chore: fixing linting errors

.dockerignore

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

.github/workflows/docker-publish.yml

@@ -10,7 +10,7 @@ on:
     - cron: '22 7 * * *'
   release:
     types: created
-    
+
 env:
   # Use docker.io for Docker Hub if empty
   REGISTRY: ghcr.io

.github/workflows/hgf-sync-bulding.yml

@@ -2,10 +2,10 @@ name: Sync building branch to Hugging Face hub
 on:
   push:
     branches: [building]
-  
+
   pull_request:
     branches: [building]
-  
+
   workflow_dispatch:
 
 jobs:

.idea/inspectionProfiles/profiles_settings.xml

@@ -3,4 +3,4 @@
     <option name="USE_PROJECT_PROFILE" value="false" />
     <version value="1.0" />
   </settings>
-</component>
+</component>

.idea/misc.xml

@@ -4,4 +4,4 @@
     <option name="sdkName" value="Python 3.11.6 WSL (Ubuntu): (/home/lennard/.virtualenvs/thesis/bin/python)" />
   </component>
   <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11.6 WSL (Ubuntu): (/home/lennard/.virtualenvs/thesis/bin/python)" project-jdk-type="Python SDK" />
-</project>
+</project>

.idea/modules.xml

@@ -5,4 +5,4 @@
       <module fileurl="file://$PROJECT_DIR$/.idea/thesis-webapp.iml" filepath="$PROJECT_DIR$/.idea/thesis-webapp.iml" />
     </modules>
   </component>
-</project>
+</project>

.idea/thesis-webapp.iml

@@ -1,10 +1,8 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$">
-      <sourceFolder url="file://$MODULE_DIR$/components/iframe/backend" isTestSource="false" />
-    </content>
+    <content url="file://$MODULE_DIR$" />
     <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>
-</module>
+</module>

.idea/vcs.xml

@@ -3,4 +3,4 @@
   <component name="VcsDirectoryMappings">
     <mapping directory="" vcs="Git" />
   </component>
-</project>
+</project>

Dockerfile

@@ -19,4 +19,4 @@ CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
 
 # build and run commands
 ## docker build -t thesis:0.1.6 -f Dockerfile-Light .
-## docker run -d --name thesis -p 8080:8080 thesis:0.1.6-small
+## docker run -d --name thesis -p 8080:8080 thesis:0.1.6-small

LICENSE.md

@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.

README.md

@@ -30,7 +30,7 @@ app_port: 8080
 
 **Author:** [@LennardZuendorf](https://github.com/LennardZuendorf)
 
-**Thesis Supervisor**: [Prof. Dr. Simbeck](https://www.htw-berlin.de/hochschule/personen/person/?eid=9862) 
+**Thesis Supervisor**: [Prof. Dr. Simbeck](https://www.htw-berlin.de/hochschule/personen/person/?eid=9862)
 <br> Second Corrector: [Prof. Dr. Hochstein](https://www.htw-berlin.de/hochschule/personen/person/?eid=10628)
 
 
@@ -42,4 +42,3 @@ See code for in detail credits, work is based on
 
 
 This Project was part of my studies of Business Computing at University of Applied Science for Technology and Business Berlin (HTW Berlin).
-

backend/controller.py

@@ -4,26 +4,35 @@ import gradio as gr
 # internal imports
 from model import mistral, llama2 as llama
 
-def interference(prompt, history,system_prompt, model, xai, ):
 
+def interference(
+    prompt,
+    history,
+    system_prompt,
+    model,
+    xai,
+):
     if system_prompt == "":
-        system_prompt = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe."
-
-    if xai =="None":
+        system_prompt = """You are a helpful, respectful and honest assistant.
+                         Always answer as helpfully as possible, while being safe."""
 
+    if xai == "None":
         match model:
             case "Mistral":
-                prompt_output, history_output = mistral.chat(prompt, history, system_prompt)
+                prompt_output, history_output = mistral.chat(
+                    prompt, history, system_prompt
+                )
             case "LlaMa 2":
-                prompt_output, history_output = llama.chat(prompt, history, 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}\"")
+                gr.Warning("There was an error in the selected model.")
                 return "", "", ""
 
         xai_output = ""
 
