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import streamlit as st
from transformers import pipeline
##############################################################################
# LOAD MODELS
##############################################################################
@st.cache_resource
def load_model_engineer():
# Engineer: DeepSeek-V5 via pipeline
engineer_pipeline = pipeline(
"text-generation",
model="unsloth/DeepSeek-V3",
trust_remote_code=True
)
return engineer_pipeline
@st.cache_resource
def load_model_analyst():
# Analyst: DeepSeek-V5 via pipeline
analyst_pipeline = pipeline(
"text-generation",
model="unsloth/DeepSeek-V3",
trust_remote_code=True
)
return analyst_pipeline
# Load models
engineer_pipeline = load_model_engineer()
analyst_pipeline = load_model_analyst()
##############################################################################
# ENGINEER / ANALYST GENERATION
##############################################################################
def generate_response(prompt, pipeline_model, max_sentences=2):
"""
Generate a concise response based on the provided prompt.
"""
outputs = pipeline_model(prompt, max_new_tokens=50, temperature=0.6, top_p=0.8)
response = outputs[0]["generated_text"].strip()
# Limit to max_sentences by splitting and rejoining
return " ".join(response.split(".")[:max_sentences]) + "."
def summarize_conversation(conversation):
"""
Summarize the entire conversation to produce a cohesive and actionable plan.
"""
summary = "### Final Plan\n"
key_points = []
for speaker, text in conversation:
if speaker == "Engineer" or speaker == "Analyst":
key_points.append(f"- {speaker}: {text}")
summary += "\n".join(key_points[-6:]) # Include only the last 3 turns each
summary += "\n\nThis collaborative plan integrates technical and analytical insights into an actionable framework."
return summary
##############################################################################
# STREAMLIT APP
##############################################################################
st.title("Multi-Agent System with XAI Demo")
if "conversation" not in st.session_state:
st.session_state.conversation = []
if "user_input" not in st.session_state:
st.session_state.user_input = ""
st.text_area("Enter your query:", value=st.session_state.user_input, height=100, max_chars=None, key="user_input")
if st.button("Generate Responses"):
if st.session_state.user_input.strip():
user_text = st.session_state.user_input
st.session_state.conversation = [("User", user_text)] # Clear and restart conversation
engineer_prompt_base = f"Given the problem: {user_text}, provide a concise and actionable technical solution."
analyst_prompt_base = "Based on the engineer's suggestion, provide complementary data-driven recommendations."
for turn in range(3):
# Engineer generates a response
with st.spinner(f"Engineer is formulating response {turn + 1}..."):
engineer_resp = generate_response(
prompt=engineer_prompt_base,
pipeline_model=engineer_pipeline
)
st.session_state.conversation.append(("Engineer", engineer_resp))
# Display Engineer response
st.markdown(f"### Engineer Response ({turn + 1})\n{engineer_resp}")
# Analyst generates a response based on engineer's output
with st.spinner(f"Analyst is formulating response {turn + 1}..."):
analyst_resp = generate_response(
prompt=f"Engineer suggested: {engineer_resp}. {analyst_prompt_base}",
pipeline_model=analyst_pipeline
)
st.session_state.conversation.append(("Analyst", analyst_resp))
# Display Analyst response
st.markdown(f"### Analyst Response ({turn + 1})\n{analyst_resp}")
# Summarize the final plan
with st.spinner("Generating the final plan..."):
final_plan = summarize_conversation |