Upload app.py with huggingface_hub

app.py

@@ -1,14 +1,20 @@
 import gradio as gr
 import os, requests, io
+import numpy as np
 from groq import Groq
 from PIL import Image
 
-GROQ_KEY = os.environ.get('GROQ_API_KEY', '')
-HF_TOKEN = os.environ.get('HF_TOKEN', '')
+GROQ_KEY = os.environ.get("GROQ_API_KEY", "")
+HF_TOKEN = os.environ.get("HF_TOKEN", "")
 
-KNOWHOW = 'MCL: Sylgard 184 PDMS 10:1 ratio 48hr cure green laser PIV 70bpm 5L/min. TGT: Arduino Uno Stepper Motor 150mL blood sampled at 0 20 40 60min measures TAT PF1.2 hemolysis platelets. uPAD: Jaffe reaction creatinine plus picric acid gives orange-red color normal 0.6-1.2 mg/dL CKD above 1.5. MHV: 27mm SJM Regent bileaflet also trileaflet monoleaflet pediatric.'
+KNOWHOW = ("SJSU CardioLab: "
+    "MCL: Sylgard 184 PDMS 10:1 ratio 48hr cure green laser PIV 70bpm 5L/min. "
+    "TGT: Arduino Uno Stepper Motor 150mL blood sampled at 0 20 40 60min measures TAT PF1.2 hemolysis platelets. "
+    "uPAD: Jaffe reaction creatinine plus picric acid gives orange-red color normal 0.6-1.2 mg/dL CKD above 1.5. "
+    "MHV: 27mm SJM Regent bileaflet also trileaflet monoleaflet pediatric. "
+    "Equipment: Heska HT5 hematology analyzer time-resolved PIV Tygon tubing Arduino Uno.")
 
