app.py

import gradio as gr
import asyncio
import json
import logging
import traceback
import os
import torch
import numpy as np
import pandas as pd  # <-- ADDED
from datetime import datetime

# ARF components
from agentic_reliability_framework.runtime.engine import EnhancedReliabilityEngine
from agentic_reliability_framework.core.models.event import ReliabilityEvent

# Custom AI components
from ai_event import AIEvent
from ai_risk_engine import AIRiskEngine
from hallucination_detective import HallucinationDetectiveAgent
from memory_drift_diagnostician import MemoryDriftDiagnosticianAgent
from nli_detector import NLIDetector
from retrieval import SimpleRetriever
from image_detector import ImageQualityDetector
from audio_detector import AudioQualityDetector
from iot_simulator import IoTSimulator
from robotics_diagnostician import RoboticsDiagnostician
from iot_event import IoTEvent

# ----------------------------------------------------------------------
# Logging setup
# ----------------------------------------------------------------------
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# ----------------------------------------------------------------------
# ARF infrastructure engine (optional)
# ----------------------------------------------------------------------
try:
    logger.info("Initializing EnhancedReliabilityEngine...")
    infra_engine = EnhancedReliabilityEngine()
except Exception as e:
    logger.error(f"Infrastructure engine init failed: {e}")
    infra_engine = None

# ----------------------------------------------------------------------
# Text generation model (DialoGPT-small) with logprobs
# ----------------------------------------------------------------------
from transformers import AutoTokenizer, AutoModelForCausalLM
gen_model_name = "microsoft/DialoGPT-small"
try:
    tokenizer = AutoTokenizer.from_pretrained(gen_model_name)
    model = AutoModelForCausalLM.from_pretrained(gen_model_name)
    model.eval()
    logger.info(f"Generator {gen_model_name} loaded.")
except Exception as e:
    logger.error(f"Generator load failed: {e}")
    tokenizer = model = None

def generate_with_logprobs(prompt, max_new_tokens=100):
    """Generate text and return (generated_text, avg_log_prob)."""
    if tokenizer is None or model is None:
        return "[Model not loaded]", -10.0
    inputs = tokenizer(prompt, return_tensors="pt")
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            return_dict_in_generate=True,
            output_scores=True
        )
    scores = outputs.scores
    log_probs = [torch.log_softmax(score, dim=-1) for score in scores]
    generated_ids = outputs.sequences[0][inputs['input_ids'].shape[1]:]
    token_log_probs = []
    for i, lp in enumerate(log_probs):
        token_id = generated_ids[i]
        token_log_probs.append(lp[0, token_id].item())
    avg_log_prob = sum(token_log_probs) / len(token_log_probs) if token_log_probs else -10.0
    generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
    return generated_text, avg_log_prob

# ----------------------------------------------------------------------
# NLI detector
# ----------------------------------------------------------------------
nli_detector = NLIDetector()  # Note: this file will be updated separately

# ----------------------------------------------------------------------
# Retrieval (sentence‑transformers + ChromaDB)
# ----------------------------------------------------------------------
retriever = SimpleRetriever()

# ----------------------------------------------------------------------
# Image generation (tiny diffusion model)
# ----------------------------------------------------------------------
from diffusers import StableDiffusionPipeline
image_pipe = None
try:
    image_pipe = StableDiffusionPipeline.from_pretrained(
        "hf-internal-testing/tiny-stable-diffusion-torch"
    )
    if not torch.cuda.is_available():
        image_pipe.to("cpu")
    logger.info("Image pipeline loaded.")
except Exception as e:
    logger.warning(f"Image pipeline load failed (will be disabled): {e}")

# ----------------------------------------------------------------------
# Audio transcription (Whisper tiny)
# ----------------------------------------------------------------------
from transformers import pipeline
audio_pipe = None
try:
    audio_pipe = pipeline(
        "automatic-speech-recognition",
        model="openai/whisper-tiny.en",
        device=0 if torch.cuda.is_available() else -1
    )
    logger.info("Audio pipeline loaded.")
except Exception as e:
    logger.warning(f"Audio pipeline load failed (will be disabled): {e}")

