feat: implementing controller and other updates

.dockerignore

@@ -4,3 +4,5 @@ Dockerfile-Base
 Dockerfile-Light
 entrypoint.sh
 railway.json
+/components/
+/components/*

.idea/thesis-webapp.iml

@@ -1,7 +1,9 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
+    <content url="file://$MODULE_DIR$">
+      <sourceFolder url="file://$MODULE_DIR$/components/iframe/backend" isTestSource="false" />
+    </content>
     <orderEntry type="jdk" jdkName="Python 3.11.6 WSL (Ubuntu): (/home/lennard/.virtualenvs/thesis/bin/python)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>

{controller → backend}/controller.py

@@ -4,14 +4,7 @@ import gradio as gr
 # internal imports
 from model import mistral, llama2 as llama
 
-# default model settings
-model_temperature = 0.7
-model_max_new_tokens = 100
-model_top_p = 0.95
-model_repetition_penalty = 1.1
-
 def interference(prompt, history,system_prompt, model, xai, ):
-    global model_temperature, model_max_new_tokens, model_top_p, model_repetition_penalty
 
     if system_prompt == "":
         system_prompt = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe."
@@ -20,11 +13,9 @@ def interference(prompt, history,system_prompt, model, xai, ):
 
         match model:
             case "Mistral":
-                prompt_output, history_output = mistral.chat(prompt, history, model_temperature, model_max_new_tokens,
-                                                             model_top_p, model_repetition_penalty, system_prompt)
+                prompt_output, history_output = mistral.chat(prompt, history, system_prompt)
             case "LlaMa 2":
-                prompt_output, history_output = llama.chat(prompt, history, model_temperature, model_max_new_tokens,
-                                                           model_top_p, model_repetition_penalty, system_prompt)
+                prompt_output, history_output = llama.chat(prompt, history, system_prompt)
             case _:
                 gr.Warning(f"There was an error in the selected model. It is \"{model}\"")
                 return "", "", ""

components/iframe/.gitignore

@@ -0,0 +1,9 @@
+.eggs/
+dist/
+*.pyc
+__pycache__/
+*.py[cod]
+*$py.class
+__tmp/*
+*.pyi
+node_modules

components/iframe/README.md

@@ -0,0 +1,10 @@
+
+# gradio_iframe
+A Custom Gradio component.
+
+## Example usage
+
+```python
+import gradio as gr
+from gradio_iframe import iFrame
+```

components/iframe/backend/gradio_iframe/__init__.py

@@ -0,0 +1,4 @@
+
+from .iframe import iFrame
+
+__all__ = ['iFrame']

components/iframe/backend/gradio_iframe/iframe.py

@@ -0,0 +1,72 @@
+"""gr.HTML() component."""
+
+from __future__ import annotations
+
+from typing import Any, Callable
+
+from gradio_client.documentation import document, set_documentation_group
+
+from gradio.components.base import Component
+from gradio.events import Events
+
+set_documentation_group("component")
+
+
+@document()
+class iFrame(Component):
+    """
+    Used to display arbitrary iFrame output.
+    Preprocessing: this component does *not* accept input.
+    Postprocessing: expects a valid iFrame {str}.
+
+    Demos: text_analysis
+    Guides: key-features
+    """
+
+    EVENTS = [Events.change]
+
+    def __init__(
+        self,
+        value: str | Callable = "",
+        *,
+        label: str | None = None,
+        every: float | None = None,
+        show_label: bool | None = None,
+        visible: bool = True,
+        elem_id: str | None = None,
+        elem_classes: list[str] | str | None = None,
+        render: bool = True,
+    ):
+        """
+        Parameters:
+            value: Default value. If callable, the function will be called whenever the app loads to set the initial value of the component.
+            label: The label for this component. Is used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
+            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
+            show_label: This parameter has no effect.
+            visible: If False, component will be hidden.
+            elem_id: An optional string that is assigned as the id of this component in the iFrame DOM. Can be used for targeting CSS styles.
+            elem_classes: An optional list of strings that are assigned as the classes of this component in the iFrame DOM. Can be used for targeting CSS styles.
+            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
+        """
+        super().__init__(
+            label=label,
+            every=every,
+            show_label=show_label,
+            visible=visible,
+            elem_id=elem_id,
+            elem_classes=elem_classes,
+            render=render,
+            value=value,
+        )
+
+    def example_inputs(self) -> Any:
+        return "<p>Hello</p>"
+
+    def preprocess(self, payload: str | None) -> str | None:
+        return payload
+
+    def postprocess(self, value: str | None) -> str | None:
+        return value
+
+    def api_info(self) -> dict[str, Any]:
+        return {"type": "string"}

