added RBC count and quotation marls

.github/workflows/main.yml

@@ -0,0 +1,10 @@
+name: secret-test
+on: [push]
+jobs:
+  Explore-GitHub-Actions:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Hello world action
+        run: echo "JUST LOADING SECRET ONLY"
+        env: 
+          API_KEY: ${{ secrets.API_KEY }}

packages.txt

@@ -0,0 +1 @@
+libgl1-mesa-glx

requirements.txt

@@ -1,3 +1,5 @@
-altair
-pandas
+opencv_python
+paddleocr
+paddlepaddle
 streamlit
+openai

streamlit_app.py

@@ -1,40 +1,195 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import numpy as np
+import os
+import time
+import base64
+import json
+from openai import OpenAI
+import cv2
+from paddleocr import PaddleOCR
 
-"""
-# Welcome to Streamlit!
+# REMOVE THIS BEFORE COPYING TO GITHUB!
+API_KEY = os.environ['API_KEY']
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+test_attributes = {}
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+client = OpenAI(api_key=API_KEY)
+
+template_prompt = """
+Extract the items from the health screening result listed below into a json format, using the example json template. Ignore other items not listed in the json template. Output data types are "test_found" (True/False), "test_value" (float), "test_unit" (string in lower case), "test_ref_min" (float), "test_ref_max" (float). If test item is not found, output False for "test_found", and output False for the other values. If test is found but reference max, reference min, or reference range is not found, output False for "test_ref_min" and "test_ref_max". 
+
+Example output json template
+{
+	"ldl_cholesterol": {
+		"test_found":True,
+		"test_value":20,
+		"test_unit":"mmol/l",
+		"test_ref_min":False,
+		"test_ref_max":False
+		},
+	"hdl_cholesterol": {
+		"test_found":True,
+		"test_value":20,
+		"test_unit":"mmol/l",
+		"test_ref_min":False,
+		"test_ref_max":False
+		},
+	"total_cholesterol": {
+		"test_found":True,
+		"test_value":20,
+		"test_unit":"mmol/l",
+		"test_ref_min":False,
+		"test_ref_max":False
+		},
+	"mcv":{
+		"test_found":False,
+		"test_value":False,
+		"test_unit":False,
+		"test_ref_min":False,
+		"test_ref_max":False
+		},
+	"hb":{
+		"test_found":False,
+		"test_value":False,
+		"test_unit":False,
+		"test_ref_min":False,
+		"test_ref_max":False
+		},
+    "rbc_count":{
+		"test_found":False,
+		"test_value":False,
+		"test_unit":False,
+		"test_ref_min":False,
+		"test_ref_max":False
+		},
+	"uric_acid":{
+		"test_found":False,
+		"test_value":False,
+		"test_unit":False,
+		"test_ref_min":False,
+		"test_ref_max":False
+		},
+	"hba1c":{
+		"test_found":False,
+		"test_value":False,
+		"test_unit":False,
+		"test_ref_min":False,
+		"test_ref_max":False
+		},
+	"systolic_bp":{
+		"test_found":False,
+		"test_value":False,
+		"test_unit":False,
+		"test_ref_min":False,
+		"test_ref_max":False
+		},
+	"diastolic_bp":{
+		"test_found":False,
+		"test_value":False,
+		"test_unit":False,
+		"test_ref_min":False,
+		"test_ref_max":False
+		},
+	"height":{
+		"test_found":False,
+		"test_value":False,
+		"test_unit":False,
+		"test_ref_min":False,
+		"test_ref_max":False
+		},
+	"weight":{
+		"test_found":False,
+		"test_value":False,
+		"test_unit":False,
+		"test_ref_min":False,
+		"test_ref_max":False
+		}
+}
+
+Health screening result:
 """
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+def load_image():
+    uploaded_file = st.file_uploader(label='Upload your test results image below:')
+    if uploaded_file is not None:
+        st.toast("Image uploaded")  # Toast message
+        image_data = uploaded_file.getvalue()
+        st.image(image_data, caption='', use_column_width=True)  # Adjust width for mobile screens
+        return image_data
+
+st.title('Explain my test results please!')
+st.header('Instructions')
+st.markdown('Answer the questions, take a picture of your lab test results, upload it, and we will explain it to you!')
+
+# User inputs
+age = st.number_input("Enter your age", min_value=0, max_value=140, step=1,value="min")
+sex = st.selectbox("Select your sex", ["Female","Male"])
+race = st.selectbox("Select your race", ["Chinese", "Malay", "Indian", "Others"])
+smoker = st.checkbox("Are you a smoker?")
+stroke = st.checkbox("Have you ever had a stroke?")
+diabetes = st.checkbox("Do you have a history of diabetes?")
+heart_attack = st.checkbox("Have you ever had a heart attack?")
+on_bp_meds = st.checkbox("Are you taking blood pressure medication?")
+systolic_bp = st.number_input("Enter your last recorded systolic blood pressure (leave blank if not available)", min_value=50,max_value=300,value=None)
+
+ocr_model = PaddleOCR(use_angle_cls=True, lang='en')
+
+image = load_image()
+
+if st.button('Analyse my results'):
+    # Save test attributes
+    test_attributes["age"] = age
+    test_attributes["sex"] = sex
+    test_attributes["race"] = race
+    test_attributes["smoker"] = smoker #true or false
+    test_attributes["stroke"] = stroke
+    test_attributes["diabetes"] = diabetes
+    test_attributes["heart_attack"] = heart_attack
+    test_attributes["on_BP_meds"] = on_bp_meds
+    test_attributes["systolic_blood_pressure"] = systolic_bp #null or integer
+    st.json(test_attributes)
+    # Extract text from image
+    if not image: #Image not uploaded
+        st.error("Upload an image of your test results first!",icon="🚨")
+    else: # Image uploaded
+        ocr_start_time = time.time()
+        result = ocr_model.ocr(image)
+        result = result[0] #idk why this needs a result[0] instead of result for Github
+        extracted_text = ''
+        for idx in range(len(result)):
+            txt = result[idx][1][0]
+            extracted_text += txt + " "
+        ocr_end_time = time.time()
+        ocr_time = int(ocr_end_time - ocr_start_time)
+        st.markdown(extracted_text)
+        st.success(f"Processed image in {ocr_time} seconds.")  # Use status instead of toast/success
+        # Remove NRIC from extracted text
+
+        # Extract structured data from text using ChatGPT
+        # TODO: PUT TRY AND ERROR IF FAIL 
+        extract_start_time = time.time()
+        extract_prompt = f"{template_prompt} {extracted_text}"
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo-1106",
+            response_format={ "type": "json_object" },
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant designed to output JSON."},
+                {"role": "user", "content": extract_prompt}
+            ]
+        )
+        test_results = json.loads(response.choices[0].message.content)
+        st.json(test_results)
+        st.text(response.usage)
+        extract_end_time = time.time()
+        extract_time = int(extract_end_time - extract_start_time)
+        st.success(f"Extracted values in {extract_time} seconds.")  # Use status instead of toast/success
+        for test_name, test_info in test_results.items():
+            if test_info["test_found"]:
+                st.markdown(f"**Test Name:** {test_name.replace('_', ' ').upper()}")
+                st.markdown(f"**Test Value:** {test_info['test_value']} {test_info['test_unit']}")
+                #st.markdown(f"**Reference Range:** {test_info['test_ref_min']} - {test_info['test_ref_max']} {test_info['test_unit']}")
+                st.text("")
+        # Insert YT logic
+
+st.caption('Disclaimer: Not medical advice, not liable, blah')