Update app.py

app.py

@@ -1,337 +1,336 @@
-import os
-import streamlit as st
-from PIL import Image
-import pandas as pd
-from datetime import datetime
-from transformers import (
-    AutoFeatureExtractor,
-    AutoModelForImageClassification,
-    AutoTokenizer,
-    AutoModelForSeq2SeqLM,
-    pipeline,
-    Qwen2VLForConditionalGeneration, Qwen2VLProcessor,
-)
-import requests
-from geopy.geocoders import Nominatim
-import folium
-from streamlit_folium import st_folium
-import cv2
-import numpy as np
-
-
-st.set_page_config(page_title="Skin Cancer Dashboard", layout="wide")
-
-# --- Configuration ---
-# Ensure you have set your Hugging Face token as an environment variable:
-# export HF_TOKEN="YOUR_TOKEN_HERE"
-MODEL_NAME = "Anwarkh1/Skin_Cancer-Image_Classification"
-LLM_NAME = "google/flan-t5-xl"
-HF_TOKEN   = ".."
-DATA_DIR = "data/harvard_dataset"  # Path where you download and unpack the Harvard Dataverse dataset
-DIARY_CSV = "diary.csv"
-CANCER_DIR = r"D:\Models\googleflan-t5-xl"
-LLM_DIR = r"D:\Models\SkinCancer"
-
-# Initialize session state defaults
-if 'initialized' not in st.session_state:
-    st.session_state['label'] = None
-    st.session_state['score'] = None
-    st.session_state['mole_id'] = ''
-    st.session_state['geo_location'] = ''
-    st.session_state['chat_history'] = []
-    st.session_state['initialized'] = True
-
-# Initialize geolocator for free geocoding
-geolocator = Nominatim(user_agent="skin-dashboard", timeout = 10)
-
-# --- Load Model & Feature Extractor ---
-@st.cache_resource
-def load_image_model(token: str):
-    extractor = AutoFeatureExtractor.from_pretrained(
-        MODEL_NAME,
-        use_auth_token=token
-    )
-    model = AutoModelForImageClassification.from_pretrained(
-        MODEL_NAME,
-        use_auth_token=token
-    )
-    return pipeline(
-        "image-classification",
-        model=model,
-        feature_extractor=extractor,
-        device=0  # set to GPU index or -1 for CPU
-    )
-
-@st.cache_resource
-def load_llm(token: str):
-
-    tokenizer = AutoTokenizer.from_pretrained(
-        LLM_NAME,
-        use_auth_token=token
-    )
-    # Use Seq2SeqLM for T5-style (text2text) models:
-    model = AutoModelForSeq2SeqLM.from_pretrained(
-        LLM_NAME,
-        use_auth_token=token,
-    )
-    return pipeline(
-        "text2text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        device_map="auto",    # or device=0 for single GPU / -1 for CPU
-        max_length=10000,
-        num_beams=5,
-        no_repeat_ngram_size=2,
-        early_stopping=True,
-
-    )
-classifier = load_image_model(HF_TOKEN) if HF_TOKEN else None
-explainer = load_llm(HF_TOKEN) if HF_TOKEN else None
-
-# --- Diary Init ----
-
-if not os.path.exists(DIARY_CSV):
-    pd.DataFrame(
-        columns=["timestamp", "image_path", "mole_id", "geo_location", "label", "score",
-                 "body_location", "prior_consultation", "pain", "itch"]
-    ).to_csv(DIARY_CSV, index=False)
-
-# --- Save entry helper
-
-def save_entry(img_path: str, mole_id: str, geo_location: str,
-               label: str, score: float,
-               body_location: str, prior_consult: str, pain: str, itch: str):
-    df = pd.read_csv(DIARY_CSV)
-    entry = {
-        "timestamp": datetime.now().isoformat(),
-        "image_path": img_path,
-        "mole_id": mole_id,
-        "geo_location": geo_location,
-        "label": label,
-        "score": float(score),
-        "body_location": body_location,
-        "prior_consultation": prior_consult,
-        "pain": pain,
-        "itch": itch
-    }
-    df.loc[len(df)] = entry
-    df.to_csv(DIARY_CSV, index=False)
-
-# --- Preprocessing Functions ---
-def remove_hair(img: np.ndarray) -> np.ndarray:
-    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (17, 17))
-    blackhat = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, kernel)
-    _, mask = cv2.threshold(blackhat, 10, 255, cv2.THRESH_BINARY)
-    return cv2.inpaint(img, mask, 1, cv2.INPAINT_TELEA)
-
-
-def preprocess(img: Image.Image, size: int = 224) -> Image.Image:
-    arr = np.array(img)
