import streamlit as st import requests import json import os import psutil import platform import time import pypdf from concurrent.futures import ThreadPoolExecutor from streamlit.runtime.scriptrunner import add_script_run_ctx, get_script_run_ctx from agent import AgentWorkspace, CodeExecutor, extract_code_blocks, parse_filename_from_code # -------------------- CONFIG -------------------- st.set_page_config(page_title="Neon AI", layout="wide", page_icon="โšก") MEMORY_FILE = "/app/data/memory.json" if os.path.exists("/app/data") else "memory.json" # -------------------- MEMORY SYSTEM -------------------- def load_memory(): if os.path.exists(MEMORY_FILE): try: with open(MEMORY_FILE, "r") as f: return json.load(f) except: return [] return [] def save_memory(messages): try: with open(MEMORY_FILE, "w") as f: json.dump(messages, f, indent=2) except: pass if "messages" not in st.session_state: st.session_state.messages = load_memory() if "file_context" not in st.session_state: st.session_state.file_context = "" # Initialize Agent System if "workspace" not in st.session_state: st.session_state.workspace = AgentWorkspace() st.session_state.executor = CodeExecutor(st.session_state.workspace) if "terminal_log" not in st.session_state: st.session_state.terminal_log = "Welcome to Neon Terminal v1.0\n" if "trigger_gen" not in st.session_state: st.session_state.trigger_gen = False # -------------------- CSS -------------------- st.markdown(""" """, unsafe_allow_html=True) # -------------------- HELPERS -------------------- def get_size(bytes, suffix="B"): factor = 1024 for unit in ["", "K", "M", "G", "T", "P"]: if bytes < factor: return f"{bytes:.1f}{unit}{suffix}" bytes /= factor def get_system_stats(): cpu = psutil.cpu_percent(interval=0.1) ram = psutil.virtual_memory() disk = psutil.disk_usage('/') return cpu, ram.percent, get_size(ram.used), get_size(ram.total), disk.percent, get_size(disk.free), get_size(disk.total) def process_uploaded_file(uploaded_file): if uploaded_file is None: return "" try: uploaded_file.seek(0) # PDF Processing if uploaded_file.type == "application/pdf": reader = pypdf.PdfReader(uploaded_file) text = "" for page in reader.pages: text += page.extract_text() + "\n" return text.strip() # Image Processing (Placeholder for now, just description) elif uploaded_file.type in ["image/png", "image/jpeg", "image/jpg"]: return f"[User uploaded an image: {uploaded_file.name}]" else: return f"[Unsupported file type: {uploaded_file.type}]" except Exception as e: return f"[Error processing file: {e}]" # -------------------- SIDEBAR -------------------- with st.sidebar: st.markdown("### ๐Ÿ–ฅ๏ธ SYSTEM HUD") cpu, ram_p, ram_u, ram_t, disk_p, disk_f, disk_t = get_system_stats() cpu_c = "#ff4b4b" if cpu > 80 else "#00ffe0" ram_c = "#ff4b4b" if ram_p > 85 else "#ff00cc" # SVG Strings (Compact) cpu_svg = f'{cpu}%' ram_svg = f'{ram_p}%' st.markdown(f"""
{cpu_svg}
CPU Load
{ram_svg}
RAM Usage
{ram_u} / {ram_t}
""", unsafe_allow_html=True) st.markdown(f"""
Storage{disk_f} Free
{disk_p}% Full of {disk_t}
""", unsafe_allow_html=True) # ---------------- MODEL SELECTION LOGIC ---------------- st.markdown("### ๐Ÿง  Neural Core") # 1. Local Models (Ollama) local_models = [] try: models_json = requests.get("http://localhost:11434/api/tags", timeout=1).json() local_models = [m["name"] for m in models_json["models"]] except: local_models = ["qwen2.5-coder:0.5b"] # 2. Cloud Dolphin Model cloud_dolphin = "โ˜๏ธ Dolphin 24B (Free API)" # 3. Hugging Face Models (From ENV & Defaults) default_hf_models = [ "dphn/Dolphin-Mistral-24B-Venice-Edition:featherless-ai", "google/gemma-7b", "moonshotai/Kimi-K2.5:novita", "qwen2.5-coder:0.5b" ] hf_models_env = os.environ.get("HF_MODELS", "") env_models = [m.strip() for m in hf_models_env.split(",") if m.strip()] hf_models = list(dict.fromkeys(env_models + default_hf_models)) # Remove duplicates while preserving order # Combine All all_models = [cloud_dolphin] + hf_models + local_models