# ----------------------------------------------------------------------
# AI agents
# ----------------------------------------------------------------------
hallucination_detective = HallucinationDetectiveAgent(nli_detector=nli_detector)
memory_drift_diagnostician = MemoryDriftDiagnosticianAgent()
image_quality_detector = ImageQualityDetector()
audio_quality_detector = AudioQualityDetector()
robotics_diagnostician = RoboticsDiagnostician()

# ----------------------------------------------------------------------
# Bayesian risk engine
# ----------------------------------------------------------------------
ai_risk_engine = AIRiskEngine()

# ----------------------------------------------------------------------
# IoT simulator
# ----------------------------------------------------------------------
iot_sim = IoTSimulator()

# ----------------------------------------------------------------------
# Helper: update risk with feedback (global state – shared across users)
# For per‑session risk, use gr.State instead of globals.
# ----------------------------------------------------------------------
last_task_category = None

def feedback(thumbs_up: bool):
    """Handle user feedback to update Beta priors."""
    global last_task_category
    if last_task_category is None:
        return "No previous analysis to rate."
    ai_risk_engine.update_outcome(last_task_category, success=thumbs_up)
    return f"Feedback recorded: {'👍' if thumbs_up else '👎'} for {last_task_category}."

# ----------------------------------------------------------------------
# Async handlers for each tab
# ----------------------------------------------------------------------
async def handle_text(task_type, prompt):
    """Handle text generation and analysis."""
    global last_task_category
    last_task_category = task_type
    try:
        response, avg_log_prob = generate_with_logprobs(prompt)
        retrieval_score = retriever.get_similarity(prompt)
        event = AIEvent(
            timestamp=datetime.utcnow(),
            component="ai",
            service_mesh="ai",
            latency_p99=0,
            error_rate=0.0,
            throughput=1,
            cpu_util=None,
            memory_util=None,
            action_category=task_type,
            model_name=gen_model_name,
            model_version="latest",
            prompt=prompt,
            response=response,
            response_length=len(response),
            confidence=float(np.exp(avg_log_prob)),  # convert to [0,1] scale (approx)
            perplexity=None,
            retrieval_scores=[retrieval_score],
            user_feedback=None,
            latency_ms=0
        )
        hallu_result = await hallucination_detective.analyze(event)
        drift_result = await memory_drift_diagnostician.analyze(event)
        risk_metrics = ai_risk_engine.risk_score(task_type)
        return {
            "response": response,
            "avg_log_prob": avg_log_prob,
            "confidence": event.confidence,
            "retrieval_score": retrieval_score,
            "hallucination_detection": hallu_result,
            "memory_drift_detection": drift_result,
            "risk_metrics": risk_metrics
        }
    except Exception as e:
        logger.error(f"Text task error: {e}")
        return {"error": str(e)}

async def handle_image(prompt, steps):
    """Handle image generation with configurable steps. Returns (image, json_data)."""
    global last_task_category
    last_task_category = "image"
    if image_pipe is None:
        return None, {"error": "Image model not loaded"}
    try:
        import time
        start = time.time()
        image = image_pipe(prompt, num_inference_steps=steps).images[0]
        gen_time = time.time() - start
        retrieval_score = retriever.get_similarity(prompt)
        event = AIEvent(
            timestamp=datetime.utcnow(),
            component="image",
            service_mesh="ai",
            latency_p99=0,
            error_rate=0.0,
            throughput=1,
            cpu_util=None,
            memory_util=None,
            action_category="image",
            model_name="tiny-sd",
            model_version="latest",
            prompt=prompt,
            response="",  # not text
            response_length=0,
            confidence=1.0 / (gen_time + 1),  # heuristic
            perplexity=None,
            retrieval_scores=[retrieval_score, gen_time],
            user_feedback=None,
            latency_ms=gen_time * 1000
        )
        quality_result = await image_quality_detector.analyze(event)
        json_data = {
            "generation_time": gen_time,
            "retrieval_score": retrieval_score,
            "quality_detection": quality_result
        }
        return image, json_data
    except Exception as e:
        logger.error(f"Image task error: {e}")
        return None, {"error": str(e)}

