src/streamlit_app.py

# FallnAI AgentBuilder Pro v2.0
import streamlit as st
import sys
import io
import re
import json
import contextlib
from typing import List, Optional, Dict, Any
from pydantic import BaseModel, Field
from crewai import Agent, Task, Crew, Process, LLM

# --- CONFIGURATION SCHEMAS ---

class AgentSchema(BaseModel):
    role: str = Field(..., min_length=2)
    goal: str = Field(..., min_length=5)
    backstory: str = Field(..., min_length=5)
    allow_delegation: bool = False
    verbose: bool = True

class TaskSchema(BaseModel):
    description: str = Field(..., min_length=5)
    expected_output: str = Field(..., min_length=5)
    agent_index: int

class CrewConfigSchema(BaseModel):
    agents: List[AgentSchema]
    tasks: List[TaskSchema]
    process_type: str = "sequential"
    memory: bool = False
    cache: bool = True

# --- LOGGING INTERFACE ---

class StreamlitRedirect(io.StringIO):
    """Custom stream to redirect stdout to a Streamlit container."""
    def __init__(self, container):
        super().__init__()
        self.container = container
        self.text = ""

    def write(self, data):
        # Clean ANSI escape sequences for cleaner UI logs
        clean_data = re.sub(r'\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])', '', data)
        if clean_data:
            self.text += clean_data
            self.container.code(self.text, language='text')
        return len(data)

@contextlib.contextmanager
def st_capture_stdout(container):
    """Context manager to capture stdout and update streamlit code block."""
    redirector = StreamlitRedirect(container)
    old_stdout = sys.stdout
    sys.stdout = redirector
    try:
        yield redirector
    finally:
        sys.stdout = old_stdout

# --- THE ENGINE LAYER ---

class CrewProcessor:
    """Transforms validated UI data into runnable CrewAI objects using Hugging Face."""
    
    @staticmethod
    def build_and_run(config: CrewConfigSchema, hf_token: str, model_id: str, temperature: float) -> str:
        # Initialize LLM using the Hugging Face provider
        custom_llm = LLM(
            model=f"huggingface/{model_id}",
            api_key=hf_token,
            temperature=temperature
        )
        
        # 1. Build Agents
        agents = []
        for a_conf in config.agents:
            agents.append(Agent(
                role=a_conf.role,
                goal=a_conf.goal,
                backstory=a_conf.backstory,
                allow_delegation=a_conf.allow_delegation,
                verbose=a_conf.verbose,
                llm=custom_llm
            ))
            
        # 2. Build Tasks
        tasks = []
        for t_conf in config.tasks:
            assigned_agent = agents[t_conf.agent_index]
            tasks.append(Task(
                description=t_conf.description,
                expected_output=t_conf.expected_output,
                agent=assigned_agent
            ))
            
        # 3. Formulate Crew
        process_map = {
            "sequential": Process.sequential,
            "hierarchical": Process.hierarchical
        }
        
        crew = Crew(
            agents=agents,
            tasks=tasks,
            process=process_map.get(config.process_type, Process.sequential),
            manager_llm=custom_llm if config.process_type == "hierarchical" else None,
            verbose=True,
            memory=config.memory,
            cache=config.cache
        )
        
        # Kickoff returns a CrewOutput object; .raw contains the string result
        result = crew.kickoff()
        return str(result.raw) if hasattr(result, 'raw') else str(result)

# --- CODE GENERATOR ---

def generate_standalone_script(config_dict: dict, model_id: str, temperature: float, process_type: str) -> str:
    """Generates a standalone Python script for CLI execution."""
    script = f"""# Auto-generated FallnAI CrewAI Script
import os
from crewai import Agent, Task, Crew, Process, LLM