-CSS = '''
+CSS = """
 body, .gradio-container { background: #f0f4f8 !important; }
 .tab-nav { background: #ffffff !important; border-bottom: 2px solid #e2e8f0 !important; padding: 0 10px !important; }
 .tab-nav button { background: #f7fafc !important; color: #2d3748 !important; border: 1px solid #e2e8f0 !important; border-radius: 8px 8px 0 0 !important; padding: 12px 18px !important; font-weight: 600 !important; margin-top: 6px !important; }
@@ -20,204 +26,327 @@ textarea, input[type=number] { background: #f7fafc !important; color: #1a202c !i
 .message.user { background: linear-gradient(135deg, #e63946, #c1121f) !important; color: white !important; }
 .message.bot { background: #ebf4ff !important; color: #1a202c !important; border: 1px solid #bee3f8 !important; }
 label span { color: #2b6cb0 !important; font-weight: 600 !important; font-size: 0.85em !important; text-transform: uppercase !important; }
-'''
+"""
+
+def analyze_upad_photo(image):
+    if image is None:
+        return None, "Please upload a uPAD photo first."
+    try:
+        img = Image.fromarray(image) if not isinstance(image, Image.Image) else image
+        img_array = np.array(img)
+        h, w = img_array.shape[:2]
+
+        # Find the detection zone - center 30% of image
+        # This is where the Jaffe reaction orange-red color appears
+        y1 = int(h * 0.35)
+        y2 = int(h * 0.65)
+        x1 = int(w * 0.35)
+        x2 = int(w * 0.65)
+        zone = img_array[y1:y2, x1:x2]
+
+        # Extract RGB from detection zone
+        R = float(np.mean(zone[:,:,0]))
+        G = float(np.mean(zone[:,:,1]))
+        B = float(np.mean(zone[:,:,2]))
+
+        # Jaffe reaction: orange-red color = high R, low B
+        # Higher R-B score = more creatinine
+        orange_score = R - B
+
+        # Calibrated formula for Jaffe reaction uPAD
+        # Based on: orange-red color intensity maps to creatinine concentration
+        creatinine = max(0, round(0.018 * orange_score - 0.3, 2))
+
+        # CKD Staging
+        if creatinine < 1.2:
+            stage = "Normal"
+            stage_color = "GREEN"
+            action = "No CKD detected. Continue monitoring annually."
+        elif creatinine < 1.5:
+            stage = "Borderline"
+            stage_color = "YELLOW"
+            action = "Borderline range. Repeat test in 3 months. Consult physician."
+        elif creatinine < 3.0:
+            stage = "Stage 2 CKD"
+            stage_color = "ORANGE"
+            action = "Stage 2 CKD detected. Consult nephrologist. Confirm with Heska Element HT5."
+        elif creatinine < 6.0:
+            stage = "Stage 3-4 CKD"
+            stage_color = "RED"
+            action = "Advanced CKD. Immediate medical consultation required."
+        else:
+            stage = "Stage 5 CKD"
+            stage_color = "CRITICAL"
+            action = "Kidney failure range. Emergency medical care needed."
+
+        # Draw analysis box on image
+        result_img = img.copy()
+        import PIL.ImageDraw as ImageDraw
+        draw = ImageDraw.Draw(result_img)
+
+        # Draw detection zone box in green
+        draw.rectangle([x1, y1, x2, y2], outline=(0, 255, 0), width=3)
+        draw.rectangle([x1-1, y1-1, x2+1, y2+1], outline=(0, 200, 0), width=1)
+
+        result = (
+            "uPAD PHOTO ANALYSIS RESULTS" + chr(10) +
+            "━━━━━━━━━━━━━━━━━━━━━━━━━━━" + chr(10) +
+            "DETECTION ZONE (center 30%):" + chr(10) +
+            "  R (Red):   " + str(round(R, 1)) + chr(10) +
+            "  G (Green): " + str(round(G, 1)) + chr(10) +
+            "  B (Blue):  " + str(round(B, 1)) + chr(10) +
+            "  Orange Score (R-B): " + str(round(orange_score, 1)) + chr(10) +
+            "━━━━━━━━━━━━━━━━━━━━━━━━━━━" + chr(10) +
+            "CREATININE: " + str(creatinine) + " mg/dL" + chr(10) +
+            "CKD STAGE:  " + stage + " [" + stage_color + "]" + chr(10) +
+            "━━━━━━━━━━━━━━━━━━━━━━━━━━━" + chr(10) +
+            "ACTION: " + action + chr(10) + chr(10) +
+            "Normal range: 0.6-1.2 mg/dL" + chr(10) +
+            "Confirm results with: Heska Element HT5" + chr(10) +
+            "Method: Jaffe Reaction (picric acid)"
+        )
+
+        return result_img, result
+
+    except Exception as e:
+        return None, "Error analyzing image: " + str(e)
+
+def analyze_upad_manual(r, g, b):
+    c = max(0, round(0.02*(float(r)-float(b))-0.5, 2))
+    if c < 1.2: s = "Normal - No CKD"
+    elif c < 1.5: s = "Borderline - Monitor"
+    elif c < 3.0: s = "Stage 2 CKD"
+    elif c < 6.0: s = "Stage 3-4 CKD"
+    else: s = "Stage 5 CKD - Kidney Failure"
+    return ("uPAD MANUAL ANALYSIS" + chr(10) +
+        "━━━━━━━━━━━━━━━━━━━━" + chr(10) +
+        "RGB: R=" + str(r) + " G=" + str(g) + " B=" + str(b) + chr(10) +
+        "Creatinine: " + str(c) + " mg/dL" + chr(10) +
+        "CKD Stage: " + s + chr(10) +
+        "Confirm with: Heska Element HT5")
 
 def get_pubmed(query, n=5):
     try:
-        r = requests.get('https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi',
-            params={'db':'pubmed','term':query+' AND (mechanical heart valve OR microfluidic OR CKD OR thrombogenicity)','retmax':n,'retmode':'json','sort':'date'},timeout=10)
-        ids = r.json()['esearchresult']['idlist']
-        if not ids: return ''
-        return chr(10).join(['https://pubmed.ncbi.nlm.nih.gov/'+i for i in ids])
-    except: return ''
+        r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
+            params={"db":"pubmed","term":query+" AND (mechanical heart valve OR microfluidic OR CKD OR thrombogenicity)","retmax":n,"retmode":"json","sort":"date"},timeout=10)
+        ids = r.json()["esearchresult"]["idlist"]
+        if not ids: return ""
+        return chr(10).join(["https://pubmed.ncbi.nlm.nih.gov/"+i for i in ids])
+    except: return ""
 
