Updated README with Information about the FER Model

README.md

@@ -13,8 +13,40 @@ TADBot is small language model that is trained on the <input_data_set_name> data
 - Flask: A library used to create a server for TADBot.
 - Raspberry Pi: A small, low-cost computer used to host Test to Speech and Speech To Text models and TADBot server.
 - FER: A deep learning model used to detect emotions from faces in real-time using a webcam.
+- S2T and T2S: Speech to Text and Text to Speech models used to convert speech to text and text to speech respectively.
 
 # Features
+## FER Model: 
+  - TADBot uses a deep learning model to detect emotions from faces in real-time using a webcam. This allows TADBot to better understand the emotional context of a conversation and provide more appropriate and empathetic responses.
+  - The Data from the FER model is sent to the TADBot server and is used to identify the emotion from the image sent by the client. This information is then used to generate a more appropriate response from the model.
+  - The data is also logged seperatly in a text file which can be accessed by the client to track the change in emotion during the conversation. This can be used to provide insights into the conversation.
+  - The Data is not collected and erased after every conversation adhereing to the doctor-client privacy
+  > HLD for the FER model
+
+  ```mermaid
+    flowchart TD
+  %% User Interface Layer
+  A[Raspberry PI] -->|Sends Image| B[detecfaces.py]
+  B --->|Returns processed data| A
+
+  %%Server
+  subgraph Server 
+  %% Processing Layer
+      B --> |Captured Image| T1[prediction.py]
+      M1[RAFDB trained model] --> |epoch with best acc 92%|B
+      T1-->|Top 3 emotions predicted| B
+
+      %%Model Layer
+      M1
+
+      %% Processing
+      subgraph Processing
+          T1 --> |Send Image|T2[detec_faces]
+          T2 --> |Returns a 224x224 face|T1
+      end
+  end
+  ```   
+## S2T Model and T2S Model:
 
 # How It Works