video, seconds_per_frame=100)
-    chat_history.append({
-        "role": "user",
-        "content": [
-            {"type": "text", "text": "These are the frames from the video."},
-            *map(lambda x: {"type": "image_url", "image_url": {"url": f'data:image/jpg;base64,{x}', "detail": "low"}}, base64Frames)
-        ]
-    })
-
-    if transcript:
-        chat_history[-1]['content'].append({
-            "type": "text", 
-            "text": f"Also, below is the template of transcript from the video:\n"
-                    "Speaker: <the speaker of the dialogue>\n"
-                    "Dialogue: <the text of the dialogue>\n"
-                    "start: <the starting timestamp of the dialogue in the video in second>\n"
-                    "end: <the ending timestamp of the dialogue in the video in second>\n"
-                    f"Transcription: {transcript}"
-        })
-
-    video_file = video
-    return video_file
-
-'''
-Chatbot
-'''
-
-def new_prompt(prompt):
-    global chat_history, video_file
-    chat_history.append({"role": "user","content": prompt,})
-    MODEL="gpt-4o"
-    # print(chat_history)
-    print(transcript)
-    try:
-        if video_file:
-            # Video exists and is processed
-            response = client.chat.completions.create(model=MODEL,messages=chat_history,temperature=0,)
-        else:
-            # No video uploaded yet
-            response = client.chat.completions.create(model=MODEL,messages=chat_history,temperature=0,)
-
-        # Extract the text content from the response and append it to the chat history
-        assistant_message = response.choices[0].message.content
-        chat_history.append({'role': 'model', 'content': assistant_message})
-        print(assistant_message)
-    except Exception as e:
-        print("Error: ",e)
-        assistant_message = "API rate limit has been reached. Please wait a moment and try again."
-        chat_history.append({'role': 'model', 'content': assistant_message})
-
-    # except google.api_core.exceptions.ResourceExhausted:
-    #     assistant_message = "API rate limit has been reached. Please wait a moment and try again."
-    #     chat_history.append({'role': 'model', 'parts': [assistant_message]})
-    # except Exception as e:
-    #     assistant_message = f"An error occurred: {str(e)}"
-    #     chat_history.append({'role': 'model', 'parts': [assistant_message]})
-
-    return chat_history
-
-def user_input(user_message, history):
-    return "", history + [[user_message, None]]
-
-def bot_response(history):
-    user_message = history[-1][0]
-    updated_history = new_prompt(user_message)
-    assistant_message = updated_history[-1]['content']
-    history[-1][1] = assistant_message
-    yield history
-
-
-'''
-Behaivor box
-'''
-initial_behaviors = [
-    ("Initiating Behavioral Request (IBR)", 
-    ("The child's skill in using behavior(s) to elicit aid in obtaining an object, or object related event", 
-    ["00:10", "00:45", "01:30"])),
-
-    ("Initiating Joint Attention (IJA)", 
-    ("The child's skill in using behavior(s) to initiate shared attention to objects or events.", 
-    ["00:15", "00:50", "01:40"])),
-
-    ("Responding to Joint Attention (RJA)", 
-    ("The child's skill in following the examiner’s line of regard and pointing gestures.", 
-    ["00:20", "01:00", "02:00"])),
-
-    ("Initiating Social Interaction (ISI)", 
-    ("The child's skill at initiating turn-taking sequences and the tendency to tease the tester", 
-    ["00:20", "00:50", "02:00"])),
-
-    ("Responding to Social Interaction (RSI)", 
-    ("The child’s skill in responding to turn-taking interactions initiated by the examiner.", 
-    ["00:20", "01:00", "02:00"]))
-]
-
-behaviors = initial_behaviors
-behavior_bank = []
-
-def add_or_update_behavior(name, definition, timestamps, selected_behavior):
-    global behaviors, behavior_bank
-    if selected_behavior:  # Update existing behavior
-        for i, (old_name, _) in enumerate(behaviors):
-            if old_name == selected_behavior:
-                behaviors[i] = (name, (definition, timestamps))
-                break
-        # Update behavior in the bank if it exists
