from fastapi import FastAPI
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer
import faiss
import pandas as pd
import random
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

app = FastAPI()

# Load AI Models
similarity_model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
summarization_model = AutoModelForSeq2SeqLM.from_pretrained("google/long-t5-tglobal-base")
summarization_tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-base")

# Load datasets
recommendations_df = pd.read_csv("treatment_recommendations.csv")
questions_df = pd.read_csv("symptom_questions.csv")

# FAISS Index for disorder detection
treatment_embeddings = similarity_model.encode(recommendations_df["Disorder"].tolist(), convert_to_numpy=True)
index = faiss.IndexFlatIP(treatment_embeddings.shape[1])
index.add(treatment_embeddings)

# FAISS Index for Question Retrieval
question_embeddings = embedding_model.encode(questions_df["Questions"].tolist(), convert_to_numpy=True)
question_index = faiss.IndexFlatL2(question_embeddings.shape[1])
question_index.add(question_embeddings)

# Request Model
class ChatRequest(BaseModel):
    message: str

class SummaryRequest(BaseModel):
    chat_history: list  # List of messages

@app.post("/get_questions")
def get_recommended_questions(request: ChatRequest):
    """Retrieve the most relevant diagnostic questions with a conversational response."""
    
    # Step 1: Encode the input message for FAISS search
    input_embedding = embedding_model.encode([request.message], convert_to_numpy=True)
    distances, indices = question_index.search(input_embedding, 3)

    # Step 2: Retrieve the top 3 relevant questions
    retrieved_questions = [questions_df["Questions"].iloc[i] for i in indices[0]]

    # Step 3: Define Dynamic Prompt Variations
    empathetic_phrases = [
        "I hear you, and I appreciate you sharing this. Let me ask:",
        "That sounds challenging. Could you help me understand better by answering this:",
        "I understand what you're going through. Here's something to think about:",
        "Thank you for opening up. Let’s explore this further:",
        "Your feelings are completely valid. Here's a question to help us understand more:"
    ]

    # Step 4: Generate Dynamic Responses
    wrapped_responses = [
        f"{random.choice(empathetic_phrases)} *{q}*"
        for q in retrieved_questions
    ]

    return {"questions": wrapped_responses}

@app.post("/summarize_chat")
def summarize_chat(request: SummaryRequest):
    """Summarize full chat session at the end."""
    chat_text = " ".join(request.chat_history)
    inputs = summarization_tokenizer("summarize: " + chat_text, return_tensors="pt", max_length=4096, truncation=True)
    summary_ids = summarization_model.generate(inputs.input_ids, max_length=500, num_beams=4, early_stopping=True)
    summary = summarization_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return {"summary": summary}

@app.post("/detect_disorders")
def detect_disorders(request: SummaryRequest):
    """Detect psychiatric disorders from full chat history at the end."""
    full_chat_text = " ".join(request.chat_history)
    text_embedding = similarity_model.encode([full_chat_text], convert_to_numpy=True)
    distances, indices = index.search(text_embedding, 3)
    disorders = [recommendations_df["Disorder"].iloc[i] for i in indices[0]]
    return {"disorders": disorders}

@app.post("/get_treatment")
def get_treatment(request: SummaryRequest):
    """Retrieve treatment recommendations based on detected disorders."""
    detected_disorders = detect_disorders(request)["disorders"]
    treatments = {
        disorder: recommendations_df[recommendations_df["Disorder"] == disorder]["Treatment Recommendation"].values[0]
        for disorder in detected_disorders
    }
    return {"treatments": treatments}