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b0b8d4d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | import numpy as np
from collections import deque
import gradio as gr
# ----------------------------------
# Dosha Agent Class
# ----------------------------------
class DoshaStateTrackingAgent:
def __init__(
self,
initial_state,
initial_confidence=0.85,
alpha=0.6,
history_window=7
):
self.alpha = alpha
self.state = initial_state.copy()
self.confidence = initial_confidence
self.baseline = initial_state.copy()
self.history = deque(maxlen=history_window)
self.trend_history = deque(maxlen=3)
def _normalize(self, obs):
total = sum(obs.values())
return {k: v / total for k, v in obs.items()}
def observe(self, observation):
obs = self._normalize(observation)
self.history.append(obs)
return obs
def update_state(self, obs):
for d in self.state:
self.state[d] = (
self.alpha * self.state[d] +
(1 - self.alpha) * obs[d]
)
self._update_confidence()
return self.state
def _update_confidence(self):
variance = np.var(list(self.state.values()))
self.confidence = max(0.4, min(0.95, 1 - variance))
def compute_imbalance(self):
imbalance = {
d: abs(self.state[d] - self.baseline[d])
for d in self.state
}
severity = self._bucket_severity(max(imbalance.values()))
return imbalance, severity
def _bucket_severity(self, value):
if value < 0.05:
return "mild"
elif value < 0.12:
return "moderate"
else:
return "severe"
def detect_trends(self):
dominant = max(self.state, key=self.state.get)
self.trend_history.append(dominant)
if len(self.trend_history) < 3:
return "stable"
if len(set(self.trend_history)) == 1:
return f"{dominant}_rising"
return "mixed"
def generate_triggers(self, severity, trend):
triggers = []
if severity == "severe":
triggers.append("high_imbalance_alert")
if "rising" in trend:
triggers.append(f"{trend}_3_days")
return triggers
def step(self, observation):
obs = self.observe(observation)
state = self.update_state(obs)
imbalance, severity = self.compute_imbalance()
trend = self.detect_trends()
triggers = self.generate_triggers(severity, trend)
return {
"State": state,
"Imbalance": imbalance,
"Severity": severity,
"Trend": trend,
"Confidence": round(self.confidence, 3),
"Triggers": triggers
}
# ----------------------------------
# Global Agent (persistent state)
# ----------------------------------
agent = DoshaStateTrackingAgent(
initial_state={"vata": 0.4, "pitta": 0.35, "kapha": 0.25}
)
# ----------------------------------
# Function for UI
# ----------------------------------
def predict(vata, pitta, kapha):
obs = {"vata": vata, "pitta": pitta, "kapha": kapha}
output = agent.step(obs)
return (
str(output["State"]),
str(output["Imbalance"]),
output["Severity"],
output["Trend"],
output["Confidence"],
str(output["Triggers"])
)
# ----------------------------------
# Gradio UI
# ----------------------------------
iface = gr.Interface(
fn=predict,
inputs=[
gr.Slider(0, 1, value=0.5, label="Vata"),
gr.Slider(0, 1, value=0.3, label="Pitta"),
gr.Slider(0, 1, value=0.2, label="Kapha"),
],
outputs=[
gr.Textbox(label="State Vector"),
gr.Textbox(label="Imbalance"),
gr.Textbox(label="Severity"),
gr.Textbox(label="Trend"),
gr.Textbox(label="Confidence"),
gr.Textbox(label="Triggers"),
],
title="🧠 Dosha State Tracking Agent",
description="Track Vata, Pitta, Kapha changes over time using EMA-based AI agent"
)
iface.launch() |