FastAPI.py

from fastapi import FastAPI, Depends, HTTPException
from pydantic import BaseModel
import pickle
from typing import List
import json

# Load model at application start
def load_model():
    with open("sentiment_model.pkl", "rb") as f:
        return pickle.load(f)

# Initialize FastAPI app
app = FastAPI()

# Define PMLL components
class PMLLMemory:
    """Persistent Memory to store historical data."""
    def __init__(self):
        self.history = []

    def save(self, input_text, prediction, feedback=None):
        entry = {"text": input_text, "prediction": prediction, "feedback": feedback}
        self.history.append(entry)

    def get_history(self):
        return self.history

# Define ARLL components
class ARLL:
    """Reinforcement Learning to improve the model."""
    def __init__(self, model):
        self.model = model
        self.feedback_data = []

    def record_feedback(self, input_text, true_sentiment):
        self.feedback_data.append((input_text, true_sentiment))

    def retrain_model(self):
        # Simulate model improvement using feedback (this part should be replaced with real retraining logic)
        print(f"Retraining model with {len(self.feedback_data)} new data points.")
        # Example: Add data to the training set and re-fit the model here.

# Define EFLL components
class EFLL:
    """Emotional Feedback Learning Loop."""
    def provide_emotional_feedback(self, sentiment):
        feedback_map = {
            "positive": "That's great to hear!",
            "negative": "I'm sorry to hear that. Let me know how I can help.",
            "neutral": "Thanks for sharing your thoughts."
        }
        return feedback_map.get(sentiment, "Thank you for your input.")

# Initialize frameworks
pmll_memory = PMLLMemory()
arll = ARLL(model=load_model())
efll = EFLL()

# Define input schema
class TextInput(BaseModel):
    text: str

class FeedbackInput(BaseModel):
    text: str
    true_sentiment: str

# Load the model as a dependency
@app.on_event("startup")
def on_startup():
    global model
    model = load_model()

# Prediction endpoint
@app.post("/predict")
async def predict(input: TextInput, model=Depends(load_model)):
    try:
        prediction = model.predict([input.text])[0]
        pmll_memory.save(input.text, prediction)  # Save to PMLL memory
        emotional_feedback = efll.provide_emotional_feedback(prediction)  # Generate emotional feedback
        return {"text": input.text, "sentiment": prediction, "emotional_feedback": emotional_feedback}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

# Feedback endpoint for ARLL
@app.post("/feedback")
async def feedback(input: FeedbackInput):
    try:
        arll.record_feedback(input.text, input.true_sentiment)
        pmll_memory.save(input.text, None, feedback=input.true_sentiment)  # Save feedback to PMLL memory
        return {"message": "Feedback recorded. Thank you!"}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

# Retrain model with ARLL
@app.post("/retrain")
async def retrain_model():
    try:
        arll.retrain_model()
        return {"message": "Model retrained with feedback data."}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

# History endpoint
@app.get("/history")
async def get_history():
    return pmll_memory.get_history()