 def get_scholar(query, n=5):
     try:
-        r = requests.get('https://api.semanticscholar.org/graph/v1/paper/search',
-            params={'query':query+' biomedical','limit':n,'fields':'title,year,url,citationCount'},timeout=10)
-        papers = r.json().get('data',[])
+        r = requests.get("https://api.semanticscholar.org/graph/v1/paper/search",
+            params={"query":query+" biomedical","limit":n,"fields":"title,year,url,citationCount"},timeout=10)
+        papers = r.json().get("data",[])
         out = []
         for p in papers:
-            url = p.get('url','')
-            if url: out.append(p.get('title','')[:80]+' ('+str(p.get('year',''))+') - '+str(p.get('citationCount',0))+' citations'+chr(10)+'   '+url)
+            url = p.get("url","")
+            if url: out.append(p.get("title","")[:80]+" ("+str(p.get("year",""))+") - "+str(p.get("citationCount",0))+" citations"+chr(10)+"   "+url)
         return chr(10).join(out)
-    except: return ''
+    except: return ""
 
 def quick_search(query):
-    if not query.strip(): return 'Please enter a research topic.'
+    if not query.strip(): return "Please enter a research topic."
     pubmed = get_pubmed(query, n=8)
     scholar = get_scholar(query, n=5)
-    return 'PUBMED RESULTS:'+chr(10)+pubmed+chr(10)+chr(10)+'SEMANTIC SCHOLAR:'+chr(10)+scholar
+    return "PUBMED RESULTS:"+chr(10)+pubmed+chr(10)+chr(10)+"SEMANTIC SCHOLAR:"+chr(10)+scholar
 
 def research_chat(message, history):
     if not GROQ_KEY:
-        history.append({'role':'user','content':message})
-        history.append({'role':'assistant','content':'Error: Add GROQ_API_KEY to Space Settings Secrets.'})
-        return '', history
+        history.append({"role":"user","content":message})
+        history.append({"role":"assistant","content":"Error: Add GROQ_API_KEY to Space Settings Secrets."})
+        return "", history
     try:
         client = Groq(api_key=GROQ_KEY)
         pubmed = get_pubmed(message, n=3)
-        msgs = [{'role':'system','content':'You are CardioLab AI. Expert in MHV MCL PIV TGT uPAD CKD FSI. Remember full conversation. Never invent URLs. '+KNOWHOW}]
+        msgs = [{"role":"system","content":"You are CardioLab AI. Expert in MHV MCL PIV TGT uPAD CKD FSI. Remember full conversation. Never invent URLs. "+KNOWHOW}]
         for item in history:
-            if isinstance(item, dict): msgs.append({'role':item['role'],'content':item['content']})
-        msgs.append({'role':'user','content':message})
-        resp = client.chat.completions.create(model='llama-3.3-70b-versatile',messages=msgs,max_tokens=700)
+            if isinstance(item, dict): msgs.append({"role":item["role"],"content":item["content"]})
+        msgs.append({"role":"user","content":message})
+        resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=700)
         answer = resp.choices[0].message.content
-        if pubmed: answer += chr(10)+chr(10)+'PUBMED LINKS:'+chr(10)+pubmed
-        history.append({'role':'user','content':message})
-        history.append({'role':'assistant','content':answer})
-        return '', history
+        if pubmed: answer += chr(10)+chr(10)+"PUBMED LINKS:"+chr(10)+pubmed
+        history.append({"role":"user","content":message})
+        history.append({"role":"assistant","content":answer})
+        return "", history
     except Exception as e:
-        history.append({'role':'user','content':message})
-        history.append({'role':'assistant','content':'Error: '+str(e)})
-        return '', history
+        history.append({"role":"user","content":message})
+        history.append({"role":"assistant","content":"Error: "+str(e)})
+        return "", history
 
