Create app.py

app.py

@@ -0,0 +1,147 @@
+import os
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, pipeline
+import streamlit as st
+
+# Define the model repository
+REPO_NAME = 'schuler/experimental-JP47D20'
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(REPO_NAME, trust_remote_code=True)
+generator_conf = GenerationConfig.from_pretrained(REPO_NAME)
+model = AutoModelForCausalLM.from_pretrained(REPO_NAME, trust_remote_code=True)
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Configure the Streamlit app
+st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗")
+st.title("Personal HuggingFace ChatBot")
+st.markdown(f"*This is a simple chatbot that uses the HuggingFace transformers library to generate responses to your text input. It uses the {REPO_NAME} model.*")
+
+# Initialize session state for avatars
+if "avatars" not in st.session_state:
+    st.session_state.avatars = {'user': None, 'assistant': None}
+
+# Initialize session state for user text input
+if 'user_text' not in st.session_state:
+    st.session_state.user_text = None
+
+# Initialize session state for model parameters
+if "max_response_length" not in st.session_state:
+    st.session_state.max_response_length = 256
+
+if "system_message" not in st.session_state:
+    st.session_state.system_message = "You are a friendly AI conversing with a human user."
+
+if "starter_message" not in st.session_state:
+    st.session_state.starter_message = "Hello, there! How can I help you today?"
+
+# Sidebar for settings
+with st.sidebar:
+    st.header("System Settings")
+
+    # AI Settings
+    st.session_state.system_message = st.text_area(
+        "System Message", value=st.session_state.system_message
+    )
+    st.session_state.starter_message = st.text_area(
+        'First AI Message', value=st.session_state.starter_message
+    )
+
+    # Model Settings
+    st.session_state.max_response_length = st.number_input(
+        "Max Response Length", value=st.session_state.max_response_length
+    )
+
+    # Avatar Selection
+    st.markdown("*Select Avatars:*")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.session_state.avatars['assistant'] = st.selectbox(
+            "AI Avatar", options=["🤗", "💬", "🤖"], index=0
+        )
+    with col2:
+        st.session_state.avatars['user'] = st.selectbox(
+            "User Avatar", options=["👤", "👱‍♂️", "👨🏾", "👩", "👧🏾"], index=0
+        )
+    # Reset Chat History
+    reset_history = st.button("Reset Chat History")
+
+# Initialize or reset chat history
+if "chat_history" not in st.session_state or reset_history:
+    st.session_state.chat_history = [{"role": "assistant", "content": st.session_state.starter_message}]
+
+def get_response(system_message, chat_history, user_text, max_new_tokens=256):
+    """
+    Generates a response from the chatbot model.
+
+    Args:
+        system_message (str): The system message for the conversation.
+        chat_history (list): The list of previous chat messages.
+        user_text (str): The user's input text.
+        max_new_tokens (int): The maximum number of new tokens to generate.
+
+    Returns:
+        tuple: A tuple containing the generated response and the updated chat history.
+    """
+    # Build the conversation prompt
+    prompt = "" 
+    # f"{system_message}\nCurrent Conversation:\n"
+    for message in chat_history:
+        role = "<|assistant|>" if message['role'] == 'assistant' else "<|user|>"
+        prompt += f"\n{role}\n{message['content']}\n"
+    prompt += f"\n<|user|>\n {user_text}\n<|assistant|>\"
+
+    # Generate the response
+    response_output = generator(
+        prompt,
+        generation_config=generator_conf,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=0.5,
+        repetition_penalty=1.2
+    )
+
+    generated_text = response_output[0]['generated_text']
+
+    # Extract the assistant's response
+    assistant_response = generated_text[len(prompt):].strip()
+
+    # Update the chat history
+    chat_history.append({'role': 'user', 'content': user_text})
+    chat_history.append({'role': 'assistant', 'content': assistant_response})
+
+    return assistant_response, chat_history
+
+# Chat interface
+chat_interface = st.container()
+with chat_interface:
+    output_container = st.container()
+    st.session_state.user_text = st.chat_input(placeholder="Enter your text here.")
+
+# Display chat messages
+with output_container:
+    for message in st.session_state.chat_history:
+        if message['role'] == 'system':
+            continue
+        with st.chat_message(message['role'], avatar=st.session_state.avatars[message['role']]):
+            st.markdown(message['content'])
+
+# When the user enters new text
+if st.session_state.user_text:
+    # Display the user's message
+    with st.chat_message("user", avatar=st.session_state.avatars['user']):
+        st.markdown(st.session_state.user_text)
+
+    # Display a spinner while generating the response
+    with st.chat_message("assistant", avatar=st.session_state.avatars['assistant']):
+        with st.spinner("Thinking..."):
+            # Generate the assistant's response
+            response, st.session_state.chat_history = get_response(
+                system_message=st.session_state.system_message,
+                user_text=st.session_state.user_text,
+                chat_history=st.session_state.chat_history,
+                max_new_tokens=st.session_state.max_response_length,
+            )
+            st.markdown(response)
+
+    # Clear the user input
+    st.session_state.user_text = None