components/iframe/demo/app.py

@@ -0,0 +1,14 @@
+
+import gradio as gr
+from gradio_iframe import iFrame
+
+
+example = iFrame().example_inputs()
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        iFrame(label="Blank"),  # blank component
+        iFrame(value=example, label="Populated"),  # populated component
+
+
+demo.launch()

components/iframe/frontend/Example.svelte

@@ -0,0 +1,20 @@
+<script lang="ts">
+	export let value: string;
+	export let type: "gallery" | "table";
+	export let selected = false;
+</script>
+
+<div
+	class:table={type === "table"}
+	class:gallery={type === "gallery"}
+	class:selected
+	class="prose"
+>
+	{@html value}
+</div>
+
+<style>
+	.gallery {
+		padding: var(--size-2);
+	}
+</style>

components/iframe/frontend/Index.svelte

@@ -0,0 +1,47 @@
+<script lang="ts">
+	import type { Gradio } from "@gradio/utils";
+	import HTML from "./shared/HTML.svelte";
+	import { StatusTracker } from "@gradio/statustracker";
+	import type { LoadingStatus } from "@gradio/statustracker";
+	import { Block } from "@gradio/atoms";
+
+	export let label: string;
+	export let elem_id = "";
+	export let elem_classes: string[] = [];
+	export let visible = true;
+	export let value = "";
+	export let loading_status: LoadingStatus;
+	export let gradio: Gradio<{
+		change: never;
+	}>;
+
+	$: label, gradio.dispatch("change");
+</script>
+
+<Block {visible} {elem_id} {elem_classes} container={false}>
+	<StatusTracker
+		autoscroll={gradio.autoscroll}
+		i18n={gradio.i18n}
+		{...loading_status}
+		variant="center"
+	/>
+	<div class:pending={loading_status?.status === "pending"}>
+		<HTML
+			min_height={loading_status && loading_status?.status !== "complete"}
+			{value}
+			{elem_classes}
+			{visible}
+			on:change={() => gradio.dispatch("change")}
+		/>
+	</div>
+</Block>
+
+<style>
+	div {
+		transition: 150ms;
+	}
+
+	.pending {
+		opacity: 0.2;
+	}
+</style>

components/iframe/frontend/package-lock.json

@@ -0,0 +1,917 @@
+{
+  "name": "gradio_iframe",
+  "version": "0.1.3",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "gradio_iframe",
+      "version": "0.1.3",
+      "license": "ISC",
+      "dependencies": {
+        "@gradio/atoms": "0.3.0",
+        "@gradio/statustracker": "0.4.0",
+        "@gradio/utils": "0.2.0"
+      }
+    },
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+      "peer": true,
+      "dependencies": {
+        "@jridgewell/gen-mapping": "^0.3.0",
+        "@jridgewell/trace-mapping": "^0.3.9"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      }
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+    }
+  }
+}

components/iframe/frontend/package.json

@@ -0,0 +1,20 @@
+{
+  "name": "gradio_iframe",
+  "version": "0.1.3",
+  "description": "Gradio UI packages",
+  "type": "module",
+  "author": "",
+  "license": "ISC",
+  "private": false,
+  "main_changeset": true,
+  "dependencies": {
+    "@gradio/atoms": "0.3.0",
+    "@gradio/statustracker": "0.4.0",
+    "@gradio/utils": "0.2.0"
+  },
+  "exports": {
+    ".": "./Index.svelte",
+    "./example": "./Example.svelte",
+    "./package.json": "./package.json"
+  }
+}

