initial commit, fixing chat history

README.md

@@ -1,13 +1,12 @@
 ---
-title: Ai Teaching Assistant Beta
-emoji: 🏢
-colorFrom: green
-colorTo: red
+title: [BETA] AI Teaching Assistant
+emoji: 🛠️
+colorFrom: blue
+colorTo: gray
 sdk: gradio
-sdk_version: 3.23.0
+sdk_version: 3.20.1
 app_file: app.py
-pinned: false
-license: mit
+pinned: False
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,346 @@
+import os
+
+import gradio as gr
+import retrieval
+# UNCOMMENT ONLY WHEN RUNNING LOCALLY (not on Spaces)
+# from dotenv import load_dotenv
+from text_generation import Client, InferenceAPIClient
+
+# load API keys from globally-availabe .env file
+# SECRETS_FILEPATH = "/mnt/project/chatbotai/huggingface_cache/internal_api_keys.env"
+# load_dotenv(dotenv_path=SECRETS_FILEPATH, override=True)
+
+openchat_preprompt = (
+    "\n<human>: Hi!\n<bot>: My name is Bot, model version is 0.15, part of an open-source kit for "
+    "fine-tuning new bots! I was created by Together, LAION, and Ontocord.ai and the open-source "
+    "community. I am not human, not evil and not alive, and thus have no thoughts and feelings, "
+    "but I am programmed to be helpful, polite, honest, and friendly. I'm really smart at answering electrical engineering questions.\n")
+
+# LOAD MODELS
+ta = retrieval.Retrieval()
+NUM_ANSWERS_GENERATED = 3
+
+
+def clip_img_search(img):
+  if img is None:
+    return []
+  else:
+    return ta.reverse_img_search(img)
+
+
+def get_client(model: str):
+  if model == "Rallio67/joi2_20Be_instruct_alpha":
+    return Client(os.getenv("JOI_API_URL"))
+  if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
+    return Client(os.getenv("OPENCHAT_API_URL"))
+  return InferenceAPIClient(model, token=os.getenv("HF_TOKEN", None))
+
+
+def get_usernames(model: str):
+  """
+    Returns:
+        (str, str, str, str): pre-prompt, username, bot name, separator
+    """
+  if model == "OpenAssistant/oasst-sft-1-pythia-12b":
+    return "", "<|prompter|>", "<|assistant|>", "<|endoftext|>"
+  if model == "Rallio67/joi2_20Be_instruct_alpha":
+    return "", "User: ", "Joi: ", "\n\n"
+  if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
+    return openchat_preprompt, "<human>: ", "<bot>: ", "\n"
+  return "", "User: ", "Assistant: ", "\n"
+
+
+def predict(
+    model: str,
+    inputs: str,
+    typical_p: float,
+    top_p: float,
+    temperature: float,
+    top_k: int,
+    repetition_penalty: float,
+    watermark: bool,
+    chatbot,
+    history,
+):
+  client = get_client(model)
+  preprompt, user_name, assistant_name, sep = get_usernames(model)
+
+  history.append(inputs)
+
+  past = []
+  for data in chatbot:
+    user_data, model_data = data
+
+    if not user_data.startswith(user_name):
+      user_data = user_name + user_data
+    if not model_data.startswith(sep + assistant_name):
+      model_data = sep + assistant_name + model_data
+
+    past.append(user_data + model_data.rstrip() + sep)
+
+  if not inputs.startswith(user_name):
+    inputs = user_name + inputs
+
+  total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip()
+
+  partial_words = ""
+
+  if model == "OpenAssistant/oasst-sft-1-pythia-12b":
+    iterator = client.generate_stream(
+        total_inputs,
+        typical_p=typical_p,
+        truncate=1000,
+        watermark=watermark,
+        max_new_tokens=500,
+    )
+  else:
+    iterator = client.generate_stream(
+        total_inputs,
+        top_p=top_p if top_p < 1.0 else None,
+        top_k=top_k,
+        truncate=1000,
+        repetition_penalty=repetition_penalty,
+        watermark=watermark,
+        temperature=temperature,
+        max_new_tokens=500,
+        stop_sequences=[user_name.rstrip(), assistant_name.rstrip()],
+    )
+
+  for i, response in enumerate(iterator):
+    if response.token.special:
+      continue
+
+    partial_words = partial_words + response.token.text
+    if partial_words.endswith(user_name.rstrip()):
+      partial_words = partial_words.rstrip(user_name.rstrip())
+    if partial_words.endswith(assistant_name.rstrip()):