async def handle_audio(audio_file):
    """Handle audio transcription and quality analysis."""
    global last_task_category
    last_task_category = "audio"
    if audio_pipe is None:
        return {"error": "Audio model not loaded"}
    try:
        import librosa
        import soundfile as sf
        import tempfile
        audio, sr = librosa.load(audio_file, sr=16000)
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
            sf.write(tmp.name, audio, sr)
            result = audio_pipe(tmp.name, return_timestamps=False)
        text = result["text"]
        # Whisper does not output log probs easily; use placeholder
        avg_log_prob = -2.0
        event = AIEvent(
            timestamp=datetime.utcnow(),
            component="audio",
            service_mesh="ai",
            latency_p99=0,
            error_rate=0.0,
            throughput=1,
            cpu_util=None,
            memory_util=None,
            action_category="audio",
            model_name="whisper-tiny.en",
            model_version="latest",
            prompt="",  # audio file path
            response=text,
            response_length=len(text),
            confidence=float(np.exp(avg_log_prob)),
            perplexity=None,
            retrieval_scores=[avg_log_prob],
            user_feedback=None,
            latency_ms=0
        )
        quality_result = await audio_quality_detector.analyze(event)
        return {
            "transcription": text,
            "avg_log_prob": avg_log_prob,
            "confidence": event.confidence,
            "quality_detection": quality_result
        }
    except Exception as e:
        logger.error(f"Audio task error: {e}")
        return {"error": str(e)}

# MODIFIED: accept session state, return updated state and DataFrame
async def read_iot_sensors(fault_type, history_state):
    """Read simulated IoT sensors, run diagnostics, predict failure, and return updated plot data."""
    global last_task_category
    last_task_category = "iot"
    iot_sim.set_fault(fault_type if fault_type != "none" else None)
    data = iot_sim.read()
    history_state.append(data)
    if len(history_state) > 100:
        history_state.pop(0)

    # Create IoTEvent with valid component name
    event = IoTEvent(
        timestamp=datetime.utcnow(),
        component="robotic-arm",
        service_mesh="factory",
        latency_p99=0,
        error_rate=0.0,
        throughput=1,
        cpu_util=None,
        memory_util=None,
        temperature=data['temperature'],
        vibration=data['vibration'],
        motor_current=data['motor_current'],
        position_error=data['position_error']
    )
    diag_result = await robotics_diagnostician.analyze(event)

    # Simple failure prediction
    prediction = None
    if len(history_state) >= 5:
        temps = [h['temperature'] for h in history_state[-5:]]
        x = np.arange(len(temps))
        slope, intercept = np.polyfit(x, temps, 1)
        next_temp = slope * len(temps) + intercept
        if slope > 0.1:
            time_to_threshold = (40.0 - next_temp) / slope if slope > 0 else None
            prediction = {
                "predicted_temperature": next_temp,
                "time_to_overheat_min": time_to_threshold
            }

    # Prepare temperature history for plotting as DataFrame
    temp_history = [h['temperature'] for h in history_state[-20:]]  # last 20 readings
    df = pd.DataFrame({
        "index": list(range(len(temp_history))),
        "temperature": temp_history
    })

    return data, diag_result, prediction, df, history_state  # return updated state

# ----------------------------------------------------------------------
# Gradio UI
# ----------------------------------------------------------------------
with gr.Blocks(title="ARF v4 – AI Reliability Lab", theme="soft") as demo:
    gr.Markdown("# 🧠 ARF v4 – AI Reliability Lab\n**Detect hallucinations, drift, and failures across text, image, audio, and robotics**")

    with gr.Tabs():
        # Tab 1: Text Generation
        with gr.TabItem("Text Generation"):
            text_task = gr.Dropdown(["chat", "code", "summary"], value="chat", label="Task")
            text_prompt = gr.Textbox(label="Prompt", value="What is the capital of France?", lines=3)
            text_btn = gr.Button("Generate")
            text_output = gr.JSON(label="Analysis")