arena_mode = st.toggle("โš”๏ธ Arena Mode (Multi-Model)", value=False) if arena_mode: selected_models = st.multiselect("Select Models", all_models, default=[all_models[0]] if all_models else []) else: selected_model = st.selectbox("Active Model", all_models) selected_models = [selected_model] # Brain Template (Visible for Cloud/HF Models) template_mode = st.selectbox( "Thinking Style", ["creative", "logical", "code-advanced", "summary"], index=0 ) # Mode Toggle agent_mode = st.toggle("๐Ÿ› ๏ธ Agent Mode (Code & Exec)", value=False) if agent_mode: auto_gpt_mode = st.toggle("๐Ÿค– Autonomous Agent (Auto-Fix)", value=False) else: auto_gpt_mode = False # File Upload # st.markdown("### ๐Ÿ“‚ Data Injection") # Removed per user request # Collapsible File Upload with st.expander("๐Ÿ“‚ Upload File (PDF/Image)", expanded=False): uploaded_file = st.file_uploader("Drag and drop file here", type=["pdf", "png", "jpg", "jpeg"], label_visibility="collapsed") if uploaded_file: file_text = process_uploaded_file(uploaded_file) if file_text: st.session_state.file_context = file_text st.success("File processed & injected!") if uploaded_file.type != "application/pdf": st.image(uploaded_file, caption="Uploaded Image", use_container_width=True) else: st.session_state.file_context = "" else: st.session_state.file_context = "" col1, col2 = st.columns(2) if col1.button("โ™ป Refresh"): st.rerun() if col2.button("๐Ÿงน Clear"): st.session_state.messages = [] save_memory([]) st.rerun() # -------------------- MAIN CHAT -------------------- st.markdown("

โšก NEON AI

", unsafe_allow_html=True) # Layout Setup main_chat_container = st.container() workspace_container = None if agent_mode: col1, col2 = st.columns([1.2, 1]) with col1: main_chat_container = st.container() with col2: workspace_container = st.container() with main_chat_container: # Persistent Model Header if not arena_mode and selected_models: st.markdown(f"

Active: {selected_models[0]}

", unsafe_allow_html=True) if arena_mode and selected_models: cols = st.columns(len(selected_models)) for idx, model_name in enumerate(selected_models): with cols[idx]: # Sticky Header st.markdown(f"""
๐Ÿง  {model_name}
""", unsafe_allow_html=True) for msg in st.session_state.messages: if msg["role"] == "user": st.markdown(f"
๐Ÿ‘ค {msg['content']}
", unsafe_allow_html=True) elif msg["role"] == "assistant": if msg.get("model") == model_name or msg.get("model") is None: st.markdown(f"
๐Ÿค– {msg['content']}
", unsafe_allow_html=True) else: for msg in st.session_state.messages: if msg["role"] == "user": st.markdown(f"
๐Ÿ‘ค {msg['content']}
", unsafe_allow_html=True) else: content = msg['content'] tag = f"
({msg.get('model')})" if msg.get("model") else "" st.markdown(f"
๐Ÿค– {content}{tag}
", unsafe_allow_html=True) st.markdown("
", unsafe_allow_html=True) # -------------------- WORKSPACE UI (Right Column) -------------------- if agent_mode and workspace_container: with workspace_container: st.markdown("### ๐Ÿ› ๏ธ Workspace") # Files Tab files = st.session_state.workspace.list_files() selected_file = st.selectbox("๐Ÿ“‚ Files", ["(New File)"] + files) file_content = "" if selected_file and selected_file != "(New File)": file_content = st.session_state.workspace.read_file(selected_file) # Editor (Read Only for now) st.code(file_content if file_content else "# No file selected", language="python", line_numbers=True) # Actions c1, c2 = st.columns(2) if c1.button("โ–ถ Run File", disabled=not selected_file): if selected_file.endswith(".py"): output = st.session_state.executor.run_python(selected_file) st.session_state.terminal_log += f"\n$ python {selected_file}\n{output}\n" elif selected_file.endswith(".sh"): output = st.session_state.executor.run_command(f"bash {selected_file}") st.session_state.terminal_log += f"\n$ bash {selected_file}\n{output}\n" else: st.session_state.terminal_log += f"\n$ {selected_file} is not executable.\n" st.rerun() if c2.button("๐Ÿ’พ Save to Memory"): # Placeholder for saving to RAG or Context pass # Terminal st.markdown("#### ๐Ÿ“Ÿ Terminal Output") st.markdown(f"
