Create app.py

app.py

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+import torch
+import pandas as pd
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+# 🔹 Initialize FastAPI
+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 (Ensure files are uploaded to Hugging Face Space)
+try:
+    recommendations_df = pd.read_csv("treatment_recommendations.csv")
+    questions_df = pd.read_csv("symptom_questions.csv")
+except FileNotFoundError:
+    recommendations_df = pd.DataFrame(columns=["Disorder", "Treatment Recommendation"])
+    questions_df = pd.DataFrame(columns=["Questions"])
+
+# 🔹 Create FAISS Index for Treatment Retrieval
+if not recommendations_df.empty:
+    treatment_embeddings = similarity_model.encode(recommendations_df["Disorder"].tolist(), convert_to_numpy=True)
+    index = faiss.IndexFlatIP(treatment_embeddings.shape[1])
+    index.add(treatment_embeddings)
+else:
+    index = None
+
+# 🔹 Create FAISS Index for Question Retrieval
+if not questions_df.empty:
+    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)
+else:
+    question_index = None
+
+# 🔹 API Request Model
+class ChatRequest(BaseModel):
+    message: str
+
+@app.post("/detect_disorders")
+def detect_disorders(request: ChatRequest):
+    """ Detect psychiatric disorders from user input """
+    if index is None:
+        return {"error": "Dataset is missing or empty"}
+    
+    text_embedding = similarity_model.encode([request.message], 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: ChatRequest):
+    """ Retrieve treatment recommendations """
+    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}
+
+@app.post("/get_questions")
+def get_recommended_questions(request: ChatRequest):
+    """Retrieve the most relevant diagnostic questions based on patient symptoms."""
+    if question_index is None:
+        return {"error": "Questions dataset is missing or empty"}
+    
+    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: ChatRequest):
+    """ Summarize chat logs using LongT5 """
+    inputs = summarization_tokenizer("summarize: " + request.message, 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}