-        behavior_bank = [name if b == selected_behavior else b for b in behavior_bank]
-    else:  # Add new behavior
-        new_behavior = (name, (definition, timestamps))
-        behaviors.append(new_behavior)
-    choices = [b[0] for b in behaviors]
-    return gr.Dropdown(choices=choices, value=None, interactive=True), gr.CheckboxGroup(choices=behavior_bank, value=behavior_bank, interactive=True), "", "", ""
-
-def add_to_behaivor_bank(selected_behavior, checkbox_group_values):
-    global behavior_bank
-    if selected_behavior and selected_behavior not in checkbox_group_values:
-        checkbox_group_values.append(selected_behavior)
-    behavior_bank = checkbox_group_values
-    return gr.CheckboxGroup(choices=checkbox_group_values, value=checkbox_group_values, interactive=True), gr.Dropdown(value=None,interactive=True)
-
-def delete_behavior(selected_behavior, checkbox_group_values):
-    global behaviors, behavior_bank
-    behaviors = [b for b in behaviors if b[0] != selected_behavior]
-    behavior_bank = [b for b in behavior_bank if b != selected_behavior]
-    updated_choices = [b[0] for b in behaviors]
-    updated_checkbox_group = [cb for cb in checkbox_group_values if cb != selected_behavior]
-    return gr.Dropdown(choices=updated_choices, value=None, interactive=True), gr.CheckboxGroup(choices=updated_checkbox_group, value=updated_checkbox_group, interactive=True)
-
-def edit_behavior(selected_behavior):
-    for name, (definition, timestamps) in behaviors:
-        if name == selected_behavior:
-            # Return values to populate textboxes
-            return name, definition, timestamps
-    return "", "", ""
-
-
-welcome_message = """
-Hello! I'm your AI assistant.
-I can help you analyze your video sessions following your instructions.
-To get started, please upload a video or add your behaviors to the Behavior Bank using the Behavior Manager.
-"""
-#If you want to tell me about the people in the video, please name them starting from left to right.
-
-css="""
-    body {
-        background-color: #edf1fa; /* offwhite */
-    }
-    .gradio-container {
-        background-color: #edf1fa; /* offwhite */
-    }
-    .column-form .wrap {
-        flex-direction: column;
-    }
-    .sidebar {
-        background: #ffffff; 
-        padding: 10px; 
-        border-right: 1px solid #dee2e6;
-    }
-    .content {
-        padding: 10px;
-    }
-"""
-
-'''
-Gradio Demo
-'''
-with gr.Blocks(theme='base', css=css, title="Soap.AI") as demo:
-    gr.Markdown("# 🤖 AI-Supported SOAP Generation")
-
-    with gr.Row():
-        with gr.Column():
-            video = gr.Video(label="Video", visible=True, height=360, container=True)
-            with gr.Row():
-                with gr.Column(min_width=1, scale=1):
-                    video_upload_button = gr.Button("Analyze Video", variant="primary")
-                with gr.Column(min_width=1, scale=1):
-                    example_video_button = gr.Button("Load Example Video")
-
-            video_upload_button.click(handle_video, inputs=video, outputs=video)
-            example_video_button.click(handle_video, None, outputs=video)
-
-        with gr.Column():
-            chat_section = gr.Group(visible=True)
-            with chat_section:
-                chatbot = gr.Chatbot(elem_id="chatbot", 
-                                     container=True, 
-                                     likeable=True, 
-                                     value=[[None, welcome_message]],
-                                     avatar_images=(None, "./avatar.webp"))
-                with gr.Row():
-                    txt = gr.Textbox(show_label=False, placeholder="Type here!")
-            with gr.Row():
-                send_btn = gr.Button("Send Message", elem_id="send-btn", variant="primary")
-                clear_btn = gr.Button("Clear Chat", elem_id="clear-btn")
-            
-            with gr.Row():
-                behaivor_bank = gr.CheckboxGroup(label="Behavior Bank", 
-                                                 choices=[], 
-                                                 interactive=True,
-                                                 info="A space to store all the behaviors you want to analyze.")