-    bgr = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
-    bgr = remove_hair(bgr)
-    bgr = cv2.bilateralFilter(bgr, d=9, sigmaColor=75, sigmaSpace=75)
-    lab = cv2.cvtColor(bgr, cv2.COLOR_BGR2LAB)
-    l, a, b = cv2.split(lab)
-    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
-    cl = clahe.apply(l)
-    merged = cv2.merge((cl, a, b))
-    bgr = cv2.cvtColor(merged, cv2.COLOR_LAB2BGR)
-    h, w = bgr.shape[:2]
-    scale = size / max(h, w)
-    nh, nw = int(h*scale), int(w*scale)
-    resized = cv2.resize(bgr, (nw, nh), interpolation=cv2.INTER_AREA)
-    canvas = np.full((size, size, 3), 128, dtype=np.uint8)
-    top, left = (size-nh)//2, (size-nw)//2
-    canvas[top:top+nh, left:left+nw] = resized
-    rgb = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB)
-    return Image.fromarray(rgb)
-
-# -----Streamlit layout ---- 
-st.title("🩺 Skin Cancer Recognition Dashboard")
-menu = ["Scan Mole","Chat","Diary", "Dataset Explorer"]
-choice = st.sidebar.selectbox("Navigation", menu)
-
-# --- Initialize Scan a Mole ---
-if choice == "Scan Mole":
-    st.header("🔍 Scan a Mole")
-    if not classifier:
-        st.error("Missing HF_TOKEN.")
-        st.stop()
-
-    upload = st.file_uploader("Upload a skin image", type=["jpg","jpeg","png"])
-    if not upload:
-        st.info("Please upload an image to begin.")
-        st.stop()
-
-    raw = Image.open(upload).convert("RGB")
-    st.image(raw, caption="Original", use_container_width=True)
-
-    proc = preprocess(raw)
-    st.image(proc, caption="Preprocessed", use_container_width=True)
-
-    mole = st.text_input("Mole ID")
-    city = st.text_input("Geographic location")
-    body = st.selectbox("Body location", ["Face","Scalp","Neck","Chest","Back","Arm","Hand","Leg","Foot","Other"])
-    prior = st.radio("Prior consult?", ["Yes","No"], horizontal=True)
-    pain = st.radio("Pain?", ["Yes","No"], horizontal=True)
-    itch = st.radio("Itch?", ["Yes","No"], horizontal=True)
-
-    if st.button("Classify"):
-        if not mole or not city:
-            st.error("Enter ID and location.")
-        else:
-            with st.spinner("Analyzing..."):
-                out = classifier(proc)
-            lbl, scr = out[0]["label"], out[0]["score"]
-            save_dir = os.path.join("scans", f"{mole}_{datetime.now().timestamp()}.png")
-            os.makedirs(os.path.dirname(save_dir), exist_ok=True)
-            raw.save(save_dir)
-            save_entry(save_dir, mole, city, lbl, scr, body, prior, pain, itch)
-            st.session_state.update({
-                'label': lbl,
-                'score': scr,
-                'mole_id': mole,
-                'geo_location': city
-            })
-
-    if st.session_state['label']:
-        st.success(f"Prediction: {st.session_state['label']} (score {st.session_state['score']:.2f})")
-        if explainer:
-            with st.spinner("Explaining..."):
-                text = explainer(f"Explain {st.session_state['label']} and recommendation.")[0]['generated_text']
-            st.markdown("### Explanation"); st.write(text)
-
-        loc = geolocator.geocode(st.session_state['geo_location'])
-        if loc:
-            m = folium.Map([loc.latitude, loc.longitude], zoom_start=12)
-            folium.Marker([loc.latitude, loc.longitude], "You").add_to(m)
-            resp = requests.post(
-                "https://overpass-api.de/api/interpreter",
-                data={"data": f"[out:json];node(around:5000,{loc.latitude},{loc.longitude})[~\"^(amenity|healthcare)$\"~\"clinic|doctors\"];out;"}
-            )
-            for el in resp.json().get('elements', []):
-                tags = el.get('tags', {});
-                lat = el.get('lat') or el['center']['lat']; lon = el.get('lon') or el['center']['lon']
-                folium.Marker([lat, lon], tags.get('name','Clinic')).add_to(m)
-            st.markdown("### Nearby Clinics"); st_folium(m, width=700)
-
-# --- Chat Tab ---
-elif choice == "Chat":
-    st.header("💬 Follow-Up Chat")
-    if not st.session_state['label']:
-        st.info("Please perform a scan first in the 'Scan Mole' tab.")