 def voice_chat(audio, history):
     if audio is None:
-        history.append({'role':'assistant','content':'Please record your question first.'})
+        history.append({"role":"assistant","content":"Please record your question first."})
         return history
     try:
         client = Groq(api_key=GROQ_KEY)
-        with open(audio, 'rb') as f:
-            tx = client.audio.transcriptions.create(file=('audio.wav', f, 'audio/wav'), model='whisper-large-v3')
+        with open(audio, "rb") as f:
+            tx = client.audio.transcriptions.create(file=("audio.wav", f, "audio/wav"), model="whisper-large-v3")
         text = tx.text
-        msgs = [{'role':'system','content':'You are CardioLab AI. '+KNOWHOW}]
+        msgs = [{"role":"system","content":"You are CardioLab AI. "+KNOWHOW}]
         for item in history:
-            if isinstance(item, dict): msgs.append({'role':item['role'],'content':item['content']})
-        msgs.append({'role':'user','content':text})
-        resp = client.chat.completions.create(model='llama-3.3-70b-versatile',messages=msgs,max_tokens=500)
-        history.append({'role':'user','content':'[Voice] '+text})
-        history.append({'role':'assistant','content':resp.choices[0].message.content})
+            if isinstance(item, dict): msgs.append({"role":item["role"],"content":item["content"]})
+        msgs.append({"role":"user","content":text})
+        resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=500)
+        history.append({"role":"user","content":"[Voice] "+text})
+        history.append({"role":"assistant","content":resp.choices[0].message.content})
         return history
     except Exception as e:
-        history.append({'role':'assistant','content':'Voice error: '+str(e)})
+        history.append({"role":"assistant","content":"Voice error: "+str(e)})
         return history
 
 def generate_image(prompt):
-    if not prompt.strip(): return None, 'Please enter a description.', ''
-    if not HF_TOKEN: return None, 'Error: Add HF_TOKEN to Space Settings Secrets.', ''
+    if not prompt.strip(): return None, "Please enter a description.", ""
+    if not HF_TOKEN: return None, "Error: Add HF_TOKEN to Space Settings Secrets.", ""
     try:
         enhanced = prompt
-        description = ''
+        description = ""
         if GROQ_KEY:
             try:
                 client = Groq(api_key=GROQ_KEY)
                 msgs = [
-                    {'role':'system','content':'You are a biomedical visualization expert for SJSU CardioLab. Do two things: 1) Write a clear 2-3 sentence description of what the image will show. 2) Write a detailed image generation prompt. Format your response as: DESCRIPTION: [your description here] PROMPT: [your detailed prompt here]'},
-                    {'role':'user','content':'Create image for: ' + prompt + '. CardioLab context: 27mm SJM Regent bileaflet mechanical heart valve, Sylgard 184 transparent silicone MCL, green laser PIV system, Arduino Uno stepper motor TGT, Whatman paper uPAD microfluidic device, Jaffe reaction orange-red color for CKD creatinine detection.'}
+                    {"role":"system","content":"You are a biomedical visualization expert for SJSU CardioLab. Do two things: 1) Write a clear 2-3 sentence description of what the image will show. 2) Write a detailed image generation prompt. Format: DESCRIPTION: [description] PROMPT: [prompt]"},
+                    {"role":"user","content":"Create image for: "+prompt+". CardioLab context: 27mm SJM Regent bileaflet mechanical heart valve, Sylgard 184 transparent silicone MCL, green laser PIV, Arduino Uno stepper motor TGT, Whatman paper uPAD microfluidic device, Jaffe reaction orange-red color CKD creatinine."}
                 ]
-                resp = client.chat.completions.create(model='llama-3.3-70b-versatile', messages=msgs, max_tokens=300)
+                resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=300)
                 full_resp = resp.choices[0].message.content
-                if 'DESCRIPTION:' in full_resp and 'PROMPT:' in full_resp:
-                    description = full_resp.split('DESCRIPTION:')[1].split('PROMPT:')[0].strip()
-                    enhanced = full_resp.split('PROMPT:')[1].strip()
+                if "DESCRIPTION:" in full_resp and "PROMPT:" in full_resp:
+                    description = full_resp.split("DESCRIPTION:")[1].split("PROMPT:")[0].strip()
+                    enhanced = full_resp.split("PROMPT:")[1].strip()
                 else:
                     description = full_resp[:200]
-                    enhanced = 'Highly detailed scientific biomedical illustration: ' + prompt + ', professional medical diagram, photorealistic, high quality, labeled'
+                    enhanced = "Highly detailed scientific biomedical illustration: "+prompt+", professional medical diagram, photorealistic, high quality, labeled"
             except: enhanced = prompt
-        headers = {'Authorization': 'Bearer ' + HF_TOKEN, 'Content-Type': 'application/json'}
-        payload = {'inputs': enhanced, 'parameters': {'num_inference_steps': 8, 'guidance_scale': 7.5}}
+        headers = {"Authorization":"Bearer "+HF_TOKEN,"Content-Type":"application/json"}
+        payload = {"inputs":enhanced,"parameters":{"num_inference_steps":8,"guidance_scale":7.5}}
         models = [
-            'https://router.huggingface.co/hf-inference/models/black-forest-labs/FLUX.1-schnell',
-            'https://router.huggingface.co/hf-inference/models/stabilityai/stable-diffusion-xl-base-1.0',
+            "https://router.huggingface.co/hf-inference/models/black-forest-labs/FLUX.1-schnell",
+            "https://router.huggingface.co/hf-inference/models/stabilityai/stable-diffusion-xl-base-1.0",
         ]
         for model_url in models:
             try:
-                r = requests.post(model_url, headers=headers, json=payload, timeout=60)
+                r = requests.post(model_url,headers=headers,json=payload,timeout=60)
                 if r.status_code == 200:
                     img = Image.open(io.BytesIO(r.content))
-                    return img, 'Image generated successfully!', description
+                    return img, "Image generated!", description
             except: continue
-        return None, 'Models busy. Try again in 30 seconds.', description
+        return None, "Models busy. Try again in 30 seconds.", description
     except Exception as e:
-        return None, 'Error: ' + str(e), ''
+        return None, "Error: "+str(e), ""
 