-    elif xai == "SHAP" or xai == "Visualizer":
-
+    elif xai in ("SHAP", "Visualizer"):
         match model:
             case "Mistral":
                 prompt_output, history_output, xai_output = "", "", ""
@@ -32,11 +41,11 @@ def interference(prompt, history,system_prompt, model, xai, ):
                 prompt_output, history_output, xai_output = "", "", ""
 
             case _:
-                gr.Warning(f"There was an error in the selected model. It is \"{model}\"")
+                gr.Warning(f'There was an error in the selected model. It is "{model}"')
                 return "", "", ""
 
     else:
-        gr.Warning(f"There was an error in the selected XAI Approach. It is \"{xai}\"")
+        gr.Warning(f'There was an error in the selected XAI Approach. It is "{xai}"')
         return "", "", ""
 
-    return prompt_output, history_output
+    return prompt_output, history_output, xai_output

components/iframe/.gitignore

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

components/iframe/README.md

@@ -1,10 +0,0 @@
-
-# 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

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

components/iframe/backend/gradio_iframe/iframe.py

@@ -1,72 +0,0 @@
-"""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

@@ -1,14 +0,0 @@
-
-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

@@ -1,20 +0,0 @@
-<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

@@ -1,47 +0,0 @@
-<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

@@ -1,917 +0,0 @@
-{
-  "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"
-      }
-    },
-    "node_modules/@ampproject/remapping": {
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-      "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.2.1.tgz",
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-      "peer": true,
-      "dependencies": {
-        "@jridgewell/gen-mapping": "^0.3.0",
-        "@jridgewell/trace-mapping": "^0.3.9"
-      },
-      "engines": {
-        "node": ">=6.0.0"
-      }
-    },
-    "node_modules/@esbuild/android-arm": {
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components/iframe/frontend/package.json

@@ -1,20 +0,0 @@
-{
-  "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

@@ -1,28 +0,0 @@
-<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

@@ -1,42 +0,0 @@
-[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 = "[email protected]" }]
-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"]

entrypoint.sh

@@ -4,4 +4,4 @@
 pip install --no-cache-dir --upgrade -r requirements.txt
 
 # running the fastapi app
-uvicorn main:app --host 0.0.0.0 --port 8080
+uvicorn main:app --host 0.0.0.0 --port 8080

explanation/interpret.py

@@ -1,8 +1,10 @@
+"""
 import seaborn as sns
 import matplotlib.pyplot as plt
 
+
 def interpret_plot(shap_values):
-    values=shap_values[0]
+    values = shap_values[0]
     input_text = shap_values.data[0]
     output_text = shap_values.output_names
 
@@ -11,13 +13,28 @@ def interpret_plot(shap_values):
     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})
+    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.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
+    return plt
+"""

main.py

@@ -1,17 +1,8 @@
-# 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
+# main application file initializing the gradio based ui and calling other
+# external imports
 from fastapi import FastAPI
+import markdown
+import gradio as gr
 
 # internal imports
 from backend.controller import interference
@@ -21,30 +12,36 @@ app = FastAPI()
 
 # different functions to provide frontend abilities
 
+
 # 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:
+    # credit: official python-markdown documentation
+    # (https://python-markdown.github.io/reference/)
+    with open(path, "r", encoding="utf-8") as file:
         text = file.read()
     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.")
+        gr.Info("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}")
 
 
-# ui interface based on Gradio Blocks (see documentation: https://www.gradio.app/docs/interface)
+# ui interface based on Gradio Blocks (see documentation:
+# https://www.gradio.app/docs/interface)
 with gr.Blocks() as ui:
     # header row with markdown based text
     with gr.Row():
@@ -52,48 +49,90 @@ with gr.Blocks() as ui:
             """
             # Thesis Demo - AI Chat Application with XAI
             ### Select between tabs below for the different views.
-            """)
+            """
+        )
     # ChatBot tab used to chat with the AI chatbot
     with gr.Tab("AI ChatBot"):
         with gr.Row():
             gr.Markdown(
                 """
                 ### ChatBot Demo
-                Chat with the AI ChatBot using the textbox below. Manipulate the settings in the row above, including the selection of the model, the system prompt and the XAI method.
-                """)
-        # row with textbox to enter the system prompt, which is handed over to the model at every turn
+                Chat with the AI ChatBot using the textbox below.
+                Manipulate the settings in the row above,
+                including the selection of the model,
+                the system prompt and the XAI method.
+                """
+            )
+        # row with textbox to enter the system prompt, which is handed over to
+        # the model at every turn
         with gr.Row(equal_height=True):
             with gr.Column(scale=3):
-                system_prompt = gr.Textbox(label="System Prompt",info= "Set the models system prompt, dictating how it answers.", placeholder="You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.")
+                system_prompt = gr.Textbox(
+                    label="System Prompt",
+                    info="Set the models system prompt, dictating how it answers.",
+                    placeholder=(
+                        "You are a helpful, respectful and honest assistant. Always"
+                        " answer as helpfully as possible, while being safe."
+                    ),
+                )
             with gr.Column(scale=1):
-                model = gr.Radio(["Mistral", "Llama 2"], label="Model Selection", info="Select Model to use for chat.", value="Mistral", interactive=True, show_label=True)
+                model = gr.Radio(
+                    ["Mistral", "Llama 2"],
+                    label="Model Selection",
+                    info="Select Model to use for chat.",
+                    value="Mistral",
+                    interactive=True,
+                    show_label=True,
+                )
             with gr.Column(scale=1):
-                xai = gr.Radio(["None", "SHAP", "Visualizer"], label="XAI Settings", info="XAI Functionalities to use.", value="None", interactive=True, show_label=True)
+                xai = gr.Radio(
+                    ["None", "SHAP", "Visualizer"],
+                    label="XAI Settings",
+                    info="XAI Functionalities to use.",
+                    value="None",
+                    interactive=True,
+                    show_label=True,
+                )
 