-                open_sidebar_btn = gr.Button("Show Behavior Manager", scale=0)
-                close_sidebar_btn = gr.Button("Hide Behavior Manager", visible=False, scale=0)
-
-            txt.submit(user_input, [txt, chatbot], [txt, chatbot], queue=False).then(
-                bot_response, chatbot, chatbot)
-            send_btn.click(user_input, [txt, chatbot], [txt, chatbot], queue=False).then(
-                bot_response, chatbot, chatbot)
-            clear_btn.click(lambda: None, None, chatbot, queue=False)
-            
-        # Define a sidebar column that is initially hidden
-        with gr.Column(visible=False, min_width=200, scale=0.5, elem_classes="sidebar") as sidebar:
-            behavior_dropdown = gr.Dropdown(label="Behavior Collection",
-                                            choices=behaviors, 
-                                            interactive=True,
-                                            container=True,
-                                            elem_classes="column-form",
-                                            info="Choose a behavior to add to the bank, edit or remove.")
-            with gr.Row():
-                add_toBank_button = gr.Button("Add Behavior to Bank", variant="primary")
-                edit_button = gr.Button("Edit Behavior")
-                delete_button = gr.Button("Remove Behavior")
-
-            with gr.Row():
-                name_input = gr.Textbox(label="Behavior Name", 
-                                        placeholder="(e.g., IBR)",
-                                        info="The name you give to the specific behavior you're tracking or analyzing.")
-                timestamps_input = gr.Textbox(label="Timestamps MM:SS", 
-                                              placeholder="(e.g., (01:15,01:35) )",
-                                              info="The exact times during a session when you saw the behavior. The first two digits represent minutes and the last two digits represent seconds.")
-                definition_input = gr.Textbox(lines=3,
-                                              label="Behavior Definition", 
-                                              placeholder="(e.g., the child's skill in using behavior(s) to elicit aid in obtaining an object, or object related event)",
-                                              info="Provide a clear definition of the behavior.")
-
-            with gr.Row():
-                submit_button = gr.Button("Save Behavior", variant="primary")
-
-    submit_button.click(fn=add_or_update_behavior, 
-                    inputs=[name_input, definition_input, timestamps_input, behavior_dropdown], 
-                    outputs=[behavior_dropdown, behaivor_bank, name_input, definition_input, timestamps_input])
-
-    add_toBank_button.click(fn=add_to_behaivor_bank, 
-                            inputs=[behavior_dropdown, behaivor_bank], 
-                            outputs=[behaivor_bank, behavior_dropdown])
-
-    delete_button.click(fn=delete_behavior, 
-                        inputs=[behavior_dropdown, behaivor_bank], 
-                        outputs=[behavior_dropdown, behaivor_bank])
-
-    edit_button.click(fn=edit_behavior, 
-                    inputs=[behavior_dropdown], 
-                    outputs=[name_input, definition_input, timestamps_input])
-        
-    # Function to open the sidebar
-    open_sidebar_btn.click(lambda: {
-        open_sidebar_btn: gr.Button(visible=False),
-        close_sidebar_btn: gr.Button(visible=True),
-        sidebar: gr.Column(visible=True)
-    }, outputs=[open_sidebar_btn, close_sidebar_btn, sidebar])
-
-    # Function to close the sidebar
-    close_sidebar_btn.click(lambda: {
-        open_sidebar_btn: gr.Button(visible=True),
-        close_sidebar_btn: gr.Button(visible=False),
-        sidebar: gr.Column(visible=False)
-    }, outputs=[open_sidebar_btn, close_sidebar_btn, sidebar])