        # Tab 2: Image Generation (enhanced with steps slider)
        with gr.TabItem("Image Generation"):
            img_prompt = gr.Textbox(label="Prompt", value="A cat wearing a hat")
            img_steps = gr.Slider(1, 10, value=2, step=1, label="Inference Steps (higher = better quality, slower)")
            img_btn = gr.Button("Generate")
            img_output = gr.Image(label="Generated Image")
            img_json = gr.JSON(label="Analysis")

        # Tab 3: Audio Transcription
        with gr.TabItem("Audio Transcription"):
            # Add a sample audio button for quick testing
            gr.Markdown("Click the microphone to record, or upload a file. Try the sample: [Sample Audio](https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac)")
            audio_input = gr.Audio(type="filepath", label="Upload audio file")
            audio_btn = gr.Button("Transcribe")
            audio_output = gr.JSON(label="Analysis")

        # Tab 4: Robotics / IoT (enhanced with live plot)
        with gr.TabItem("Robotics / IoT"):
            gr.Markdown("### Simulated Robotic Arm Monitoring")
            iot_state = gr.State(value=[])  # per-session history state

            with gr.Row():
                with gr.Column():
                    fault_type = gr.Dropdown(
                        ["none", "overheat", "vibration", "stall", "drift"],
                        value="none",
                        label="Inject Fault"
                    )
                    refresh_btn = gr.Button("Read Sensors")
                with gr.Column():
                    sensor_display = gr.JSON(label="Sensor Readings")
            with gr.Row():
                with gr.Column():
                    diag_display = gr.JSON(label="Diagnosis")
                with gr.Column():
                    pred_display = gr.JSON(label="Failure Prediction")
            with gr.Row():
                with gr.Column(scale=1, min_width=600):  # Controls width, height will be automatic
                    temp_plot = gr.LinePlot(
                        label="Temperature History (last 20 readings)",
                        x="index",
                        y="temperature"
                    )

        # Tab 5: Enterprise – Marketing and Sales
        with gr.TabItem("Enterprise"):
            gr.Markdown("""
            ## 🚀 ARF Enterprise – Governed Execution for Autonomous Infrastructure

            Take ARF to production with enterprise‑grade safety, compliance, and learning.

            ### Key Enterprise Features:
            - **Autonomous Execution** – Deterministic, policy‑controlled healing actions.
            - **Audit Trails & Compliance** – Full traceability for SOC2, HIPAA, GDPR.
            - **Learning Loops** – Models improve over time with your data.
            - **Multi‑Tenant Control** – Role‑based access and isolation.
            - **Cloud Integrations** – Azure, AWS, GCP native clients.
            - **24/7 Support & SLAs** – Enterprise‑grade reliability.

            ### Get Started
            - 📅 [Book a Demo](https://calendly.com/petter2025us/30min)
            - 📧 [Contact Sales](mailto:petter2025us@outlook.com)
            - 📄 [Download Datasheet](#) (coming soon)

            *Already using ARF OSS? Upgrade seamlessly – same core, governed execution.*
            """)

    # Feedback row (shared across all users – for demo purposes)
    with gr.Row():
        feedback_up = gr.Button("👍 Correct")
        feedback_down = gr.Button("👎 Incorrect")
        feedback_msg = gr.Textbox(label="Feedback", interactive=False)

    # Wire events
    text_btn.click(
        fn=lambda task, p: asyncio.run(handle_text(task, p)),
        inputs=[text_task, text_prompt],
        outputs=text_output
    )
    img_btn.click(
        fn=lambda p, s: asyncio.run(handle_image(p, s)),
        inputs=[img_prompt, img_steps],
        outputs=[img_output, img_json]
    )
    audio_btn.click(
        fn=lambda f: asyncio.run(handle_audio(f)),
        inputs=audio_input,
        outputs=audio_output
    )
    # MODIFIED: include state as input and output
    refresh_btn.click(
        fn=lambda f, h: asyncio.run(read_iot_sensors(f, h)),
        inputs=[fault_type, iot_state],
        outputs=[sensor_display, diag_display, pred_display, temp_plot, iot_state]
    )
    feedback_up.click(fn=lambda: feedback(True), outputs=feedback_msg)
    feedback_down.click(fn=lambda: feedback(False), outputs=feedback_msg)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)