+      partial_words = partial_words.rstrip(assistant_name.rstrip())
+
+    if i == 0:
+      history.append(" " + partial_words)
+    elif response.token.text not in user_name:
+      history[-1] = partial_words
+
+    chat = [(history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2)]
+    yield chat, history, None, None, None, []
+
+  # Pinecone context retrieval
+  top_context_list = ta.retrieve_contexts_from_pinecone(user_question=inputs, topk=NUM_ANSWERS_GENERATED)
+  # yield chat, history, top_context_list[0], top_context_list[1], top_context_list[2], []
+  yield None, None, top_context_list[0], top_context_list[1], top_context_list[2], []
+
+  # run CLIP
+  images_list = ta.clip_text_to_image(inputs)
+  # yield chat, history, top_context_list[0], top_context_list[1], top_context_list[2], images_list
+  yield None, None, top_context_list[0], top_context_list[1], top_context_list[2], images_list
+
+
+def reset_textbox():
+  return gr.update(value="")
+
+
+def radio_on_change(
+    value: str,
+    disclaimer,
+    typical_p,
+    top_p,
+    top_k,
+    temperature,
+    repetition_penalty,
+    watermark,
+):
+  if value == "OpenAssistant/oasst-sft-1-pythia-12b":
+    typical_p = typical_p.update(value=0.2, visible=True)
+    top_p = top_p.update(visible=False)
+    top_k = top_k.update(visible=False)
+    temperature = temperature.update(visible=False)
+    disclaimer = disclaimer.update(visible=False)
+    repetition_penalty = repetition_penalty.update(visible=False)
+    watermark = watermark.update(False)
+  elif value == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
+    typical_p = typical_p.update(visible=False)
+    top_p = top_p.update(value=0.25, visible=True)
+    top_k = top_k.update(value=50, visible=True)
+    temperature = temperature.update(value=0.6, visible=True)
+    repetition_penalty = repetition_penalty.update(value=1.01, visible=True)
+    watermark = watermark.update(False)
+    disclaimer = disclaimer.update(visible=True)
+  else:
+    typical_p = typical_p.update(visible=False)
+    top_p = top_p.update(value=0.95, visible=True)
+    top_k = top_k.update(value=4, visible=True)
+    temperature = temperature.update(value=0.5, visible=True)
+    repetition_penalty = repetition_penalty.update(value=1.03, visible=True)
+    watermark = watermark.update(True)
+    disclaimer = disclaimer.update(visible=False)
+  return (
+      disclaimer,
+      typical_p,
+      top_p,
+      top_k,
+      temperature,
+      repetition_penalty,
+      watermark,
+  )
+
+
+title = """<h1 align="center">🔥Teaching Assistant Chatbot"""
+description = """
+"""
+
+openchat_disclaimer = """
+<div align="center">Checkout the official <a href=https://huggingface.co/spaces/togethercomputer/OpenChatKit>OpenChatKit feedback app</a> for the full experience.</div>
+"""
+
+with gr.Blocks(css="""#col_container {margin-left: auto; margin-right: auto;}
+                #chatbot {height: 520px; overflow: auto;}""") as demo:
+  gr.HTML(title)
+  with gr.Row():
+    with gr.Accordion("Model choices", open=False, visible=True):
+      model = gr.Radio(
+          value="OpenAssistant/oasst-sft-1-pythia-12b",
+          choices=[
+              "OpenAssistant/oasst-sft-1-pythia-12b",
+              # "togethercomputer/GPT-NeoXT-Chat-Base-20B",
+              "Rallio67/joi2_20Be_instruct_alpha",
+              "google/flan-t5-xxl",
+              "google/flan-ul2",
+              "bigscience/bloom",
+              "bigscience/bloomz",
+              "EleutherAI/gpt-neox-20b",
+          ],
+          label="",
+          interactive=True,
+      )
+  # with gr.Row():
+  #     with gr.Column():
+  #         use_gpt3_checkbox = gr.Checkbox(label="Include GPT-3 (paid)?")
+  #     with gr.Column():
+  #         use_equation_checkbox = gr.Checkbox(label="Prioritize equations?")
+  state = gr.State([])
+
+  with gr.Row():
+    with gr.Column():
+      chatbot = gr.Chatbot(elem_id="chatbot")
+      inputs = gr.Textbox(placeholder="Ask an Electrical Engineering question!", label="Send a message...")