-
-# Launch the demo
+import gradio as gr
+import plotly.graph_objs as go
+import numpy as np
+import time
+from openai import OpenAI
+import os
+from hardCodedData import *
+from Helper import *
+import cv2
+from moviepy.editor import VideoFileClip
+import time
+import base64
+import whisperx
+import gc 
+from moviepy.editor import VideoFileClip
+from dotenv import load_dotenv
+
+load_dotenv()
+
+'''
+Model Information 
+GPT4o
+'''
+
+import openai
+api_key = os.getenv("OPENAI_API_KEY")
+client = openai.OpenAI(
+    api_key=api_key,
+    base_url="https://openai.gateway.salt-lab.org/v1",
+)
+MODEL="gpt-4o"
+
+# Whisperx config
+device = "cpu" 
+batch_size = 16 # reduce if low on GPU mem
+compute_type = "int8" # change to "int8" if low on GPU mem (may reduce accuracy)
+max_new_tokens = 512  # Example value, adjust as needed
+clip_timestamps = True  # Example value, adjust as needed
+hallucination_silence_threshold = 0.5  # Example value, adjust as needed
+
+# Load the model with the required arguments
+default_asr_options = {
+    "max_new_tokens": max_new_tokens,
+    "clip_timestamps": clip_timestamps,
+    "hallucination_silence_threshold": hallucination_silence_threshold
+}
+
+model = whisperx.load_model("large-v2", device, compute_type=compute_type, **default_asr_options)
+'''
+Video
+'''
+video_file = None
+audio_path=None
+base64Frames = []
+transcript=""
+
+def process_video(video_path, seconds_per_frame=2):
+    global base64Frames, audio_path
+    base_video_path, _ = os.path.splitext(video_path)
+
+    video = cv2.VideoCapture(video_path)
+    total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = video.get(cv2.CAP_PROP_FPS)
+    frames_to_skip = int(fps * seconds_per_frame)
+    curr_frame=0
+
+    while curr_frame < total_frames - 1:
+        video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame)
+        success, frame = video.read()
+        if not success:
+            break
+        _, buffer = cv2.imencode(".jpg", frame)
+        base64Frames.append(base64.b64encode(buffer).decode("utf-8"))
+        curr_frame += frames_to_skip
+    video.release()
+
+    audio_path = "./TEST.mp3"
+    clip = VideoFileClip(video_path)
+    clip.audio.write_audiofile(audio_path, bitrate="32k")
+    clip.audio.close()
+    clip.close()
+    # transcribe_video(audio_path)
+    print(f"Extracted {len(base64Frames)} frames")
+    print(f"Extracted audio to {audio_path}")
+    return base64Frames, audio_path
+
+chat_history = []
+# chat_history.append({
+#             "role": "system",
+#             "content": (
+#                     """
+#                         You are an assistant chatbot for a Speech Language Pathologist (SLP). 
+#                         Your task is to help analyze a provided video of a therapy session and answer questions accurately. 
+#                         Provide timestamps for specific events or behaviors mentioned. Conclude each response with possible follow-up questions.
+
+#                         Follow these steps:
+
+#                         1.	Suggest to the user to ask, “To get started, you can try asking me how many people there are in the video.”
+#                         2.  Detect how many people are in the video.
+#                         2.	Suggest to the user to tell you the names of the people in the video, starting from left to right.
+#                         3.	After receiving the names, respond with, “Ok thank you! Now you can ask me any questions about this video.”
+#                         4.	If the user asks about a behavior, respond with, “My understanding of this behavior is [xxx - AI generated output]. Is this a behavior that you want to track? If it is, please define this behavior and tell me more about it so I can analyze it more accurately according to your practice.”
+#                         5.	If you receive names, confirm that these are the names of the people from left to right.
