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import streamlit as st
import os
import json
import faiss
import numpy as np
import google.generativeai as genai
from sentence_transformers import SentenceTransformer
import requests
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
SERPER_API_KEY = os.getenv("SERPER_API_KEY")
# Configure Gemini
genai.configure(api_key=GOOGLE_API_KEY)
# Load local knowledge base
with open("data.txt", "r", encoding="utf-8") as f:
snippets = [line.strip() for line in f if line.strip()]
# Initialize embedding model and FAISS
embed_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = embed_model.encode(snippets)
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(np.array(embeddings))
def search_serper(query):
url = "https://google.serper.dev/search"
headers = {"X-API-KEY": SERPER_API_KEY}
payload = {"q": query}
res = requests.post(url, headers=headers, json=payload)
return res.json().get("organic", [])[:3]
def ask_gemini(user_input, predicted_condition, local_docs, web_results):
prompt = f"""
⚠️ *Disclaimer*: This response is not a substitute for professional medical advice.
**Patient Input**: {user_input}
**Likely Condition(s)**: {predicted_condition}
**Local Medical Knowledge**:
{chr(10).join(f"- {doc}" for doc in local_docs)}
**Web Evidence**:
{chr(10).join(f"- {r['title']}: {r['snippet']}" for r in web_results)}
Format the response using bullet points and keep it within 250 words. Cite sources at the end by title or snippet name. keep *Disclaimer*
"""
model = genai.GenerativeModel("gemini-2.0-flash-001")
response = model.generate_content(prompt)
return response.text
def predict_condition(user_input):
input_lower = user_input.lower()
if "sugar" in input_lower or "glucose" in input_lower:
return "Diabetes or Hypoglycemia"
elif "chest" in input_lower or "pain" in input_lower:
return "Myocardial Infarction or Angina"
elif "urine" in input_lower or "swelling" in input_lower:
return "AKI or CKD"
elif "heartbeat" in input_lower:
return "Arrhythmia"
return "Undetermined – please consult a doctor"
def get_local_knowledge(user_input, top_k=3):
q_emb = embed_model.encode([user_input])
D, I = index.search(np.array(q_emb), k=top_k)
return [snippets[i] for i in I[0]]
# Streamlit UI
st.title("🩺 Patient-Safety–Aware Chatbot")
st.markdown("Enter symptoms to receive safe, evidence-informed first-aid guidance.")
user_input = st.text_area("Describe symptoms here")
if st.button("Get First-Aid Guidance") and user_input:
with st.spinner("Analyzing..."):
condition = predict_condition(user_input)
local_hits = get_local_knowledge(user_input)
web_hits = search_serper(user_input)
response = ask_gemini(user_input, condition, local_hits, web_hits)
st.markdown("---")
st.subheader("🛟 First-Aid Guidance")
st.markdown(response)
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