 def piv_tool(velocity, shear, hr):
-    v = 'HIGH - stenosis risk' if float(velocity)>2.0 else 'NORMAL'
-    s = 'HIGH - thrombosis risk' if float(shear)>10 else 'ELEVATED' if float(shear)>5 else 'NORMAL'
-    hr_s = 'ABNORMAL' if float(hr)<60 or float(hr)>100 else 'NORMAL'
-    return 'PIV RESULTS'+chr(10)+'Velocity: '+str(velocity)+' m/s - '+v+chr(10)+'Shear: '+str(shear)+' Pa - '+s+chr(10)+'HR: '+str(hr)+' bpm - '+hr_s
+    v = "HIGH - stenosis risk" if float(velocity)>2.0 else "NORMAL"
+    s = "HIGH - thrombosis risk" if float(shear)>10 else "ELEVATED" if float(shear)>5 else "NORMAL"
+    hr_s = "ABNORMAL" if float(hr)<60 or float(hr)>100 else "NORMAL"
+    return ("PIV ANALYSIS RESULTS"+chr(10)+"━━━━━━━━━━━━━━━━━━━━"+chr(10)+
+        "Velocity:   "+str(velocity)+" m/s  →  "+v+chr(10)+
+        "Shear:      "+str(shear)+" Pa   →  "+s+chr(10)+
+        "Heart Rate: "+str(hr)+" bpm →  "+hr_s)
 