+#                     """
+#             )
+#         })
+
+def transcribe_video(filename):
+    global transcript
+    if not audio_path:
+        raise ValueError("Audio path is None")
+    print(audio_path)
+    audio = whisperx.load_audio(audio_path)
+    result = model.transcribe(audio, batch_size=batch_size)
+
+    model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
+    result = whisperx.align(result["segments"], model_a, metadata, audio, device, return_char_alignments=False)
+
+
+    hf_auth_token = os.getenv("HF_AUTH_TOKEN")
+    diarize_model = whisperx.DiarizationPipeline(use_auth_token=hf_auth_token, device=device)
+
+    diarize_segments = diarize_model(audio)
+
+    dia_result = whisperx.assign_word_speakers(diarize_segments, result)
+
+    for res in dia_result["segments"]:
+        # transcript += "Speaker: " + str(res.get("speaker", None)) + "\n"
+        transcript += "Dialogue: " + str(res["text"].lstrip()) + "\n"
+        transcript += "start: " + str(int(res["start"])) + "\n"
+        transcript += "end: " + str(int(res["end"])) + "\n"
+        transcript += "\n"
+
+    return transcript
+
+
+def handle_video(video=None):
+    global video_file, base64Frames, audio_path, chat_history, transcript
+    
+    if video is None:
+        # Load example video
+        video = "./TEST.mp4"
+   
+    base64Frames, audio_path = process_video(video_path=video, seconds_per_frame=100)
+    chat_history.append({
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "These are the frames from the video."},
+            *map(lambda x: {"type": "image_url", "image_url": {"url": f'data:image/jpg;base64,{x}', "detail": "low"}}, base64Frames)
+        ]
+    })
+
+    if transcript:
+        chat_history[-1]['content'].append({
+            "type": "text", 
+            "text": f"Also, below is the template of transcript from the video:\n"
+                    "Speaker: <the speaker of the dialogue>\n"
+                    "Dialogue: <the text of the dialogue>\n"
+                    "start: <the starting timestamp of the dialogue in the video in second>\n"
+                    "end: <the ending timestamp of the dialogue in the video in second>\n"
+                    f"Transcription: {transcript}"
+        })
+
+    video_file = video
+    return video_file
+
+'''
+Chatbot
+'''
+
+def new_prompt(prompt):
+    global chat_history, video_file
+    chat_history.append({"role": "user","content": prompt,})
+    MODEL="gpt-4o"
+    # print(chat_history)
+    print(transcript)
+    try:
+        if video_file:
+            # Video exists and is processed
+            response = client.chat.completions.create(model=MODEL,messages=chat_history,temperature=0,)
+        else:
+            # No video uploaded yet
+            response = client.chat.completions.create(model=MODEL,messages=chat_history,temperature=0,)
+
+        # Extract the text content from the response and append it to the chat history
+        assistant_message = response.choices[0].message.content
+        chat_history.append({'role': 'model', 'content': assistant_message})
+        print(assistant_message)
+    except Exception as e:
+        print("Error: ",e)
+        assistant_message = "API rate limit has been reached. Please wait a moment and try again."
+        chat_history.append({'role': 'model', 'content': assistant_message})
+
+    # except google.api_core.exceptions.ResourceExhausted:
+    #     assistant_message = "API rate limit has been reached. Please wait a moment and try again."
+    #     chat_history.append({'role': 'model', 'parts': [assistant_message]})
+    # except Exception as e:
+    #     assistant_message = f"An error occurred: {str(e)}"
+    #     chat_history.append({'role': 'model', 'parts': [assistant_message]})
+
+    return chat_history
+
+def user_input(user_message, history):
+    return "", history + [[user_message, None]]
+
+def bot_response(history):
+    user_message = history[-1][0]
+    updated_history = new_prompt(user_message)
+    assistant_message = updated_history[-1]['content']
+    history[-1][1] = assistant_message
+    yield history
+
+
+'''
+Behaivor box
+'''
+initial_behaviors = [
+    ("Initiating Behavioral Request (IBR)", 
+    ("The child's skill in using behavior(s) to elicit aid in obtaining an object, or object related event", 
+    ["00:10", "00:45", "01:30"])),
+
+    ("Initiating Joint Attention (IJA)", 
+    ("The child's skill in using behavior(s) to initiate shared attention to objects or events.", 
+    ["00:15", "00:50", "01:40"])),
+
+    ("Responding to Joint Attention (RJA)", 