{st.session_state.terminal_log}
", unsafe_allow_html=True) if st.button("Clear Terminal"): st.session_state.terminal_log = "$ \n" st.rerun() prompt = st.chat_input("Inject code or query...") should_run = False if prompt: # 1. Append User Message st.session_state.messages.append({"role": "user", "content": prompt}) save_memory(st.session_state.messages) should_run = True is_triggered_run = False if st.session_state.trigger_gen: st.session_state.trigger_gen = False should_run = True is_triggered_run = True # Recover prompt from last message for display purposes if st.session_state.messages and st.session_state.messages[-1]["role"] == "user": prompt = st.session_state.messages[-1]["content"] if should_run: # Determine containers for streaming if arena_mode and selected_models: with main_chat_container: live_containers = st.columns(len(selected_models)) elif auto_gpt_mode: # Full width for autonomous mode visualization with main_chat_container: if not is_triggered_run: st.markdown(f"
๐Ÿ‘ค {prompt}
", unsafe_allow_html=True) live_containers = [st.container()] else: with main_chat_container: if not is_triggered_run: st.markdown(f"
๐Ÿ‘ค {prompt}
", unsafe_allow_html=True) live_containers = [st.container()] # Capture the current context to pass to threads main_ctx = get_script_run_ctx() def run_autonomous_loop(model_name, prompt, container, ctx): """Runs an Auto-GPT style loop: Generate -> Run -> Fix -> Repeat.""" add_script_run_ctx(ctx=ctx) max_retries = 5 current_prompt = prompt history = [ {"role": "system", "content": "You are an autonomous coding agent. Write complete, runnable Python code to solve the user's problem. If you encounter errors, analyze them and fix your code. Always output the full corrected code block."} ] if st.session_state.file_context: history.append({"role": "system", "content": f"CONTEXT FILE:\n{st.session_state.file_context}"}) history.append({"role": "user", "content": current_prompt}) with container: st.markdown(f"### ๐Ÿค– Autonomous Agent: {model_name}") status_container = st.container() for attempt in range(max_retries): with status_container.status(f"Attempt {attempt+1}/{max_retries}", expanded=True) as status: # 1. Generate Code st.write("๐Ÿง  Thinking & Writing Code...") full_response = "" # --- MODEL CALL (Simplified for Loop) --- try: # Prepare Context msgs = history.copy() # API Call (Generic Adapter) if model_name in hf_models: hf_token = os.environ.get("HF_TOKEN") api_url = "https://router.huggingface.co/v1/chat/completions" headers = {"Authorization": f"Bearer {hf_token}"} # Increased max_tokens to prevent code truncation payload = {"model": model_name, "messages": msgs, "stream": False, "max_tokens": 4096} resp = requests.post(api_url, headers=headers, json=payload).json() full_response = resp["choices"][0]["message"]["content"] elif model_name == cloud_dolphin: api_url = "https://chat.dphn.ai/api/chat" payload = {"messages": msgs, "model": "dolphinserver:24B", "template": "code-advanced"} resp = requests.post(api_url, json=payload, headers={"Content-Type": "application/json"}).json() full_response = resp["choices"][0]["message"]["content"] else: # Local Ollama resp = requests.post("http://localhost:11434/api/chat", json={"model": model_name, "messages": msgs, "stream": False}).json() full_response = resp["message"]["content"] except Exception as e: status.update(label="โŒ Generation Failed", state="error") st.error(f"Model Error: {e}") break # ---------------------------------------- st.markdown(full_response) history.append({"role": "assistant", "content": full_response}) # 2. Extract & Save blocks = extract_code_blocks(full_response) if not blocks: status.update(label="โš ๏ธ No Code Generated", state="complete") st.warning("Model didn't generate any code.") return # Exit loop if no code # Assume last block is the main script code_block = blocks[-1] code = code_block['code'] lang = code_block['language'] if lang not in ["python", "py", "bash", "sh"]: status.update(label="โš ๏ธ Non-Executable Code", state="complete") st.info(f"Generated {lang} code, skipping execution.") return filename = parse_filename_from_code(code) if not filename: ext = "py" if lang in ["python", "py"] else "sh" filename = f"agent_script_{int(time.time())}.