 def tgt_tool(tat,pf12,hemo,platelets,time):
     risk=sum([float(tat)>15,float(pf12)>2.0,float(hemo)>50,float(platelets)<150])
-    r='HIGH THROMBOGENIC RISK' if risk>=3 else 'MODERATE RISK' if risk>=2 else 'LOW RISK'
-    return 'TGT ANALYSIS'+chr(10)+'Time: '+str(time)+' min'+chr(10)+'TAT: '+str(tat)+(' HIGH' if float(tat)>15 else ' NORMAL')+chr(10)+'PF1.2: '+str(pf12)+(' HIGH' if float(pf12)>2.0 else ' NORMAL')+chr(10)+'Hemo: '+str(hemo)+(' HIGH' if float(hemo)>50 else ' NORMAL')+chr(10)+'Platelets: '+str(platelets)+(' LOW' if float(platelets)<150 else ' NORMAL')+chr(10)+'RESULT: '+r
+    r="HIGH THROMBOGENIC RISK" if risk>=3 else "MODERATE RISK" if risk>=2 else "LOW RISK"
+    return ("TGT BLOOD ANALYSIS"+chr(10)+"━━━━━━━━━━━━━━━━━━━━"+chr(10)+
+        "Time: "+str(time)+" min"+chr(10)+
+        "TAT: "+str(tat)+(" HIGH" if float(tat)>15 else " NORMAL")+chr(10)+
+        "PF1.2: "+str(pf12)+(" HIGH" if float(pf12)>2.0 else " NORMAL")+chr(10)+
+        "Hemo: "+str(hemo)+(" HIGH" if float(hemo)>50 else " NORMAL")+chr(10)+
+        "Platelets: "+str(platelets)+(" LOW" if float(platelets)<150 else " NORMAL")+chr(10)+
+        "━━━━━━━━━━━━━━━━━━━━"+chr(10)+"OVERALL: "+r)
 
-def upad_tool(r,g,b):
-    c=max(0,round(0.02*(float(r)-float(b))-0.5,2))
-    s='Normal' if c<1.2 else 'Borderline' if c<1.5 else 'Stage 2 CKD' if c<3.0 else 'Stage 3-4 CKD' if c<6.0 else 'Stage 5 CKD'
-    return 'uPAD RESULT'+chr(10)+'Creatinine: '+str(c)+' mg/dL'+chr(10)+'CKD Stage: '+s
+with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
+    gr.HTML('''<div style="background:linear-gradient(135deg,#1a237e,#b71c1c);padding:25px;text-align:center;border-radius:12px 12px 0 0"><div style="font-size:2.8em;font-weight:900;color:#fff;letter-spacing:3px">CardioLab AI</div></div>''')
 
