added Pinecone text retrieval, CLIP text-to-image retrieval, and CLIP image-to-image retrieval

README.md

@@ -1,13 +1,12 @@
 ---
-title: Ai Teaching Assistant
-emoji: 📊
-colorFrom: pink
-colorTo: purple
+title: 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: true
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,350 @@
+import os
+
+import gradio as gr
+import retrieval
+import ray
+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)
+
+NUM_ANSWERS_GENERATED = 3
+
+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()
+
+context1 = None
+context2 = None
+context3 = None
+
+
+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], []
+
+  # 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
+
+
+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(share=True, debug=True)

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