components/iframe/frontend/shared/HTML.svelte

@@ -0,0 +1,28 @@
+<script lang="ts">
+	import { createEventDispatcher } from "svelte";
+	export let elem_classes: string[] = [];
+	export let value: string;
+	export let visible = true;
+	export let min_height = false;
+
+	const dispatch = createEventDispatcher<{ change: undefined }>();
+
+	$: value, dispatch("change");
+</script>
+
+<div
+	class="prose {elem_classes.join(' ')}"
+	class:min={min_height}
+	class:hide={!visible}
+>
+	{@html value}
+</div>
+
+<style>
+	.min {
+		min-height: var(--size-24);
+	}
+	.hide {
+		display: none;
+	}
+</style>

components/iframe/pyproject.toml

@@ -0,0 +1,42 @@
+[build-system]
+requires = [
+  "hatchling",
+  "hatch-requirements-txt",
+  "hatch-fancy-pypi-readme>=22.5.0",
+]
+build-backend = "hatchling.build"
+
+[project]
+name = "gradio_iframe"
+version = "0.0.1"
+description = "A custom component allowing an iFrame component inside Gradio, based on the existing HTML component implementation."
+readme = "README.md"
+license = "MIT"
+requires-python = ">=3.8"
+authors = [{ name = "YOUR NAME", email = "YOUREMAIL@domain.com" }]
+keywords = ["gradio-custom-component", "gradio-template-HTML", "iFrame", "HTML"]
+# Add dependencies here
+dependencies = ["gradio>=4.0,<5.0"]
+classifiers = [
+  'Development Status :: 3 - Alpha',
+  'License :: OSI Approved :: Apache Software License',
+  'Operating System :: OS Independent',
+  'Programming Language :: Python :: 3',
+  'Programming Language :: Python :: 3 :: Only',
+  'Programming Language :: Python :: 3.8',
+  'Programming Language :: Python :: 3.9',
+  'Programming Language :: Python :: 3.10',
+  'Programming Language :: Python :: 3.11',
+  'Topic :: Scientific/Engineering',
+  'Topic :: Scientific/Engineering :: Artificial Intelligence',
+  'Topic :: Scientific/Engineering :: Visualization',
+]
+
+[project.optional-dependencies]
+dev = ["build", "twine"]
+
+[tool.hatch.build]
+artifacts = ["/backend/gradio_iframe/templates", "*.pyi"]
+
+[tool.hatch.build.targets.wheel]
+packages = ["/backend/gradio_iframe"]

explanation/interpret.py

@@ -0,0 +1,23 @@
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+def interpret_plot(shap_values):
+    values=shap_values[0]
+    input_text = shap_values.data[0]
+    output_text = shap_values.output_names
+
+    # Set the seaborn style for better aesthetics
+    sns.set(style="whitegrid")
+    plt.figure(figsize=(20, 10))
+
+    # Create the heatmap with horizontal shape
+    sns.heatmap(shap_values_large, cmap=color_palette, center=0, annot=False, cbar_kws={'fraction': 0.02})
+
+    # Adjust the labels for the larger set
+    plt.xticks(ticks=np.arange(len(output_text_large)) + 0.5, labels=output_text_large, rotation=90)
+    plt.yticks(ticks=np.arange(len(input_text_large)) + 0.5, labels=input_text_large, rotation=0)
+
+    plt.xlabel("Output Tokens")
+    plt.ylabel("Input Tokens")
+
+    return plt

main.py

@@ -1,37 +1,45 @@
 # main application file initializing the gradio based ui and calling other modules
 
+import subprocess
+import sys
+
+def install(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "./components/testinghtml/dist/gradio_testinghtml-0.0.1-py3-none-any.whl"])
+
+from gradio_testinghtml import testingHTML
+
 # external package imports
 import gradio as gr
 import markdown
 from fastapi import FastAPI
-import os
 
 # internal imports
-from model import mistral
-from controller import controller as contr
+from backend.controller import interference
 
 # Global Variables
 app = FastAPI()
 
 # different functions to provide frontend abilities
-def load_md(path):
 
+# function to load markdown files
+def load_md(path):
     # credit: official python-markdown documentation (https://python-markdown.github.io/reference/)
     with open(path, "r") as file:
         text = file.read()
-
-    # return markdown as a string
     return markdown.markdown(text)
 
+# function to display the system prompt info
 def system_prompt_info(sys_prompt_txt):
     gr.Info(f"The system prompt was set to:\n {sys_prompt_txt}")
 
+# function to display the xai info
 def xai_info(xai_radio):
     if xai_radio != "None":
         gr.Info(f"The XAI was set to:\n {xai_radio}")
     else:
         gr.Info(f"No XAI method was selected.")
 
