import streamlit as st
from graph import EssayWriter, RouteQuery, GraphState
from language_options import language_options
from crew import *
import os
import re
import traceback
import base64
# Install Graphviz if not found
if os.system("which dot") != 0:
os.system("apt-get update && apt-get install -y graphviz")
st.markdown(
"""
Multi-Agent Essay Writing Assistant
""",
unsafe_allow_html=True
)
# Ensure session state variables are initialized properly
if "messages" not in st.session_state:
st.session_state["messages"] = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
if "app" not in st.session_state:
st.session_state["app"] = None
if "chat_active" not in st.session_state:
st.session_state["chat_active"] = True
# Sidebar with essay settings and user-defined length
# Sidebar with essay settings and user-defined length
with st.sidebar:
st.subheader("📝 Note:")
st.info(
"\n\n 1. This app uses the 'gpt-4o-mini-2024-07-18' model."
"\n\n 2. Writing essays may take some time, approximately 1-2 minutes."
)
# API Key Retrieval
openai_key = st.secrets.get("OPENAI_API_KEY", "")
st.divider()
# User-defined essay length selection
st.subheader("⚙️🛠️ Configure Essay Settings:")
essay_length = st.number_input(
"Select Essay Length (words):",
min_value=150,
max_value=500,
value=250,
step=50
)
#st.divider()
#Language Selection
#st.subheader("🌍 Select Language:")
selected_language = st.selectbox("Choose Language:", sorted(language_options.keys()), index=list(language_options.keys()).index("English"))
st.divider()
# Reference section
st.subheader("📖 References:")
st.markdown(
"[1. Multi-Agent System with CrewAI and LangChain](https://discuss.streamlit.io/t/new-project-i-have-build-a-multi-agent-system-with-crewai-and-langchain/84002)",
unsafe_allow_html=True
)
# Initialize agents function
def initialize_agents():
if not openai_key:
st.error("⚠️ OpenAI API key is missing! Please provide a valid key through Hugging Face Secrets.")
st.session_state["chat_active"] = True
return None
os.environ["OPENAI_API_KEY"] = openai_key
try:
# Prevent re-initialization
if "app" in st.session_state and st.session_state["app"] is not None:
return st.session_state["app"]
# Initialize the full EssayWriter instance
essay_writer = EssayWriter() # Store the full instance
st.session_state["app"] = essay_writer # Now contains `graph`
st.session_state["chat_active"] = False # Enable chat after successful initialization
return essay_writer
except Exception as e:
st.error(f"❌ Error initializing agents: {e}")
st.session_state["chat_active"] = True
return None
# Automatically initialize agents on app load
if st.session_state["app"] is None:
st.session_state["app"] = initialize_agents()
if st.session_state["app"] is None:
st.error("⚠️ Failed to initialize agents. Please check your API key and restart the app.")
app = st.session_state["app"]
# Function to invoke the agent and generate a response
def enforce_word_limit(text, limit):
"""Enforces strict word limit by truncating extra words."""
words = re.findall(r'\b\w+\b', text)
return ' '.join(words[:limit]) if len(words) > limit else text
def detect_unexpected_english(text, selected_language):
"""Detects unintended English words when another language is selected."""
if selected_language != "English":
english_words = re.findall(r'\b(?:is|the|and|or|in|on|at|to|with|for|of|by|it|that|this|was|he|she|they|we|you|I)\b', text)
return len(english_words) > 5 # Allow a small tolerance
def generate_response(topic, length, selected_language):
if not app or not hasattr(app, "graph"):
st.error("Agents are not initialized. Please check the system or restart the app.")
return {"response": "Error: Agents not initialized."}
# Dynamically adjust structure based on length
if length <= 250:
intro_limit, body_limit, conclusion_limit = length // 5, length // 2, length // 5
num_sections = 2 # Shorter essays should have fewer sections
elif length <= 350:
intro_limit, body_limit, conclusion_limit = length // 6, length // 1.8, length // 6
num_sections = 3
else:
intro_limit, body_limit, conclusion_limit = length // 7, length // 1.7, length // 7
num_sections = 4
# Optimized Structured Prompt
refined_prompt = f"""
Write a **well-structured, informative, and engaging** essay on "{topic}" **strictly in {selected_language}.**
**Word Limit:** Exactly {length} words. **Do not exceed or fall short of this limit.**
**Language Rules:** Use natural linguistic style from {selected_language}. **Do not use English** unless explicitly requested.
**Essay Structure:**
- **Title**: Max 10 words.
- **Introduction ({intro_limit} words max)**:
- Clearly define the topic and its significance.
- Provide a strong thesis statement.
- Preview the key points covered in the essay.
- **Main Body ({body_limit} words max, {num_sections} sections)**:
- Each section must have:
- A **clear subheading**.
- A concise topic sentence with supporting details.
- Relevant **examples, statistics, or historical references**.
- Maintain natural **flow** between sections.
- **Conclusion ({conclusion_limit} words max)**:
- Summarize key insights **without repetition**.
- Reinforce the thesis **based on discussion**.
- End with a strong **closing statement** (reflection or call to action).
**Hard Rules:**
- **Use only {selected_language}**. No English unless explicitly requested.
- **Do not exceed {length} words.** Absolute limit.
- **Write concisely and avoid fluff**. No redundancy.