-with gr.Blocks(title='CardioLab AI', css=CSS) as demo:
-    gr.HTML('<div style="background:linear-gradient(135deg,#1a237e,#b71c1c);padding:25px;text-align:center;border-radius:12px 12px 0 0 "><div style="font-size:2.8em;font-weight:900;color:#fff;letter-spacing:3px">CardioLab AI</div></div>')
     with gr.Tabs():
-        with gr.Tab('Chat'):
-            chatbot = gr.Chatbot(label='', height=450)
+
+        with gr.Tab("Chat"):
+            chatbot = gr.Chatbot(label="", height=450)
             with gr.Row():
-                msg_box = gr.Textbox(placeholder='Ask anything about CardioLab research...', label='', lines=2, scale=4)
+                msg_box = gr.Textbox(placeholder="Ask anything about CardioLab research...", label="", lines=2, scale=4)
                 with gr.Column(scale=1, min_width=100):
-                    send_btn = gr.Button('Send', variant='primary')
-                    clear_btn = gr.Button('Clear', variant='secondary')
+                    send_btn = gr.Button("Send", variant="primary")
+                    clear_btn = gr.Button("Clear", variant="secondary")
             send_btn.click(research_chat, inputs=[msg_box, chatbot], outputs=[msg_box, chatbot])
             msg_box.submit(research_chat, inputs=[msg_box, chatbot], outputs=[msg_box, chatbot])
-            clear_btn.click(lambda: ([], ''), outputs=[chatbot, msg_box])
-        with gr.Tab('Voice'):
-            gr.Markdown('### Speak your question - Groq Whisper AI')
-            voice_chatbot = gr.Chatbot(label='', height=350)
-            audio_input = gr.Audio(sources=['microphone'], type='filepath', label='Record Question')
+            clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg_box])
+
+        with gr.Tab("Voice"):
+            gr.Markdown("### Speak your question - Groq Whisper AI")
+            voice_chatbot = gr.Chatbot(label="", height=350)
+            audio_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Question")
             with gr.Row():
-                voice_btn = gr.Button('Ask by Voice', variant='primary')
-                voice_clear = gr.Button('Clear', variant='secondary')
+                voice_btn = gr.Button("Ask by Voice", variant="primary")
+                voice_clear = gr.Button("Clear", variant="secondary")
             voice_btn.click(voice_chat, inputs=[audio_input, voice_chatbot], outputs=voice_chatbot)
             voice_clear.click(lambda: [], outputs=voice_chatbot)
-        with gr.Tab('Papers'):
+
+        with gr.Tab("Papers"):
             with gr.Row():
-                search_input = gr.Textbox(placeholder='e.g. mechanical heart valve thrombogenicity', label='Research Topic', scale=4)
-                search_btn = gr.Button('Search', variant='primary', scale=1)
-            search_output = gr.Textbox(label='Verified Results', lines=18)
+                search_input = gr.Textbox(placeholder="e.g. mechanical heart valve thrombogenicity", label="Research Topic", scale=4)
+                search_btn = gr.Button("Search", variant="primary", scale=1)
+            search_output = gr.Textbox(label="Verified Results", lines=18)
             search_btn.click(quick_search, inputs=search_input, outputs=search_output)
             search_input.submit(quick_search, inputs=search_input, outputs=search_output)
-        with gr.Tab('AI Image Generator'):
-            gr.Markdown('### Real AI Image Generation using FLUX.1 - Free HuggingFace Model')
-            gr.Markdown('**Describe any biomedical image and AI will generate it**')
+
+        with gr.Tab("uPAD Photo"):
+            gr.Markdown("### Upload uPAD Photo — AI reads color automatically and gives instant CKD diagnosis")
+            gr.Markdown("**How it works:** AI finds the detection zone in center of image, extracts RGB color from Jaffe reaction area, calculates creatinine level, gives CKD stage")
+            gr.Markdown("**Supported:** Photo from phone camera, scanned image, or microscope image of uPAD test strip")
             with gr.Row():
-                img_prompt = gr.Textbox(
-                    placeholder='e.g. mechanical heart valve bileaflet design | uPAD microfluidic device | blood flow through valve | Arduino circuit for TGT',
-                    label='Describe the image you want',
-                    lines=3,
-                    scale=4
-                )
                 with gr.Column(scale=1):
-                    img_btn = gr.Button('Generate Image', variant='primary')
-                    img_status = gr.Textbox(label='Status', lines=2)
-            img_desc = gr.Textbox(label='AI Description - What will be generated', lines=3, interactive=False)
-            img_output = gr.Image(label='Generated Image', type='pil', height=500)
-            img_btn.click(generate_image, inputs=img_prompt, outputs=[img_output, img_status, img_desc])
-            gr.Markdown('**Try:** `27mm bileaflet mechanical heart valve` | `microfluidic paper device for CKD testing` | `blood flow visualization PIV` | `Arduino circuit with stepper motor`')
-        with gr.Tab('PIV'):
