First commit

.gitignore

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+*.pyc

README.md

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 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Dumbot Project
+
+## Overview
+Dumbot is a Python-based project designed to provide a simple interface for users to interact with a chatbot. The chatbot is built using the Streamlit library and Facebook blenderbot model.
+
+## Contents
+- `functions.py`: Contains various functions used in the project.
+- `config.py`: Configuration settings for the project.
+- `requirements.txt`: Lists the dependencies required to run the project.
+- `app.py`: The main application file.
+- `.gitignore`: Specifies files and directories to be ignored by git.
+- `.gitattributes`: Configuration for git attributes.
+
+## Getting Started
+
+### Prerequisites
+Ensure you have the following installed:
+- Python 3.x
+- pip (Python package installer)
+
+### Installation
+1. Clone the repository:
+2. Navigate to the project directory:
+    ```sh
+    cd dumbot
+    ```
+3. Install the required dependencies:
+    ```sh
+    pip install -r requirements.txt
+    ```
+
+### Running the Application
+Run the main application file:
+```sh
+streamlit run app.py
+```

app.py

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+from functions import *
+
+# set the title
+st.sidebar.title(DASHBOARD_TITLE)
+info_section = st.empty()
+
+# add an explanation of what is NER and why it is important for medical tasks
+st.sidebar.markdown(
+    f"""
+    Facebook blenderbot is a family of conversational models that are trained on a large dataset of conversations and can generate limited, yet sometimes coherent responses.
+
+    For this project, we are using the 400M distill version of the model. This model is smaller and faster than the original model, but it may not be as accurate. I have used Streamlit to create a simple chatbot interface that allows you to chat with the model and demonstrate how easy it is to use these models for conversational AI tasks.
+
+    Have fun, but don't expect too much from the model! It is a little dumb sometimes.
+    
+    Model used: [{MODEL_PATH}]({MODEL_LINK})
+    """
+)
+
+
+first_assistant_message = "Hello! I am a dumb bot. What is your dumb question?"
+
+# clear conversation
+if st.sidebar.button("Clear conversation"):
+    chat_history = [{'user':'assistant', 'content':first_assistant_message}]
+    st.session_state['chat_history'] = chat_history
+    st.rerun()
+
+# Get the chat history
+if "chat_history" not in st.session_state:
+    chat_history = [{'user':'assistant', 'content':first_assistant_message}]
+    st.session_state['chat_history'] = chat_history
+else:
+    chat_history = st.session_state['chat_history']
+
+# print the conversation
+for message in chat_history:
+    with st.chat_message(message['user']):
+        st.write(message['content'])
+
+# convert the chat history to a string to be passed to the model
+# keep only last 4 messages
+chat_history_str = "\n".join([message['content'] for message in chat_history[-4:] if 'content' in message])
+
+# get the input from user
+user_input = st.chat_input("Write something...")
+if user_input:
+
+    with st.chat_message("user"):
+        st.write(user_input)
+
+    # load the tokenizer
+    info_section.info("Loading the tokenizer. This may take a while...")
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+    inputs = tokenizer.encode_plus(chat_history_str, 
+                                   user_input, 
+                                   return_tensors="pt")
+    
+    # get the model's response
+    info_section.info("Loading the model. This also may take a while...")
+    model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_PATH)
+    info_section.empty()
+
+    with st.spinner("Generating the response..."):
+
+        # generate the response
+        outputs = model.generate(**inputs)
+        # decode the outputs
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
+
+    # append to the history
+    chat_history.append({'content':user_input, 'user':'user'})
+    chat_history.append({'content':response,   'user':'assistant'})
+
+    st.session_state['chat_history'] = chat_history
+    st.rerun()
+

config.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+
+DASHBOARD_TITLE = "Dumb Chatbot"
+MODEL_PATH = "facebook/blenderbot-400M-distill"
+MODEL_LINK = f"https://huggingface.co/{MODEL_PATH}"

functions.py

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+from config import *

requirements.txt

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+streamlit
+tensorflow
+tf-keras
+transformers==4.30.2 
+torch