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from fastapi import FastAPI
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer
import faiss
import pandas as pd
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."""
input_embedding = embedding_model.encode([request.message], convert_to_numpy=True)
distances, indices = question_index.search(input_embedding, 3)
retrieved_questions = [questions_df["Questions"].iloc[i] for i in indices[0]]
return {"questions": retrieved_questions}
@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}
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