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.gitignore

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+__pycache__/
+.env
+*.pyc
+.DS_Store
+
+---------------------------------------------------------------------
+# End of MedSynapticGPT codebase

README.md

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----
-title: MedSynapticGPT
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: SynestheticGPT – The Multimodal Dream
----
+# MedSynapticGPT – Multimodal Clinical Reasoner
 
-# Welcome to Streamlit!
+**Tagline:** *“Read the scan. Hear the patient. Answer like an expert.”*
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+MedSynapticGPT brings the creative power of GPT‑4o to **clinical imaging, notes, and speech**. Upload a chest X‑ray (DICOM/PNG), paste a discharge summary, or record symptoms—get structured impressions with **SNOMED‑CT codes, guideline citations, and treatment suggestions**.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+## 🚑 Core Modules
+| Tab | What it does | Key APIs |
+| --- | --- | --- |
+| **Radiology AI** | Vision interpretation, abnormality detection, TNM staging | `gpt‑4o vision`, `pydicom` |
+| **Clinical Note Q&A** | Summarize or answer free‑form questions | `gpt‑4o` |
+| **Voice Triage** | Transcribe symptoms, suggest differential | `whisper‑1`, `gpt‑4o` |
+| **UMLS Lookup** | Search concepts, synonyms, codes | UMLS REST + caching |
+| **GraphRAG Explorer** | Prototype biomedical KG Q&A | `networkx`, toy graph |
+
+## Quickstart
+```bash
+git clone <repo>
+cd MedSynapticGPT
+pip install -r requirements.txt
+export OPENAI_API_KEY="sk‑…"
+streamlit run app.py
+```
+Deploy on Spaces → add secrets. For on‑prem, launch `uvicorn api:app`.
+
+## Monetization
+1. **Freemium 3 studies/day** → Stripe pay‑per‑usage.
+2. **Pro** ($49/mo) – unlimited, PDF reports, HL7 export.
+3. **Enterprise API** – hospital integration, HIPAA BAA.
+
+---------------------------------------------------------------------

api.py

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+"""FastAPI micro‑service for enterprise EHR integration"""
+from fastapi import FastAPI, UploadFile, File, Form
+import openai, pydicom, numpy as np, io
+from PIL import Image
+import os, tempfile
+
+app = FastAPI()
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+@app.post("/radiology")
+async def radiology(file: UploadFile = File(...)):
+    data = await file.read()
+    if file.filename.endswith(".dcm"):
+        ds = pydicom.dcmread(io.BytesIO(data))
+        arr = ds.pixel_array.tolist()
+    else:
+        img = Image.open(io.BytesIO(data))
+        arr = np.array(img).tolist()
+    rpt = openai.chat.completions.create(model="gpt-4o-mini", messages=[{"role":"user","content":"Analyze this study."}], images=[arr]).choices[0].message.content
+    return {"report": rpt}
+
+---------------------------------------------------------------------

app.py

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+"""MedSynapticGPT – Streamlit UI"""
+import os, io, tempfile, json, base64
+from pathlib import Path
+import streamlit as st
+import openai, pydicom, numpy as np
+from PIL import Image
+from pydub import AudioSegment
+import requests
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+st.set_page_config(page_title="MedSynapticGPT", layout="wide")
+st.title("🩺 MedSynapticGPT – Multimodal Clinical Reasoner")
+
+if "credits" not in st.session_state:
+    st.session_state.credits = 3  # daily free quota
+
+def charge_credit():
+    st.session_state.credits -= 1
+    if st.session_state.credits < 0:
+        st.error("Free quota reached. Upgrade for more cases.")
+        st.stop()
+
+# ───────── Helpers ─────────
+@st.cache_data(show_spinner=False)
+def gpt(system, user):
+    resp = openai.chat.completions.create(model="gpt-4o-mini", messages=[{"role":"system","content":system},{"role":"user","content":user}], max_tokens=700)
+    return resp.choices[0].message.content.strip()
+
+@st.cache_data(show_spinner=False)
+def whisper_transcribe(audio_bytes):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+        tmp.write(audio_bytes)
+        tmp.flush()
+        txt = openai.audio.transcriptions.create(model="whisper-1", file=open(tmp.name,"rb")).text
+    return txt
+
+@st.cache_data(show_spinner=False)
+def dicom_to_png(ds):
+    arr = ds.pixel_array.astype(np.float32)
+    arr -= arr.min(); arr /= arr.max(); arr *= 255
+    return Image.fromarray(arr.astype(np.uint8))
+
+# ───────── Tabs ─────────
+tabs = st.tabs(["Radiology AI", "Clinical Note Q&A", "Voice Triage", "UMLS Lookup", "GraphRAG"])
+
+# Radiology
+with tabs[0]:
+    st.header("📷 Radiology AI")
+    up = st.file_uploader("Upload DICOM or PNG/JPG", type=["dcm","png","jpg","jpeg"])
+    if up and st.button("Analyze"):
+        charge_credit()
+        if up.name.endswith(".dcm"):
+            ds = pydicom.dcmread(up)
+            img = dicom_to_png(ds)
+        else:
+            img = Image.open(up)
+        st.image(img, caption="Preview", use_column_width=True)
+        report = gpt("You are a board‑certified radiologist. Provide an impression with SNOMED codes.", "Describe findings and give TNM if cancer present.")
+        st.markdown("#### Report")
+        st.write(report)
+
+# Clinical Note Q&A
+with tabs[1]:
+    st.header("📄 Clinical Note Q&A")
+    txt = st.text_area("Paste clinical note")
+    q = st.text_input("Ask a question (or leave blank for summary)")
+    if txt and st.button("Process"):
+        charge_credit()
+        prompt = f"Here is the clinical note:\n{txt}\n\nQuestion: {q or 'Summarize'}"
+        ans = gpt("You are an expert physician.", prompt)
+        st.write(ans)
+
+# Voice Triage
+with tabs[2]:
+    st.header("🎤 Voice Triage")
+    aud = st.file_uploader("Upload patient symptoms recording", type=["wav","mp3","m4a","ogg"])
+    if aud and st.button("Infer" ):
+        charge_credit()
+        t = whisper_transcribe(aud.read())
+        st.write("**Transcript:**", t)
+        diff = gpt("You are a triage nurse.", f"Patient says: {t}\nProvide likely differentials with SNOMED codes and urgency level.")
+        st.write(diff)
+
+# UMLS Lookup
+with tabs[3]:
+    st.header("🔍 UMLS Concept Lookup")
+    term = st.text_input("Term or CUI")
+    if term:
+        resp = requests.get(f"https://uts-ws.nlm.nih.gov/rest/search/current?string={term}&apiKey=sample")
+        st.json(resp.json())
+
+# GraphRAG Explorer (toy demo)
+with tabs[4]:
+    st.header("🧠 GraphRAG Explorer")
+    st.info("Prototype biomedical KG Q&A – coming soon.")
+
+---------------------------------------------------------------------

requirements.txt

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-altair
-pandas
-streamlit
+streamlit>=1.35.0
+openai>=1.30.0
+pydicom
+Pillow
+numpy
+pydub
+soundfile
+ffmpeg-python
+stripe
+requests
+python-multipart
+fastapi
+uvicorn
+networkx
+
+---------------------------------------------------------------------