feat: finally actual final changes

README.md

@@ -51,7 +51,7 @@ This Project was part of my studies of Business Computing at the University of A
 
 1. Clone the repository using git or GitHub cli.
 2. Start the (virtual) environment.
-3. Set the environment variable "HOSTING", i.e. like this `export HOSTING=local USER=admin PW=test`, see [fastAPI Docu](https://fastapi.tiangolo.com/advanced/settings/)
+3. Set the environment variable "HOSTING", i.e. like this `export HOSTING=local`, see [fastAPI Docu](https://fastapi.tiangolo.com/advanced/settings/)
 3. Install the requirements using `pip install -r requirements.txt`
 4. Run the app using `uvicorn main:app`. You can add `--reload` to enable hot reloading. The app will be available at `localhost:8000`.
 
@@ -60,14 +60,14 @@ This Project was part of my studies of Business Computing at the University of A
 
 1. Clone the repository using git or GitHub cli.
 2. Build the docker image using `docker build -t thesis-webapp -f Dockerfile . .`, the command commented in the docker file or the command referenced by your hosting service.
-3. Run the docker image using `docker run --name thesis-webapp -e HOSTING=local USER=admin PW=test -p 8080:8080 thesis-webapp`, the command commented in the docker file or the command referenced by your hosting service.
+3. Run the docker image using `docker run --name thesis-webapp -e HOSTING=local -p 8080:8080 thesis-webapp`, the command commented in the docker file or the command referenced by your hosting service.
 4. The app will be available at `localhost:8080`. If you are using a hosting service, the port may be different.
 
 ### 🐳 Docker Image :
 (This assumes you have set up docker desktop or are using a hosting service able to handle Docker images.)
 
 1. Pull the docker image from ghcr using `docker pull ghcr.io/LennardZuendorf/thesis-webapp:1.3.1`.
-2. Run the docker image in terminal using `docker run --name thesis-webapp -e HOSTING=local USER=admin PW=test -p 8080:8080 lennardzuendorf/thesis-webapp::1.3.1`, the command commented in the docker file or the command referenced by your hosting service.
+2. Run the docker image in terminal using `docker run --name thesis-webapp -e  PW=test -p 8080:8080 lennardzuendorf/thesis-webapp::1.3.1`, the command commented in the docker file or the command referenced by your hosting service.
 3. The app will be available at `localhost:8080`. If you are using a hosting service, the port may be different.
 
 ## 📝 License and Credits:
@@ -75,7 +75,7 @@ This Project was part of my studies of Business Computing at the University of A
 This project is licensed under the MIT License, see [LICENSE](LICENSE.md) for more information. Please cite this project, it's author and my university if you use it in your work.
 
 - Title: Building an Interpretable Natural Language AI Tool based on Transformer Models and approaches of Explainable AI.
-- Date: 2024-01-27
+- Date: 2024-02-14
 - Author: Lennard Zündorf
 - University: HTW Berlin
 

explanation/plotting.py

@@ -42,13 +42,14 @@ def plot_seq(seq_values: list, method: str = ""):
             },  # White background
         )
 
+    # setting plot properties, labels, and title
     plt.axhline(0, color="black", linewidth=1)
     plt.title(f"Input Token Attribution with {method}")
     plt.xlabel("Input Tokens", labelpad=0.5)
     plt.ylabel("Attribution")
     plt.xticks(x_positions, tokens, rotation=45)
 
-    # Adjust y-axis limits to ensure there's enough space for labels
+    # adjusting y-axis limits to ensure there's enough space for labels
     y_min, y_max = plt.ylim()
     y_range = y_max - y_min
     plt.ylim(y_min - 0.1 * y_range, y_max + 0.1 * y_range)

main.py

@@ -147,7 +147,7 @@ with gr.Blocks(
                     )
 
                 # calling info functions on inputs/submits for different settings
-                system_prompt.change(system_prompt_info, [system_prompt])
+                system_prompt.input(system_prompt_info, [system_prompt])
                 xai_selection.change(xai_info, [xai_selection])
                 model_selection.change(model_info, [model_selection])
 

model/mistral.py

@@ -34,7 +34,7 @@ TOKENIZER = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
 CONFIG = GenerationConfig.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
 base_config_dict = {
     "temperature": 1,
-    "max_new_tokens": 100,
+    "max_new_tokens": 64,
     "top_p": 0.9,
     "repetition_penalty": 1.2,
     "do_sample": True,

public/about.md

@@ -12,7 +12,7 @@ This research tackles the rise of LLM based applications such a chatbots and exp
 
 ## Implementation
 
-This project is an implementation of PartitionSHAP into GODEL by Microsoft - [GODEL Model](https://huggingface.co/microsoft/GODEL-v1_1-large-seq2seq) which is a generative seq2seq transformer fine-tuned for goal directed dialog. It supports context and knowledge base inputs.
+This project
 
 The UI is build with Gradio, utilizing some custom components and FastAPI.