app.py

import os
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer
import faiss
import pandas as pd
import os
import logging
from groq import Groq
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# ✅ Set a writable cache directory
os.environ["HF_HOME"] = "/tmp/huggingface"
os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp/huggingface"
# ✅ Initialize FastAPI
app = FastAPI()

# ✅ Securely Fetch API Key
GROQ_API_KEY = os.getenv("GROQ_API_KEY")  # ✅ FIXED
if not GROQ_API_KEY:
    raise ValueError("GROQ_API_KEY is missing. Set it as an environment variable.")

client = Groq(api_key=GROQ_API_KEY)  # ✅ Ensure the API key is passed correctly


# ✅ Load AI Models (Now uses /tmp/huggingface as cache)
similarity_model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2", cache_folder="/tmp/huggingface")
embedding_model = SentenceTransformer("all-MiniLM-L6-v2", cache_folder="/tmp/huggingface")
summarization_model = AutoModelForSeq2SeqLM.from_pretrained("google/long-t5-tglobal-base", cache_dir="/tmp/huggingface")
summarization_tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-base", cache_dir="/tmp/huggingface")

# ✅ Check if files exist before loading
print("🔍 Available Files:", os.listdir("."))  # This will log available files

# ✅ Load datasets with error handling
try:
    recommendations_df = pd.read_csv("treatment_recommendations .csv")
    questions_df = pd.read_csv("symptom_questions.csv")
except FileNotFoundError as e:
    logging.error(f"❌ Missing dataset file: {e}")
    raise HTTPException(status_code=500, detail=f"Dataset file not found: {str(e)}")

# ✅ 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

# ✅ Retrieve the most relevant question
def retrieve_questions(user_input):
    """Retrieve the most relevant individual diagnostic question using FAISS."""
    input_embedding = embedding_model.encode([user_input], convert_to_numpy=True)
    _, indices = question_index.search(input_embedding, 1)  # ✅ Retrieve only 1 question

    if indices[0][0] == -1:
        return "I'm sorry, I couldn't find a relevant question."

    # ✅ Extract only the first meaningful question
    question_block = questions_df["Questions"].iloc[indices[0][0]]
    split_questions = question_block.split(", ")  
    best_question = split_questions[0] if split_questions else question_block  # ✅ Select the first clear question

    return best_question  # ✅ Return a single question as a string

# ✅ Groq API for rephrasing
def generate_empathetic_response(user_input, retrieved_question):
    """Use Groq API (LLaMA-3) to generate one empathetic response."""
    
    # ✅ Improved Prompt: Only One Question
    prompt = f"""
    The user said: "{user_input}"
    
    Relevant Question:
    - {retrieved_question}

    You are an empathetic AI psychiatrist. Rephrase this question naturally in a human-like way.
    Acknowledge the user's emotions before asking the question.

    Example format:
    - "I understand that anxiety can be overwhelming. Can you tell me more about when you started feeling this way?"
    
    Generate only one empathetic response.
    """

    try:
        chat_completion = client.chat.completions.create(
            messages=[
                {"role": "system", "content": "You are a helpful, empathetic AI psychiatrist."},
                {"role": "user", "content": prompt}
            ],
            model="llama-3.3-70b-versatile",  # ✅ Use Groq's LLaMA-3 Model
            temperature=0.8,
            top_p=0.9
        )
        return chat_completion.choices[0].message.content  # ✅ Return only one response
    except Exception as e:
        logging.error(f"Groq API error: {e}")
        return "I'm sorry, I couldn't process your request."

# ✅ API Endpoint: Get Empathetic Questions (Hybrid RAG)
@app.post("/get_questions")
def get_recommended_questions(request: ChatRequest):
    """Retrieve the most relevant diagnostic question and make it more empathetic using Groq API."""
    retrieved_question = retrieve_questions(request.message)
    empathetic_response = generate_empathetic_response(request.message, retrieved_question)
    
    return {"question": empathetic_response}

# ✅ API Endpoint: Summarize Chat
@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}

# ✅ API Endpoint: Detect Disorders
@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)

    if indices[0][0] == -1:
        return {"disorders": "No matching disorder found."}

    disorders = [recommendations_df["Disorder"].iloc[i] for i in indices[0]]
    return {"disorders": disorders}

# ✅ API Endpoint: Get Treatment Recommendations
@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 if disorder in recommendations_df["Disorder"].values
    }
    return {"treatments": treatments}