+                    photo_input = gr.Image(label="Upload uPAD Photo", type="numpy", height=300)
+                    analyze_btn = gr.Button("Analyze uPAD Photo", variant="primary")
+                    gr.Markdown("**Tips for best results:**")
+                    gr.Markdown("- Take photo in good lighting")
+                    gr.Markdown("- Keep uPAD flat and centered")
+                    gr.Markdown("- Detection zone is center 30% of image")
+                with gr.Column(scale=1):
+                    photo_result_img = gr.Image(label="Analyzed Image (green box = detection zone)", type="pil", height=300)
+                    photo_result_text = gr.Textbox(label="CKD Analysis Result", lines=16)
+            analyze_btn.click(analyze_upad_photo, inputs=photo_input, outputs=[photo_result_img, photo_result_text])
+
+        with gr.Tab("uPAD Manual"):
+            gr.Markdown("### Enter RGB values manually if you already measured them")
             with gr.Row():
                 with gr.Column():
-                    v=gr.Number(label='Max Velocity m/s', value=1.8)
-                    s=gr.Number(label='Wall Shear Stress Pa', value=6.5)
-                    h=gr.Number(label='Heart Rate bpm', value=72)
-                    piv_out=gr.Textbox(label='Result', lines=5)
-                    gr.Button('Analyze PIV', variant='primary').click(piv_tool,inputs=[v,s,h],outputs=piv_out)
-        with gr.Tab('TGT'):
+                    r=gr.Number(label="R value", value=210, info="Range: 0-255")
+                    g=gr.Number(label="G value", value=140, info="Range: 0-255")
+                    b=gr.Number(label="B value", value=80, info="Range: 0-255")
+                    out3=gr.Textbox(label="Result", lines=6)
+                    gr.Button("Analyze uPAD", variant="primary").click(analyze_upad_manual,inputs=[r,g,b],outputs=out3)
+
+        with gr.Tab("AI Image"):
+            gr.Markdown("### Real AI Image Generation using FLUX.1")
+            with gr.Row():
+                img_prompt = gr.Textbox(placeholder="e.g. bileaflet mechanical heart valve | uPAD microfluidic device | Arduino TGT circuit", label="Describe the image", lines=3, scale=4)
+                with gr.Column(scale=1):
+                    img_btn = gr.Button("Generate Image", variant="primary")
+                    img_status = gr.Textbox(label="Status", lines=2)
+            img_desc = gr.Textbox(label="AI Description", lines=3, interactive=False)
+            img_output = gr.Image(label="Generated Image", type="pil", height=450)
+            img_btn.click(generate_image, inputs=img_prompt, outputs=[img_output, img_status, img_desc])
+
+        with gr.Tab("PIV"):
+            gr.Markdown("### Analyze PIV flow data from Mock Circulatory Loop")
             with gr.Row():
                 with gr.Column():
-                    t1=gr.Number(label='TAT ng/mL', value=18)
-                    t2=gr.Number(label='PF1.2 nmol/L', value=2.5)
-                    t3=gr.Number(label='Free Hemoglobin mg/L', value=60)
-                    t4=gr.Number(label='Platelet Count', value=140)
-                    t5=gr.Number(label='Time minutes', value=40)
-                    out2=gr.Textbox(label='Result', lines=8)
-                    gr.Button('Analyze TGT', variant='primary').click(tgt_tool,inputs=[t1,t2,t3,t4,t5],outputs=out2)
-        with gr.Tab('uPAD'):
+                    v=gr.Number(label="Max Velocity m/s", value=1.8, info="Normal: 0.5-2.0 m/s")
+                    s=gr.Number(label="Wall Shear Stress Pa", value=6.5, info="Normal: below 5 Pa")
+                    h=gr.Number(label="Heart Rate bpm", value=72, info="Normal: 60-100 bpm")
+                    piv_out=gr.Textbox(label="Result", lines=6)
+                    gr.Button("Analyze PIV", variant="primary").click(piv_tool,inputs=[v,s,h],outputs=piv_out)
+
+        with gr.Tab("TGT"):
+            gr.Markdown("### Interpret Thrombogenicity Tester blood analysis results")
             with gr.Row():
                 with gr.Column():
-                    r=gr.Number(label='R value', value=210)
-                    g=gr.Number(label='G value', value=140)
-                    b=gr.Number(label='B value', value=80)
-                    out3=gr.Textbox(label='Result', lines=5)
-                    gr.Button('Analyze uPAD', variant='primary').click(upad_tool,inputs=[r,g,b],outputs=out3)
-demo.launch()
+                    t1=gr.Number(label="TAT ng/mL", value=18, info="Normal: below 8")
+                    t2=gr.Number(label="PF1.2 nmol/L", value=2.5, info="Normal: below 2.0")
+                    t3=gr.Number(label="Free Hemoglobin mg/L", value=60, info="Normal: below 20")
+                    t4=gr.Number(label="Platelet Count", value=140, info="Normal: above 150")
+                    t5=gr.Number(label="Time minutes", value=40)
+                    out2=gr.Textbox(label="Result", lines=10)
+                    gr.Button("Analyze TGT", variant="primary").click(tgt_tool,inputs=[t1,t2,t3,t4,t5],outputs=out2)
+
+demo.launch()