app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
from threading import Thread

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")

# Define the research paper summary
research_summary = """
Hybrid additive manufacturing processes combining FDM and SLA have been developed to improve resolution and mechanical strength.
Key insights include:
1. Optimal layer height to minimize delamination in multi-material prints.
2. Enhanced print quality with in-situ monitoring using thermal imaging.
3. Applications in medical implants, aerospace components, and lightweight structures.
4. Innovations in material blending techniques for improved flexibility and durability.
5. Cost analysis and efficiency improvements in production workflows.
"""

# Function to retrieve relevant sections of the summary
def get_relevant_summary(query, summary):
    sections = summary.split("\n")
    relevant_sections = [section for section in sections if query.lower() in section.lower()]
    return "\n".join(relevant_sections) if relevant_sections else "No relevant information found in the research summary."

# Function to generate chatbot responses
def generate_response(user_input, history, temperature, max_tokens):
    # Retrieve relevant summary sections
    relevant_summary = get_relevant_summary(user_input, research_summary)

    # Build the domain-specific system instruction
    domain_instruction = (
        "You are a chatbot specialized in additive manufacturing (3D printing). "
        "Below is a relevant section of the research paper summary:\n\n"
        + relevant_summary +
        "\n\nAnswer queries related to this research or general 3D printing topics. "
        "Provide accurate, concise, and actionable responses."
    )

    conversation = [{"role": "system", "content": domain_instruction}]
    for user, assistant in history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": user_input})

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        input_ids=input_ids,
        streamer=streamer,
        max_new_tokens=max_tokens,
        do_sample=True,
        temperature=temperature,
    )
    
    if temperature == 0:
        generate_kwargs['do_sample'] = False

    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)

# Streamlit app
st.title("Additive Manufacturing Chatbot")
st.markdown("### Ask questions about 3D printing or the research paper summary!")

# Sidebar parameters
st.sidebar.header("Model Parameters")
temperature = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, step=0.1)
max_tokens = st.sidebar.slider("Max Tokens", min_value=128, max_value=1024, value=512, step=32)

# Chat interface
if "history" not in st.session_state:
    st.session_state.history = []

user_input = st.text_input("Your question:", placeholder="Ask me about 3D printing or hybrid manufacturing...")

if user_input:
    with st.spinner("Generating response..."):
        response_generator = generate_response(user_input, st.session_state.history, temperature, max_tokens)
        response = ""
        for partial_response in response_generator:
            response = partial_response
            st.empty()  # Clear previous response output for a smooth UI update
        
        # Update conversation history
        st.session_state.history.append((user_input, response))

# Display chat history
for user, assistant in st.session_state.history:
    st.markdown(f"**You:** {user}")
    st.markdown(f"**Assistant:** {assistant}")
    st.markdown("---")