+      examples = gr.Examples(
+          examples=[
+              "What is a Finite State Machine?",
+              "How do you design a functional a Two-Bit Gray Code Counter?",
+              "How can we compare an 8-bit 2's complement number to the value -1 using AND, OR, and NOT?",
+              "What does the uninterrupted counting cycle label mean?",
+          ],
+          inputs=[inputs],
+          outputs=[],
+      )
+  gr.Markdown("## Relevant Textbook Passages & Lecture Transcripts")
+  with gr.Row():
+    with gr.Column():
+      context1 = gr.Textbox(label="Context 1")
+    with gr.Column():
+      context2 = gr.Textbox(label="Context 2")
+    with gr.Column():
+      context3 = gr.Textbox(label="Context 3")
+
+  gr.Markdown("## Relevant Lecture Slides")
+  with gr.Row():
+    with gr.Column(scale=2.6):
+      lec_gallery = gr.Gallery(label="Lecture images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
+    with gr.Column(scale=1):
+      inp_image = gr.Image(type="pil", label="Reverse Image Search (optional)", shape=(224, 398))
+
+  inp_image.change(fn=clip_img_search, inputs=inp_image, outputs=lec_gallery, scroll_to_output=True)
+  disclaimer = gr.Markdown(openchat_disclaimer, visible=False)
+  # state = gr.State([])
+
+  with gr.Row():
+    with gr.Accordion("Parameters", open=False, visible=True):
+      typical_p = gr.Slider(
+          minimum=-0,
+          maximum=1.0,
+          value=0.2,
+          step=0.05,
+          interactive=True,
+          label="Typical P mass",
+      )
+      top_p = gr.Slider(
+          minimum=-0,
+          maximum=1.0,
+          value=0.25,
+          step=0.05,
+          interactive=True,
+          label="Top-p (nucleus sampling)",
+          visible=False,
+      )
+      temperature = gr.Slider(
+          minimum=-0,
+          maximum=5.0,
+          value=0.6,
+          step=0.1,
+          interactive=True,
+          label="Temperature",
+          visible=False,
+      )
+      top_k = gr.Slider(
+          minimum=1,
+          maximum=50,
+          value=50,
+          step=1,
+          interactive=True,
+          label="Top-k",
+          visible=False,
+      )
+      repetition_penalty = gr.Slider(
+          minimum=0.1,
+          maximum=3.0,
+          value=1.03,
+          step=0.01,
+          interactive=True,
+          label="Repetition Penalty",
+          visible=False,
+      )
+      watermark = gr.Checkbox(value=False, label="Text watermarking")
+
+  model.change(
+      lambda value: radio_on_change(
+          value,
+          disclaimer,
+          typical_p,
+          top_p,
+          top_k,
+          temperature,
+          repetition_penalty,
+          watermark,
+      ),
+      inputs=model,
+      outputs=[
+          disclaimer,
+          typical_p,
+          top_p,
+          top_k,
+          temperature,
+          repetition_penalty,
+          watermark,
+      ],
+  )
+
+  inputs.submit(
+      predict,
+      [
+          model,
+          inputs,
+          typical_p,
+          top_p,
+          temperature,
+          top_k,
+          repetition_penalty,
+          watermark,
+          chatbot,
+          state,
+      ],
+      [chatbot, state, context1, context2, context3, lec_gallery],
+  )
+  inputs.submit(reset_textbox, [], [inputs])
+
+  gr.Markdown(description)
+  demo.queue(concurrency_count=16).launch(debug=True)

clip_for_ppts.py

@@ -0,0 +1,158 @@
+import os
+
+import clip
+import torch
+from PIL import Image
+
+# import sys
+# from pptx import Presentation
+# from pptx.enum.shapes import MSO_SHAPE_TYPE
+# import time
+
+
+class ClipImage:
+
+  def __init__(self, path_of_ppt_folders, path_to_save_image_features, mode='image', device='cuda'):
+    """
+    :param input_image_path: path of the input image (mode = 'image') or the actual text to be searched (mode='text')
+    :param path_of_ppt_folders: path of the folder containing all the ppt folders
+    :param path_to_save_image_features: path to save the image features
+    :param mode: 'image' or 'text' based on the type of input
+    :param device: device to run the model on
+    """
+    print("HEADS UPP  --  ALWAYS using CPU for this 'spaces' version of the project. Otherwise we get FP32/16 conflicts.")
+    # device = "cuda" if torch.cuda.is_available() else "cpu"
+    device = "cpu"
+    # Path
+    directory = 'input_features'
+    path = os.path.join(path_to_save_image_features, directory)
+    if not os.path.exists(path):
+      # Create the directory
+      os.mkdir(path)
+      print("Directory '% s' created" % directory)
+
+    self.res = []
+    if not os.path.isdir(path_of_ppt_folders):
+      raise TypeError(f"{path_of_ppt_folders} is not a directory. Please only enter a directory")
+
+    # if mode == 'image' and not os.path.exists(input_image_path):
+    #     raise FileNotFoundError(f"{input_image_path} does not exist.")