# Ensure your HF_TOKEN is set in your environment variables:
# export HF_TOKEN="your_token_here"

def main():
    llm = LLM(
        model="huggingface/{model_id}",
        api_key=os.environ.get("HF_TOKEN"),
        temperature={temperature}
    )
    """
    
    script += "\n    # --- AGENTS ---\n"
    for i, a in enumerate(config_dict['agents']):
        script += f"""    agent_{i} = Agent(
        role="{a['role']}",
        goal=\"\"\"{a['goal']}\"\"\",
        backstory=\"\"\"{a['backstory']}\"\"\",
        allow_delegation={a['allow_delegation']},
        verbose=True,
        llm=llm
    )\n"""

    script += "\n    # --- TASKS ---\n"
    for i, t in enumerate(config_dict['tasks']):
        script += f"""    task_{i} = Task(
        description=\"\"\"{t['description']}\"\"\",
        expected_output=\"\"\"{t['expected_output']}\"\"\",
        agent=agent_{t['agent_index']}
    )\n"""

    process_enum = "Process.sequential" if process_type == "sequential" else "Process.hierarchical"
    
    script += f"""
    # --- CREW ---
    crew = Crew(
        agents=[{', '.join([f'agent_{i}' for i in range(len(config_dict['agents']))])}],
        tasks=[{', '.join([f'task_{i}' for i in range(len(config_dict['tasks']))])}],
        process={process_enum},
        manager_llm=llm if "{process_type}" == "hierarchical" else None,
        verbose=True
    )

    print("🚀 Kicking off the crew...")
    result = crew.kickoff()
    print("\\n### FINAL RESULT ###\\n")
    print(getattr(result, 'raw', result))

if __name__ == "__main__":
    main()
"""
    return script

# --- THE UI LAYER ---

def init_session_state():
    if "agents" not in st.session_state:
        st.session_state.agents = [{"role": "Researcher", "goal": "Find latest AI news", "backstory": "An expert analyst.", "allow_delegation": False}]
    if "tasks" not in st.session_state:
        st.session_state.tasks = [{"description": "Summarize top 3 AI papers from ArXiv.", "expected_output": "A 3-paragraph summary.", "agent_index": 0}]

def load_config_from_file(uploaded_file):
    try:
        data = json.load(uploaded_file)
        if "agents" in data and "tasks" in data:
            st.session_state.agents = data["agents"]
            st.session_state.tasks = data["tasks"]
            st.success("Configuration loaded successfully!")
            st.rerun()
        else:
            st.error("Invalid configuration format.")
    except Exception as e:
        st.error(f"Error reading file: {e}")

def main():
    st.set_page_config(page_title="FallnAI AgentBuilder Pro", layout="wide", page_icon="⚙️")
    init_session_state()

    st.title("FallnAI AgentBuilder Pro v2.0")
    st.markdown("Design, manage, and deploy advanced multi-agentic systems.")

    # Sidebar: Global Settings & File Management
    with st.sidebar:
        st.header("📁 Configuration Manager")
        
        # File Upload
        uploaded_file = st.file_uploader("Import JSON Config", type=["json"])
        if uploaded_file is not None:
            if st.button("Load Configuration"):
                load_config_from_file(uploaded_file)

        # File Export
        current_config = {
            "agents": st.session_state.agents,
            "tasks": st.session_state.tasks
        }
        st.download_button(
            label="💾 Export JSON Config",
            data=json.dumps(current_config, indent=4),
            file_name="fallnai_crew_config.json",
            mime="application/json",
            use_container_width=True
        )

        st.divider()

        st.header("🧠 Engine Settings")
        hf_token = st.text_input("Hugging Face API Token", type="password")
        model_id = st.text_input("Model ID", value="meta-llama/Llama-3.1-8B-Instruct")
        temperature = st.slider("Temperature", 0.0, 1.0, 0.7, 0.1)
        
        st.subheader("Crew Dynamics")
        process_type = st.radio("Process Strategy", ["sequential", "hierarchical"])
        enable_memory = st.checkbox("Enable Memory (Requires Embeddings)", value=False)
        enable_cache = st.checkbox("Enable Caching", value=True)

    tab1, tab2, tab3, tab4 = st.tabs(["👥 Define Agents", "📝 Define Tasks", "🚀 Run Crew", "💻 CLI Deploy Script"])