+# function to display the model info
 def model_info(model_radio):
     gr.Info(f"The model was set to:\n {model_radio}")
 
@@ -82,8 +90,10 @@ with gr.Blocks() as ui:
                 submit_btn = gr.Button("Submit", variant="primary")
 
         # two functions performing the same action (triggered the model response), when the button is used or the textbox submit function is used (clicking enter).
-        submit_btn.click(contr.interference, [user_prompt, chatbot, system_prompt, model, xai], [user_prompt, chatbot])
-        user_prompt.submit(contr.interference, [user_prompt, chatbot, system_prompt, model, xai], [user_prompt, chatbot])
+        # hands over the information needed for the chat (prompt, history, and system prompt) and the information needed to call the right model and xai function (model, xai)
+        # returns the prompt and history to be displayed in the chatbot ui
+        submit_btn.click(interference, [user_prompt, chatbot, system_prompt, model, xai], [user_prompt, chatbot])
+        user_prompt.submit(interference, [user_prompt, chatbot, system_prompt, model, xai], [user_prompt, chatbot])
 
     # explanations tab used to provide explanations for a specific conversation
     with gr.Tab("Explanations"):
@@ -93,15 +103,11 @@ with gr.Blocks() as ui:
                 ### Get Explanations for Conversations
                 Using your selected XAI method, you can get explanations for the conversation you had with the AI ChatBot. The explanations are based on the last message you sent to the AI ChatBot.
                 """)
-
-    # visualize dashboard to display global visualization provided by the BERTViz adoption
-    with gr.Tab("Visualize Dashboard"):
         with gr.Row():
-            gr.Markdown(
-                """
-                ### Visualization Dashboard
-                Global Visualization Dashboard adopted from [BERTViz](https://github.com/jessevig/bertviz)
-                """)
+            gr.HTML(load_md("public/explanations.md"))
+        with gr.Row():
+            with gr.Accordion("Full Size Plot of All SHAP Values", open=False):
+                gr.Plot(label="SHAP Values Plot")
 
 
     # final row to show legal information - credits, data protection and link to the LICENSE on GitHub

model/llama2.py

@@ -0,0 +1,4 @@
+model_temperature = 0.7
+model_max_new_tokens = 100
+model_top_p = 0.95
+model_repetition_penalty = 1.1

model/mistral.py

@@ -10,19 +10,17 @@ interference = InferenceClient(
     "mistralai/Mistral-7B-Instruct-v0.1"
 )
 
+temperature = 0.7
+max_new_tokens = 100
+top_p = 0.95
+repetition_penalty = 1.1
+
 # chat function - basically the main function calling other functions and returning a response to showcase in chatbot ui
-def chat (prompt,
-          history,
-          temperature=0.7,
-          max_new_tokens = 320,
-          top_p = 0.95,
-          repetition_penalty = 1.1,
-          system_prompt = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe."
-):
+def chat (prompt,history,system_prompt):
 
     # creating formatted prompt and calling for an answer from the model
     formatted_prompt = format_prompt(prompt, history)
-    answer=respond(formatted_prompt,system_prompt, temperature, max_new_tokens, top_p, repetition_penalty)
+    answer=respond(formatted_prompt,system_prompt)
 
     # updating the chat history with the new answer
     history.append((prompt, answer))
@@ -44,7 +42,8 @@ def format_prompt(message, history):
 
 # function to get the response
 # credit: minimally changed from Hugging Face, Inc/ Omar Sanseviero (see https://huggingface.co/spaces/osanseviero/mistral-super-fast/)
-def respond(formatted_prompt, system_prompt, temperature, max_new_tokens, top_p, repetition_penalty):
+def respond(formatted_prompt, system_prompt):
+    global temperature, max_new_tokens, top_p, repetition_penalty
 
     # setting model temperature and
     temperature = float(temperature)