- **Merge similar ideas** to maintain smooth readability.
- **Ensure strict adherence to section word limits**.
"""
# Invoke AI model with enforced word limit
response = app.graph.invoke(input={
"topic": topic,
"length": length,
"prompt": refined_prompt,
"language": selected_language,
"max_tokens": length + 10 # Small buffer for better trimming
})
# Strict word limit enforcement
essay_text = enforce_word_limit(response.get("essay", ""), length)
# Detect unintended English words in non-English essays
if detect_unexpected_english(essay_text, selected_language):
return {"response": f"⚠️ Warning: Some English words were detected in the {selected_language} essay. Try regenerating."}
return {"essay": essay_text}
# Define Tabs
tab1, tab2 = st.tabs(["📜 Essay Generation", "📊 Workflow Viz"])
# 📜 Tab 1: Essay Generation
with tab1:
# Display chat messages from the session
if "messages" not in st.session_state:
st.session_state["messages"] = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
for message in st.session_state["messages"]:
with st.chat_message(message["role"]):
st.markdown(message["content"], unsafe_allow_html=True)
# Input
topic = st.text_input("📝 Provide an essay topic:", value="Write an essay on the cultural diversity of India")
# Add spacing
st.write("")
# Generate button
if st.button("🚀 Generate Essay"):
if topic and topic.strip(): # Ensure it's not empty
# Store user message only if it's not already stored
if not any(msg["content"] == topic for msg in st.session_state["messages"]):
st.session_state["messages"].append({"role": "user", "content": topic})
with st.spinner("⏳ Generating your essay..."):
response = None
if app:
response = app.write_essay({"topic": topic})
else:
st.error("⚠️ Agents are not initialized. Please check the system or restart the app.")
# Store and display assistant response
if response and "essay" in response:
essay = response["essay"]
assistant_response = f"Here is your {essay_length}-word essay preview and the download link."
st.session_state["messages"].append({"role": "assistant", "content": assistant_response})
st.chat_message("assistant").markdown(assistant_response)
# Create Two-Column Layout
col1, col2 = st.columns(2)
with col1:
st.markdown(f"### 📝 Essay Preview ({essay_length} words)")
st.markdown(f"#### {essay['header']}")
st.markdown(essay["entry"])
for para in essay["paragraphs"]:
st.markdown(f"**{para['sub_header']}**")
st.markdown(para["paragraph"])
st.markdown("**🖊️ Conclusion:**")
st.markdown(essay["conclusion"])
with col2:
st.markdown("### ✍️ Edit Your Essay:")
# Combine all parts of the essay into one editable text field
full_essay_text = f"## {essay['header']}\n\n{essay['entry']}\n\n"
for para in essay["paragraphs"]:
full_essay_text += f"### {para['sub_header']}\n{para['paragraph']}\n\n"
full_essay_text += f"**Conclusion:**\n{essay['conclusion']}"
# Editable text area for the user
edited_essay = st.text_area("Edit Here:", value=full_essay_text, height=300)
# Save and Download buttons
save_col1, save_col2 = st.columns(2)
with save_col1:
if st.button("💾 Save as TXT"):
with open("edited_essay.txt", "w", encoding="utf-8") as file:
file.write(edited_essay)
with open("edited_essay.txt", "rb") as file:
st.download_button(label="⬇️ Download TXT", data=file, file_name="edited_essay.txt", mime="text/plain")
with save_col2:
if st.button("📄 Save as PDF"):
from fpdf import FPDF
pdf = FPDF()
pdf.set_auto_page_break(auto=True, margin=15)
pdf.add_page()
pdf.set_font("Arial", size=12)
for line in edited_essay.split("\n"):
pdf.cell(200, 10, txt=line, ln=True, align='L')
pdf.output("edited_essay.pdf")
with open("edited_essay.pdf", "rb") as file:
st.download_button(label="⬇️ Download PDF", data=file, file_name="edited_essay.pdf", mime="application/pdf")
# Provide download link for the original PDF
pdf_name = response.get("pdf_name")
if pdf_name and os.path.exists(pdf_name):
with open(pdf_name, "rb") as pdf_file:
b64 = base64.b64encode(pdf_file.read()).decode()
href = f"📄 Click here to download the original PDF"
st.markdown(href, unsafe_allow_html=True)
# Save response in session state
st.session_state["messages"].append(
{"role": "assistant", "content": f"Here is your {essay_length}-word essay preview and the download link."}
)
elif response:
st.markdown(response["response"])
st.session_state["messages"].append({"role": "assistant", "content": response["response"]})
else:
st.error("⚠️ No response received. Please try again.")
# 📊 Tab 2: Workflow Visualization
with tab2:
#st.subheader("📊 Multi-Agent Essay Writer Workflow Viz")
try:
graph_path = "/tmp/graph.png"
if os.path.exists(graph_path):
st.image(graph_path, caption="Multi-Agent Essay Writer Workflow Visualization", use_container_width=True)
else:
st.warning("⚠️ Workflow graph not found. Please run `graph.py` to regenerate `graph.png`.")
except Exception as e:
st.error("❌ An error occurred while generating the workflow visualization.")
st.text_area("Error Details:", traceback.format_exc(), height=500)
# Acknowledgement Section
st.markdown(
"""
""",
unsafe_allow_html=True,
)