             # calling info functions on inputs for different settings
             system_prompt.submit(system_prompt_info, [system_prompt])
             model.input(model_info, [model])
             xai.input(xai_info, [xai])
 
-        # row with chatbot ui displaying "conversation" with the model (see documentation: https://www.gradio.app/docs/chatbot)
+        # row with chatbot ui displaying "conversation" with the model (see
+        # documentation: https://www.gradio.app/docs/chatbot)
         with gr.Row():
-            chatbot = gr.Chatbot(layout="panel", show_copy_button=True,avatar_images=("./public/human.jpg","./public/bot.jpg"))
+            chatbot = gr.Chatbot(
+                layout="panel",
+                show_copy_button=True,
+                avatar_images=("./public/human.jpg", "./public/bot.jpg"),
+            )
         # row with input textbox
         with gr.Row():
-                user_prompt = gr.Textbox(label="Input Message")
+            user_prompt = gr.Textbox(label="Input Message")
         # row with columns for buttons to submit and clear content
         with gr.Row():
             with gr.Column(scale=1):
-                # default clear button which clearn the given components (see documentation: https://www.gradio.app/docs/clearbutton)
+                # default clear button which clearn the given components (see
+                # documentation: https://www.gradio.app/docs/clearbutton)
                 clear_btn = gr.ClearButton([user_prompt, chatbot])
             with gr.Column(scale=1):
                 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).
-        # 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])
+        # function to trigger the model
+        # when button or the textbox submit function is used.
+        # hands over the information needed for the chat
+        # and information needed to call model and xai function
+        # returns prompt, history and xai
+        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"):
@@ -101,16 +140,19 @@ with gr.Blocks() as ui:
             gr.Markdown(
                 """
                 ### 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.
-                """)
+                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.
+                """
+            )
         with gr.Row():
             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
+    # final row to show legal information - credits, data protection and link
+    # to the LICENSE on GitHub
     with gr.Row():
         with gr.Accordion("Credits, Data Protection and License", open=False):
             gr.Markdown(value=load_md("public/credits_dataprotection_license.md"))
@@ -121,4 +163,5 @@ app = gr.mount_gradio_app(app, ui, path="/")
 # launch function using uvicorn to launch the fastAPI application
 if __name__ == "__main__":
     from uvicorn import run
-    run("main:app", port=8080, reload=True)
+
+    run("main:app", port=8080, reload=True)

model/llama2.py

@@ -1,4 +1,4 @@
-model_temperature = 0.7
-model_max_new_tokens = 100
-model_top_p = 0.95
-model_repetition_penalty = 1.1
+TEMPERATURE = 0.7
+MAX_NEW_TOKENS = 100
+TOP_P = 0.95
+REPETITION_PENALTY = 1.1

model/mistral.py

@@ -1,35 +1,39 @@
-from huggingface_hub import InferenceClient
 import os
+from huggingface_hub import InferenceClient
+
 
 # huggingface token used to load closed off models
 token = os.environ.get("HGFTOKEN")
 
 # interference client created from mistral 7b instruction fine tuned model
-# credit: copied 1:1 from Hugging Face, Inc/ Omar Sanseviero (see https://huggingface.co/spaces/osanseviero/mistral-super-fast/)
-interference = InferenceClient(
-    "mistralai/Mistral-7B-Instruct-v0.1"
-)
+# credit: copied 1:1 from Hugging Face, Inc/ Omar Sanseviero (see
+# https://huggingface.co/spaces/osanseviero/mistral-super-fast/)
+interference = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")
 