+    ("The child's skill in following the examiner’s line of regard and pointing gestures.", 
+    ["00:20", "01:00", "02:00"])),
+
+    ("Initiating Social Interaction (ISI)", 
+    ("The child's skill at initiating turn-taking sequences and the tendency to tease the tester", 
+    ["00:20", "00:50", "02:00"])),
+
+    ("Responding to Social Interaction (RSI)", 
+    ("The child’s skill in responding to turn-taking interactions initiated by the examiner.", 
+    ["00:20", "01:00", "02:00"]))
+]
+
+behaviors = initial_behaviors
+behavior_bank = []
+
+def add_or_update_behavior(name, definition, timestamps, selected_behavior):
+    global behaviors, behavior_bank
+    if selected_behavior:  # Update existing behavior
+        for i, (old_name, _) in enumerate(behaviors):
+            if old_name == selected_behavior:
+                behaviors[i] = (name, (definition, timestamps))
+                break
+        # Update behavior in the bank if it exists
+        behavior_bank = [name if b == selected_behavior else b for b in behavior_bank]
+    else:  # Add new behavior
+        new_behavior = (name, (definition, timestamps))
+        behaviors.append(new_behavior)
+    choices = [b[0] for b in behaviors]
+    return gr.Dropdown(choices=choices, value=None, interactive=True), gr.CheckboxGroup(choices=behavior_bank, value=behavior_bank, interactive=True), "", "", ""
+
+def add_to_behaivor_bank(selected_behavior, checkbox_group_values):
+    global behavior_bank
+    if selected_behavior and selected_behavior not in checkbox_group_values:
+        checkbox_group_values.append(selected_behavior)
+    behavior_bank = checkbox_group_values
+    return gr.CheckboxGroup(choices=checkbox_group_values, value=checkbox_group_values, interactive=True), gr.Dropdown(value=None,interactive=True)
+
+def delete_behavior(selected_behavior, checkbox_group_values):
+    global behaviors, behavior_bank
+    behaviors = [b for b in behaviors if b[0] != selected_behavior]
+    behavior_bank = [b for b in behavior_bank if b != selected_behavior]
+    updated_choices = [b[0] for b in behaviors]
+    updated_checkbox_group = [cb for cb in checkbox_group_values if cb != selected_behavior]
+    return gr.Dropdown(choices=updated_choices, value=None, interactive=True), gr.CheckboxGroup(choices=updated_checkbox_group, value=updated_checkbox_group, interactive=True)
+
+def edit_behavior(selected_behavior):
+    for name, (definition, timestamps) in behaviors:
+        if name == selected_behavior:
+            # Return values to populate textboxes
+            return name, definition, timestamps
+    return "", "", ""
+
+
+welcome_message = """
+Hello! I'm your AI assistant.
+I can help you analyze your video sessions following your instructions.
+To get started, please upload a video or add your behaviors to the Behavior Bank using the Behavior Manager.
+"""
+#If you want to tell me about the people in the video, please name them starting from left to right.
+
+css="""
+    body {
+        background-color: #edf1fa; /* offwhite */
+    }
+    .gradio-container {
+        background-color: #edf1fa; /* offwhite */
+    }
+    .column-form .wrap {
+        flex-direction: column;
+    }
+    .sidebar {
+        background: #ffffff; 
+        padding: 10px; 
+        border-right: 1px solid #dee2e6;
+    }
+    .content {
+        padding: 10px;
+    }
+"""
+
+'''
+Gradio Demo
+'''
+with gr.Blocks(theme='base', css=css, title="Soap.AI") as demo:
+    gr.Markdown("# 🤖 AI-Supported SOAP Generation")
+
+    with gr.Row():
+        with gr.Column():
+            video = gr.Video(label="Video", visible=True, height=360, container=True)
+            with gr.Row():
+                with gr.Column(min_width=1, scale=1):
+                    video_upload_button = gr.Button("Analyze Video", variant="primary")
+                with gr.Column(min_width=1, scale=1):
+                    example_video_button = gr.Button("Load Example Video")
+
+            video_upload_button.click(handle_video, inputs=video, outputs=video)
+            example_video_button.click(handle_video, None, outputs=video)
+
+        with gr.Column():
+            chat_section = gr.Group(visible=True)
+            with chat_section:
+                chatbot = gr.Chatbot(elem_id="chatbot", 
+                                     container=True, 
+                                     likeable=True, 
+                                     value=[[None, welcome_message]],
+                                     avatar_images=(None, "./avatar.webp"))
+                with gr.Row():
+                    txt = gr.Textbox(show_label=False, placeholder="Type here!")