+    if not os.path.exists(path_to_save_image_features) or not os.path.isdir(path_to_save_image_features):
+      raise FileNotFoundError(f"{path_to_save_image_features} is not a directory or doesn't exist.")
+    self.mode = mode
+    self.path_of_ppt_folders = path_of_ppt_folders
+    self.path_to_save_image_features = path_to_save_image_features
+    self.device = device
+
+    # consider ViT-L/14 should be the best one
+    self.model, self.preprocess = clip.load('ViT-B/32', self.device)
+
+    #print("👉 RUNNING CLIP'S ONE-TIME ENCODING STEP... will be slow the first time, and hopefully only the first time.")
+    # passing in an image as a cheap hack, to make one funciton work for initial embedding.
+    #self.calculate_similarity('/home/rsalvi/chatbotai/rohan/ai-teaching-assistant-uiuc/lecture_slides/001/Slide1.jpeg')
+    #print("🔥 DONE with CLIP's ONE TIME ENCODING")
+
+  def text_to_image_search(self, search_text: str, top_k_to_return: int = 4):
+    """ Written after the fact by kastan, so that we don't have to call init every time. """
+    assert type(search_text) == str, f"Must provide a single string, instead I got type {type(search_text)}"
+    # self.create_input_features(search_text, mode='text')
+    self.mode = 'text'
+    return self.calculate_similarity(search_text, top_k_to_return)
+
+  # TODO: WIP.
+  def image_to_images_search(self, input_image, top_k_to_return: int = 4):
+    """ Written after the fact by kastan, so that we don't have to call init every time. """
+    self.mode = 'image'
+    return self.calculate_similarity(input_image, top_k_to_return)
+
+  def create_input_features(self, input_text_or_img):
+    if self.mode == 'image':
+      # Load the image
+      #input_image = Image.open(input_text_or_img) # Not needed as image comes from gradio in PIL format
+      # Preprocess the image
+      input_arr = torch.cat([self.preprocess(input_text_or_img).unsqueeze(0)]).to(self.device)
+
+    elif self.mode == 'text':
+      # Preprocess the text
+      input_arr = torch.cat([clip.tokenize(f"{input_text_or_img}")]).to(self.device)
+
+    # Encode the image or text
+    with torch.no_grad():
+      if self.mode == 'image':
+        input_features = self.model.encode_image(input_arr)
+      elif self.mode == 'text':
+        input_features = self.model.encode_text(input_arr)
+    input_features /= input_features.norm(dim=-1, keepdim=True)
+    return input_features
+
+  def new_most_similar_slide_file(self, top_k: int):
+    # Sort the results
+    ans = sorted(self.res, key=lambda x: x[2], reverse=True)
+    return ans[:top_k]
+
+  def calculate_similarity(self, input_text_or_img, topk_val: int = 4):
+    ## Similarities across folders
+    self.res = []
+    all_similarities = []
+    slide_numbers = []
+    # Create the input features
+    input_features = self.create_input_features(input_text_or_img)
+
+    # Iterate through all the folders
+    ppts = list(os.listdir(self.path_of_ppt_folders))
+    #start_time = time.monotonic()
+    for i in ppts:
+      # Get the path of the folder containing the ppt images
+      imgs = list(os.listdir(os.path.join(self.path_of_ppt_folders, i)))
+      slide_numbers.append(imgs)
+      # Iterate through all the images and preprocess them
+
+      # Check if the preprocessed file exists and load it
+      img_flag = os.path.exists(self.path_to_save_image_features + '/input_features' + "/slides_" + i + "_tensor.pt")
+      if img_flag:
+        image_features = torch.load(self.path_to_save_image_features + '/input_features' + "/slides_" + i + "_tensor.pt",
+                                    map_location=self.device)
+      else:
+        # Encode the images and save the encoding
+        with torch.no_grad():
+          image_input = torch.cat([
+              self.preprocess(Image.open(os.path.join(self.path_of_ppt_folders, i, image))).unsqueeze(0) for image in imgs
+          ]).to(self.device)
+          image_features = self.model.encode_image(image_input)
+        image_features /= image_features.norm(dim=-1, keepdim=True)
+        torch.save(image_features, self.path_to_save_image_features + '/input_features' + "/slides_" + i + "_tensor.pt")
+        print("Saved the image features (for faster future loading) to: ", self.path_to_save_image_features + "/slides_" + i + "_tensor.pt")
+
+      # Calculate the similarity between the input image and the images in the folder
+
+    # TODO: THIS REQUIRES REFACTOR. We're only looking in a SINGLE FOLDER. need to APPEND to similarity.