-temperature = 0.7
-max_new_tokens = 100
-top_p = 0.95
-repetition_penalty = 1.1
+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,system_prompt):
 
+# chat function - basically the main function calling other functions and
+# returning a response to showcase in chatbot ui
+def chat(prompt, history):
     # creating formatted prompt and calling for an answer from the model
     formatted_prompt = format_prompt(prompt, history)
-    answer=respond(formatted_prompt,system_prompt)
+    answer = respond(formatted_prompt)
 
     # updating the chat history with the new answer
     history.append((prompt, answer))
 
     # returning the chat history to be displayed in the chatbot ui
-    return "",history
+    return "", history
 
-# function to format prompt in a way that is understandable for the text generation model
-# credit: copied 1:1 from Hugging Face, Inc/ Omar Sanseviero (see https://huggingface.co/spaces/osanseviero/mistral-super-fast/)
+
+# function to format prompt for the text generation model
+# credit: copied 1:1 from Hugging Face, Inc/ Omar Sanseviero (see
+# https://huggingface.co/spaces/osanseviero/mistral-super-fast/)
+# TODO: Setup to Use System Prompt
 def format_prompt(message, history):
     prompt = "<s>"
 
@@ -40,27 +44,35 @@ def format_prompt(message, history):
     prompt += f"[INST] {message} [/INST]"
     return prompt
 
+
 # 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):
-    global temperature, max_new_tokens, top_p, repetition_penalty
+# credit: minimally changed from Hugging Face, Inc/ Omar Sanseviero (see
+# https://huggingface.co/spaces/osanseviero/mistral-super-fast/)
+def respond(formatted_prompt):
+    global TEMPERATURE, TOP_P
 
     # setting model temperature and
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
+    TEMPERATURE = float(TEMPERATURE)
+    TEMPERATURE = max(TEMPERATURE, 1e-2)
+
+    TOP_P = float(TOP_P)
 
     # creating model arguments/settings
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=42,
-    )
+    generate_kwargs = {
+        "temperature": TEMPERATURE,
+        "max_new_tokens": MAX_NEW_TOKENS,
+        "top_p": TOP_P,
+        "repetition_penalty": REPETITION_PENALTY,
+        "do_sample": True,
+        "seed": 42,
+    }
 
     # calling for model output and returning it
-    output = interference.text_generation(formatted_prompt, **generate_kwargs, stream=False, details=True, return_full_text=False).generated_text
-    return output
+    output = interference.text_generation(
+        formatted_prompt,
+        **generate_kwargs,
+        stream=False,
+        details=True,
+        return_full_text=False,
+    ).generated_text
+    return output

public/credits_dataprotection_license.md

@@ -15,7 +15,7 @@ For this project, two different models are used. Both are used through Huggingfa
 LlaMa 2 is an open source model by Meta Research. See [offical paper](https://arxiv.org/pdf/2307.09288.pdf) for more information.
 
 - the version used in this project is LlaMa 2 7B Chat HF (HF = special version for huggingface), see [huggingface model hub](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
-- the model is fine-tuned for chat interactions by Meta Research 
+- the model is fine-tuned for chat interactions by Meta Research
 
 ##### Mistral
 Mistral is an open source model by Mistral AI. See [offical paper](https://arxiv.org/pdf/2310.06825.pdf) for more information.
@@ -43,9 +43,9 @@ This application uses a slightly customized version of the bertviz library, whic
 This is a non-commercial project, which does not collect any personal data. The only data collected is the data you enter into the application. This data is only used to generate the explanations and is not stored anywhere.
 However, the application may be hosted with an external service (i.e. Huggingface Spaces), which may collect data. Please refer to the data protection policies of the respective service for more information.
 
-If you use the "flag" feature, the data you enter will be stored in *publicly available* csv file. 
+If you use the "flag" feature, the data you enter will be stored in *publicly available* csv file.
 
 
 # License
 This Product is licensed under the MIT license. See [LICENSE](https://github.com/LennardZuendorf/thesis-webapp/blob/main/LICENSE.md) at GitHub for more information.
-Please credit the original authors of this project (Lennard Zündorf) and the credits listed above if you use this project or parts of it in your own work.
+Please credit the original authors of this project (Lennard Zündorf) and the credits listed above if you use this project or parts of it in your own work.

railway.json

@@ -10,4 +10,4 @@
     "restartPolicyType": "ON_FAILURE",
     "restartPolicyMaxRetries": 10
   }
-}
+}