{ext}" st.session_state.workspace.save_file(filename, code) st.write(f"๐Ÿ’พ Saved: `{filename}`") # 3. Execute st.write("โš™๏ธ Executing...") stdout, stderr, exit_code = "", "", 0 if filename.endswith(".py"): stdout, stderr, exit_code = st.session_state.executor.run_python_safe(filename) elif filename.endswith(".sh"): stdout, stderr, exit_code = st.session_state.executor.run_command_safe(f"bash {filename}") # 4. Check Results if exit_code == 0 and not stderr: status.update(label="โœ… Success!", state="complete") st.success("Execution Successful!") st.code(stdout if stdout else "# No Output") st.session_state.terminal_log += f"\n[AUTO-AGENT SUCCESS] {filename}\n{stdout}\n" return # DONE! else: status.update(label=f"โŒ Failed (Exit: {exit_code})", state="error") error_msg = f"Standard Output:\n{stdout}\n\nStandard Error:\n{stderr}" st.error(f"Execution Failed:\n{stderr}") st.session_state.terminal_log += f"\n[AUTO-AGENT ERROR] {filename}\n{stderr}\n" # 5. Loop Back feedback = f"The code you wrote in {filename} failed with exit code {exit_code}.\n\nOUTPUT:\n{stdout}\n\nERROR:\n{stderr}\n\nPlease fix the code and output the full corrected script." history.append({"role": "user", "content": feedback}) st.write("๐Ÿ”„ Looping for Fix...") time.sleep(1) st.error("โŒ Max retries reached. Agent could not solve the problem.") def run_chat_thread(model_name, container, ctx): # Manually attach context to this thread add_script_run_ctx(ctx=ctx) with container: if arena_mode and not is_triggered_run: st.markdown(f"
๐Ÿ‘ค {prompt}
", unsafe_allow_html=True) msg_placeholder = st.empty() full_response = "" bot_style = "style='float:none; margin-right:auto; margin-left:0;'" if arena_mode else "" try: # Prepare Context model_history = [ m for m in st.session_state.messages if m["role"] == "user" or (m["role"] == "assistant" and (m.get("model") == model_name or m.get("model") is None)) ] # INJECT FILE CONTEXT IF AVAILABLE if st.session_state.file_context: # We inject it as a system message at the start, or append to the last user message if no system support? # System message is cleaner. context_msg = f"CONTEXT FROM UPLOADED FILE:\n{st.session_state.file_context}\n\nUSER QUERY:\n" # For RAG, it's often better to prepend to the latest user prompt or add as a system instruction. # Let's add as a system instruction if the model supports it, or prepend to the last user message. # Simpler approach: Prepend to the first message if it's user, or insert system message. # We will insert a system message at index 0. model_history.insert(0, {"role": "system", "content": f"You have access to the following file content. Use it to answer questions if relevant:\n\n{st.session_state.file_context}"}) # ROUTE 1: HUGGING FACE if model_name in hf_models: hf_token = os.environ.get("HF_TOKEN") if not hf_token: raise Exception("HF_TOKEN not found.") api_url = "https://router.huggingface.co/v1/chat/completions" headers = {"Authorization": f"Bearer {hf_token}"} payload = {"model": model_name, "messages": model_history, "stream": True, "max_tokens": 1024} response = requests.post(api_url, headers=headers, json=payload, stream=True) for line in response.iter_lines(): if not line: continue if line.startswith(b"data: "): line_data = line.decode("utf-8").lstrip("data: ").strip() if line_data == "[DONE]": break try: chunk = json.loads(line_data) content = chunk["choices"][0]["delta"].get("content", "") if content: full_response += content msg_placeholder.markdown(f"
๐Ÿค– {full_response}โ–Œ