    # TAB 1: AGENT CONFIGURATION
    with tab1:
        st.subheader("Agent Configuration")
        for i, agent in enumerate(st.session_state.agents):
            with st.expander(f"Agent {i+1}: {agent.get('role', 'New')}", expanded=False):
                col1, col2 = st.columns([3, 1])
                with col1:
                    st.session_state.agents[i]['role'] = st.text_input("Role", value=agent.get('role', ''), key=f"role_{i}")
                    st.session_state.agents[i]['goal'] = st.text_input("Goal", value=agent.get('goal', ''), key=f"goal_{i}")
                    st.session_state.agents[i]['backstory'] = st.text_area("Backstory", value=agent.get('backstory', ''), key=f"bs_{i}")
                with col2:
                    st.session_state.agents[i]['allow_delegation'] = st.checkbox("Allow Delegation", value=agent.get('allow_delegation', False), key=f"del_{i}")
                    st.markdown("<br><br>", unsafe_allow_html=True)
                    if st.button("🗑️ Remove Agent", key=f"rem_agent_{i}", use_container_width=True):
                        st.session_state.agents.pop(i)
                        st.rerun()

        if st.button("➕ Add New Agent", type="secondary"):
            st.session_state.agents.append({"role": "New Agent", "goal": "", "backstory": "", "allow_delegation": False})
            st.rerun()

    # TAB 2: TASK CONFIGURATION
    with tab2:
        st.subheader("Task Execution Sequence")
        agent_roles = [a['role'] for a in st.session_state.agents]
        
        for i, task in enumerate(st.session_state.tasks):
            with st.expander(f"Task {i+1}", expanded=False):
                col1, col2 = st.columns([3, 1])
                with col1:
                    st.session_state.tasks[i]['description'] = st.text_area("Description", value=task.get('description', ''), key=f"tdesc_{i}")
                    st.session_state.tasks[i]['expected_output'] = st.text_input("Expected Output", value=task.get('expected_output', ''), key=f"tout_{i}")
                with col2:
                    current_idx = task.get('agent_index', 0)
                    safe_idx = min(current_idx, len(agent_roles) - 1) if agent_roles else 0
                    
                    st.session_state.tasks[i]['agent_index'] = st.selectbox(
                        "Assign to Agent", 
                        options=range(len(agent_roles)), 
                        format_func=lambda x: agent_roles[x] if agent_roles else "No Agents",
                        index=safe_idx,
                        key=f"t_agent_{i}"
                    )
                    st.markdown("<br><br>", unsafe_allow_html=True)
                    if st.button("🗑️ Remove Task", key=f"rem_task_{i}", use_container_width=True):
                        st.session_state.tasks.pop(i)
                        st.rerun()

        if st.button("➕ Add New Task", type="secondary"):
            st.session_state.tasks.append({"description": "", "expected_output": "", "agent_index": 0})
            st.rerun()

    # TAB 3: EXECUTION
    with tab3:
        st.subheader("Live Execution")
        
        if not hf_token:
            st.warning("⚠️ Please provide a Hugging Face API Token in the sidebar to run the crew.")
        else:
            if st.button("🚀 Execute Swarm", use_container_width=True, type="primary"):
                try:
                    # Validation
                    config_data = CrewConfigSchema(
                        agents=[AgentSchema(**a) for a in st.session_state.agents],
                        tasks=[TaskSchema(**t) for t in st.session_state.tasks],
                        process_type=process_type,
                        memory=enable_memory,
                        cache=enable_cache
                    )
                    
                    log_container = st.container()
                    log_container.markdown("### Runtime Logs")
                    log_block = log_container.empty()
                    
                    with st.spinner(f"Initializing distributed run using {model_id}..."):
                        with st_capture_stdout(log_block):
                            result = CrewProcessor.build_and_run(config_data, hf_token, model_id, temperature)
                    
                    st.success("✅ Execution Completed Successfully!")
                    st.markdown("### Output Artifact")
                    st.markdown(result)
                    
                    st.download_button(
                        label="📥 Download Output Artifact (.md)",
                        data=str(result),
                        file_name="fallnai_output.md",
                        mime="text/markdown",
                        use_container_width=True
                    )

                except Exception as e:
                    st.error(f"Execution Error: {str(e)}")

    # TAB 4: CLI SCRIPT EXPORT
    with tab4:
        st.subheader("Standalone Python Script")
        st.markdown("Export your configuration as a runnable Python script. Ideal for environments like Termux or dedicated server instances.")
        
        cli_code = generate_standalone_script(current_config, model_id, temperature, process_type)
        st.code(cli_code, language="python")
        
        st.download_button(
            label="🐍 Download Python Script",
            data=cli_code,
            file_name="run_crew.py",
            mime="text/x-python",
            use_container_width=True
        )

if __name__ == "__main__":
    main()