+            with gr.Row():
+                send_btn = gr.Button("Send Message", elem_id="send-btn", variant="primary")
+                clear_btn = gr.Button("Clear Chat", elem_id="clear-btn")
+            
+            with gr.Row():
+                behaivor_bank = gr.CheckboxGroup(label="Behavior Bank", 
+                                                 choices=[], 
+                                                 interactive=True,
+                                                 info="A space to store all the behaviors you want to analyze.")
+                open_sidebar_btn = gr.Button("Show Behavior Manager", scale=0)
+                close_sidebar_btn = gr.Button("Hide Behavior Manager", visible=False, scale=0)
+
+            txt.submit(user_input, [txt, chatbot], [txt, chatbot], queue=False).then(
+                bot_response, chatbot, chatbot)
+            send_btn.click(user_input, [txt, chatbot], [txt, chatbot], queue=False).then(
+                bot_response, chatbot, chatbot)
+            clear_btn.click(lambda: None, None, chatbot, queue=False)
+            
+        # Define a sidebar column that is initially hidden
+        with gr.Column(visible=False, min_width=200, scale=0.5, elem_classes="sidebar") as sidebar:
+            behavior_dropdown = gr.Dropdown(label="Behavior Collection",
+                                            choices=behaviors, 
+                                            interactive=True,
+                                            container=True,
+                                            elem_classes="column-form",
+                                            info="Choose a behavior to add to the bank, edit or remove.")
+            with gr.Row():
+                add_toBank_button = gr.Button("Add Behavior to Bank", variant="primary")
+                edit_button = gr.Button("Edit Behavior")
+                delete_button = gr.Button("Remove Behavior")
+
+            with gr.Row():
+                name_input = gr.Textbox(label="Behavior Name", 
+                                        placeholder="(e.g., IBR)",
+                                        info="The name you give to the specific behavior you're tracking or analyzing.")
+                timestamps_input = gr.Textbox(label="Timestamps MM:SS", 
+                                              placeholder="(e.g., (01:15,01:35) )",
+                                              info="The exact times during a session when you saw the behavior. The first two digits represent minutes and the last two digits represent seconds.")
+                definition_input = gr.Textbox(lines=3,
+                                              label="Behavior Definition", 
+                                              placeholder="(e.g., the child's skill in using behavior(s) to elicit aid in obtaining an object, or object related event)",
+                                              info="Provide a clear definition of the behavior.")
+
+            with gr.Row():
+                submit_button = gr.Button("Save Behavior", variant="primary")
+
+    submit_button.click(fn=add_or_update_behavior, 
+                    inputs=[name_input, definition_input, timestamps_input, behavior_dropdown], 
+                    outputs=[behavior_dropdown, behaivor_bank, name_input, definition_input, timestamps_input])
+
+    add_toBank_button.click(fn=add_to_behaivor_bank, 
+                            inputs=[behavior_dropdown, behaivor_bank], 
+                            outputs=[behaivor_bank, behavior_dropdown])
+
+    delete_button.click(fn=delete_behavior, 
+                        inputs=[behavior_dropdown, behaivor_bank], 
+                        outputs=[behavior_dropdown, behaivor_bank])
+
+    edit_button.click(fn=edit_behavior, 
+                    inputs=[behavior_dropdown], 
+                    outputs=[name_input, definition_input, timestamps_input])
+        
+    # Function to open the sidebar
+    open_sidebar_btn.click(lambda: {
+        open_sidebar_btn: gr.Button(visible=False),
+        close_sidebar_btn: gr.Button(visible=True),
+        sidebar: gr.Column(visible=True)
+    }, outputs=[open_sidebar_btn, close_sidebar_btn, sidebar])
+
+    # Function to close the sidebar
+    close_sidebar_btn.click(lambda: {
+        open_sidebar_btn: gr.Button(visible=True),
+        close_sidebar_btn: gr.Button(visible=False),
+        sidebar: gr.Column(visible=False)
+    }, outputs=[open_sidebar_btn, close_sidebar_btn, sidebar])
+
+# Launch the demo
 demo.launch(share=True)