", unsafe_allow_html=True) except: pass # ROUTE 2: CLOUD DOLPHIN elif model_name == cloud_dolphin: api_url = "https://chat.dphn.ai/api/chat" payload = {"messages": model_history, "model": "dolphinserver:24B", "template": template_mode} headers = {"Content-Type": "application/json"} response = requests.post(api_url, json=payload, headers=headers, stream=True) for line in response.iter_lines(): if not line: continue line_text = line.decode("utf-8") if line_text.startswith("data: "): json_str = line_text[6:] if json_str.strip() == "[DONE]": break try: chunk = json.loads(json_str) if "choices" in chunk and len(chunk["choices"]) > 0: content = chunk["choices"][0]["delta"].get("content", "") if content: full_response += content msg_placeholder.markdown(f"
๐Ÿค– {full_response}โ–Œ
", unsafe_allow_html=True) except: pass # ROUTE 3: LOCAL OLLAMA else: current_msgs = model_history.copy() # If file context was already inserted into model_history, we are good. # But if template_mode is not creative, we might have conflicting system prompts. # Let's just append the template mode system prompt if needed. if template_mode != "creative": # Check if we already have a system prompt (from file) if current_msgs and current_msgs[0]["role"] == "system": current_msgs[0]["content"] += f"\n\nAlso, act as a {template_mode} assistant." else: current_msgs.insert(0, {"role": "system", "content": f"You are a {template_mode} assistant."}) response = requests.post( "http://localhost:11434/api/chat", json={"model": model_name, "messages": current_msgs, "stream": True}, stream=True ) for line in response.iter_lines(): if line: data = json.loads(line.decode("utf-8")) if "message" in data: content = data["message"]["content"] full_response += content msg_placeholder.markdown(f"
๐Ÿค– {full_response}โ–Œ
", unsafe_allow_html=True) # Final Render & Return msg_placeholder.markdown(f"
๐Ÿค– {full_response}
", unsafe_allow_html=True) # --- AGENT: Auto-Save Code --- if agent_mode: blocks = extract_code_blocks(full_response) for block in blocks: code = block['code'] lang = block['language'] # Guess Filename filename = parse_filename_from_code(code) if not filename: ext = "py" if lang == "python" else "sh" if lang in ["bash", "sh"] else "txt" filename = f"script_{int(time.time())}.{ext}" st.session_state.workspace.save_file(filename, code) try: st.toast(f"๐Ÿ’พ Saved: {filename}", icon="โœ…") except: pass # ----------------------------- return {"role": "assistant", "content": full_response, "model": model_name} except Exception as e: msg_placeholder.error(f"โš ๏ธ Error: {e}") return None # Run Threads with ThreadPoolExecutor(max_workers=len(selected_models)) as executor: futures = [] for model, container in zip(selected_models, live_containers): if agent_mode and auto_gpt_mode: # Autonomous Loop future = executor.submit(run_autonomous_loop, model, prompt, container, main_ctx) else: # Standard Chat future = executor.submit(run_chat_thread, model, container, main_ctx) futures.append(future) # Wait for all to complete for future in futures: try: result = future.result() # Autonomous loop handles its own history/display, so we might return None or handle differently. # Standard chat returns a dict to append. if result and not auto_gpt_mode: st.session_state.messages.append(result) except Exception as e: st.error(f"Thread failed: {e}") if not auto_gpt_mode: save_memory(st.session_state.messages) # --- AGENT: Cross-Review Trigger --- if agent_mode and not auto_gpt_mode and len(selected_models) > 1: with main_chat_container: if st.button("๐Ÿ” Peer Review"): # Trigger a review by the other model # We simply append a user message requesting review and rerun # Ideally, we target the *other* model, but our current system broadcasts. # We can craft a prompt: "Model [Name], please review the above." review_prompt = "Please review the code and response generated above. Check for errors, security issues, and suggest improvements." st.session_state.messages.append({"role": "user", "content": review_prompt}) save_memory(st.session_state.messages) st.session_state.trigger_gen = True st.rerun()