+      if self.mode == 'image':
+        similarity = (100.0 * input_features @ image_features.T).softmax(dim=-1)
+        all_similarities.append((i, similarity))
+      elif self.mode == 'text':
+        similarity = (100.0 * input_features @ image_features.T).softmax(dim=-1)
+        all_similarities.append((i, similarity))
+
+    ## Looking over all the folders
+    similarity_results = []
+
+    for j in range(0, len(all_similarities)):
+      folder_name = all_similarities[j][0]
+      folder_values = all_similarities[j][1][0]
+      for i in range(0, len(folder_values)):
+        self.res.append((folder_name, slide_numbers[j][i], folder_values[i]))
+
+    #print(self.res)
+
+    return self.new_most_similar_slide_file(topk_val)
+    # Return the sorted results
+
+
+# if __name__ == "__main__":
+
+#     demo = ClipImage('/home/rsalvi/chatbotai/rohan/ai-teaching-assistant-uiuc/lecture_slides','/home/rsalvi/chatbotai/rohan/ai-teaching-assistant-uiuc')
+#     #op = demo.image_to_images_search('/home/rsalvi/chatbotai/rohan/ai-teaching-assistant-uiuc/lecture_slides/01c/Slide5.jpeg')
+#     op = demo.text_to_image_search("Unsigned Bit Pattern")
+#     print(op)
+#     op = demo.text_to_image_search("Graycode")
+#     print(op)

gpu_memory_utils.py

@@ -0,0 +1,57 @@
+import GPUtil  # pip install gputil
+
+
+def get_gpu_ids_with_sufficient_memory(memory_requirement_GB):
+  '''
+    Returns the MINIMAL SET of GPU IDs that, combined, have at least `memory_requirement` MB of free memory.
+    You will need to use all returned GPU IDs to get the desired memory requirement.
+    It returns lower IDs first [0, 1, ...]
+    
+    If `memory_requirement` is 0, returns all available GPUs.
+    If `memory_requirement` is not available, returns an empty list.
+    '''
+  memory_requirement_MB = float(memory_requirement_GB * 1024)
+  GPUs = sorted(GPUtil.getGPUs(), key=lambda x: x.memoryFree, reverse=True)
+  total_memory = sum(gpu.memoryFree for gpu in GPUs)
+  if memory_requirement_MB > total_memory:
+    return []
+  GPU_IDs = []
+  for gpu in GPUs:
+    if memory_requirement_MB <= 0:
+      break
+    GPU_IDs.append(gpu.id)
+    memory_requirement_MB -= gpu.memoryFree
+  return GPU_IDs
+
+
+def get_device_with_most_free_memory():
+  '''
+    Returns the GPU ID of the GPU with the most free memory.
+    '''
+  GPUs = GPUtil.getGPUs()
+  return sorted(GPUs, key=lambda x: x.memoryFree, reverse=True)[0].id
+
+
+def get_free_memory_dict(leave_extra_memory_unused_GiB: float = 2, leave_extra_memory_unused_gpu0_GiB: float = 3):
+  '''
+  Returns a dictionary of GPU IDs and their free memory, in MiB. 
+  Compatible with huggingface Accelerate formatting: `max_memory=get_free_memory_dict()`
+  
+  Accelerate seems to use more memory than we give it, so we default to telling Accelerate we have 2 GiB less than we actually do.
+  
+  Example output: 
+  {0: '24753MiB', 1: '26223MiB', 2: '25603MiB', 3: '9044MiB'}
+  '''
+  GPUs = GPUtil.getGPUs()
+  memory_map = {gpu.id: int(round(gpu.memoryFree)) for gpu in GPUs}
+  if leave_extra_memory_unused_GiB > 0:
+    for device_id, memory_MiB in memory_map.items():
+      memory_map[device_id] = memory_MiB - (leave_extra_memory_unused_GiB * 1024)
+  if leave_extra_memory_unused_gpu0_GiB > 0 and 0 in memory_map:
+    memory_map[0] = memory_map[0] - (leave_extra_memory_unused_gpu0_GiB * 1024)
+
+  # format to Accelerate's liking
+  for device_id, memory_MiB in memory_map.items():
+    memory_map[device_id] = f"{int(round(memory_MiB))}MiB"
+
+  return memory_map

input_features/slides_001_tensor.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29d55bc24135d9c7a840999d704b182524d2a414ef96c648f1d619d790a399a2
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