Create Finn.py

FINN!

Signed-off-by: Josef Kurk Edwards <joed6834@colorado.edu>

Finn.py

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+from fastapi import FastAPI, Depends, HTTPException
+from pydantic import BaseModel
+import pickle
+from typing import List
+import json
+
+# Initialize FastAPI
+app = FastAPI()
+
+# ---- Core Brain Components (PMLL, ARLL, EFLL) ----
+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
+
+
+class ARLL:
+    """Adaptive Reinforcement Learning for model improvement."""
+    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):
+        # Simulated model improvement logic
+        print(f"Retraining model with {len(self.feedback_data)} feedback entries.")
+
+
+class EFLL:
+    """Emotional Feedback Learning for empathetic responses."""
+    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 Framework Components ----
+pmll_memory = PMLLMemory()
+arll = ARLL(model=None)  # Model will be loaded dynamically
+efll = EFLL()
+
+# ---- Model Loading ----
+def load_model():
+    try:
+        with open("sentiment_model.pkl", "rb") as f:
+            return pickle.load(f)
+    except FileNotFoundError:
+        raise HTTPException(status_code=500, detail="Model file not found.")
+
+@app.on_event("startup")
+def startup_event():
+    global model
+    model = load_model()
+    arll.model = model  # Link the loaded model to ARLL
+
+
+# ---- FastAPI Endpoints ----
+class TextInput(BaseModel):
+    text: str
+
+class FeedbackInput(BaseModel):
+    text: str
+    true_sentiment: str
+
+@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)  # Store in PMLL
+        emotional_feedback = efll.provide_emotional_feedback(prediction)  # Generate EFLL response
+        return {"text": input.text, "sentiment": prediction, "emotional_feedback": emotional_feedback}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/feedback")
+async def feedback(input: FeedbackInput):
+    arll.record_feedback(input.text, input.true_sentiment)
+    pmll_memory.save(input.text, None, feedback=input.true_sentiment)  # Log feedback in PMLL
+    return {"message": "Feedback recorded. Thank you!"}
+
+@app.post("/retrain")
+async def retrain_model():
+    arll.retrain_model()
+    return {"message": "Model retrained successfully."}
+
+@app.get("/history")
+async def get_history():
+    return pmll_memory.get_history()
+
+# ---- Finn Initialization ----
+@app.get("/")
+async def root():
+    return {"message": "Welcome to Finn, the Brain of ChatGPT and OpenAI."}