-    else:
-        lbl = st.session_state['label']
-        scr = st.session_state['score']
-        mid = st.session_state['mole_id']
-        gloc = st.session_state['geo_location']
-        st.markdown(f"**Context:** prediction for **{mid}** at **{gloc}** is **{lbl}** (confidence {scr:.2f}).")
-
-        # New user message comes first for immediate loop
-        user_q = st.chat_input("Ask a follow-up question:", key="chat_input")
-        if user_q and explainer:
-            st.session_state['chat_history'].append({'role':'user','content':user_q})
-            system_p = "You are a dermatology assistant. Provide concise medical advice without clarifying questions."
-            tpl = (
-                f"{system_p}\nContext: prediction is {lbl} with confidence {scr:.2f}.\n"
-                f"User: {user_q}\nAssistant:"
-            )
-            with st.spinner("Generating response..."):
-                reply = explainer(tpl)[0]['generated_text']
-            st.session_state['chat_history'].append({'role':'assistant','content':reply})
-
-        # Display the updated chat history
-        for msg in st.session_state['chat_history']:
-            prefix = 'You' if msg['role']=='user' else 'AI'
-            st.markdown(f"**{prefix}:** {msg['content']}")
-
-
-# --- Diary Page ---
-elif choice == "Diary":
-    st.header("📖 Skin Cancer Diary")
-    df = pd.read_csv(DIARY_CSV)
-    df['timestamp'] = pd.to_datetime(df['timestamp'])
-    if df.empty:
-        st.info("No diary entries yet.")
-    else:
-        mole_ids = sorted(df['mole_id'].unique())
-        sel = st.selectbox("Select Mole to View", ['All'] + mole_ids, key="diary_sel")
-        if sel == 'All':
-            # Display moles in columns (max 3 per row)
-            chunks = [mole_ids[i:i+3] for i in range(0, len(mole_ids), 3)]
-            for group in chunks:
-                cols = st.columns(len(group))
-                for col, mid in zip(cols, group):
-                    with col:
-                        st.subheader(mid)
-                        entries = df[df['mole_id'] == mid].sort_values('timestamp')
-                        # Show image timeline
-                        for _, row in entries.iterrows():
-                            if os.path.exists(row['image_path']):
-                                st.image(
-                                    row['image_path'],
-                                    width=150,
-                                    caption=f"{row['timestamp'].strftime('%Y-%m-%d')} — {row['score']:.2f}"
-                                )
-                        st.write(f"Total scans: {len(entries)}")
-        else:
-            # Detailed view for a single mole
-            entries = df[df['mole_id'] == sel].sort_values('timestamp')
-            if entries.empty:
-                st.warning(f"No entries for {sel}.")
-            else:
-                # Score over time
-                st.line_chart(entries.set_index('timestamp')['score'])
-                st.markdown("#### Image Timeline")
-                for _, row in entries.iterrows():
-                    if os.path.exists(row['image_path']):
-                        st.image(
-                            row['image_path'],
-                            width=200,
-                            caption=(
-                                f"{row['timestamp'].strftime('%Y-%m-%d %H:%M')} — "
-                                f"Score: {row['score']:.2f}"
-                            )
-                        )
-                st.markdown("#### Details")
-                st.dataframe(
-                    entries[
-                        ['timestamp','geo_location','label','score',
-                         'body_location','prior_consultation','pain','itch']
-                    ]
-                    .rename(columns={
-                        'timestamp':'Time','geo_location':'Location',
-                        'label':'Diagnosis','score':'Confidence',
-                        'body_location':'Body Part','prior_consultation':'Prior Consult',
-                        'pain':'Pain','itch':'Itch'
-                    })
-                    .sort_values('Time', ascending=False)
-                )
-
-else:
-    st.header("📂 Dataset Explorer")
-    st.write("Preview images from the Harvard Skin Cancer Dataset")
-
-    # pick up to 15 image files
-    image_files = [
-    f for f in os.listdir(DATA_DIR)
-    if os.path.isfile(os.path.join(DATA_DIR, f))
-    and f.lower().endswith((".jpg", ".jpeg", ".png"))
-    ][:15]
-
-    for i in range(0, len(image_files), 3):
-        cols = st.columns(3)
-        for col, fn in zip(cols, image_files[i : i + 3]):
-            path = os.path.join(DATA_DIR, fn)
-            img = Image.open(path)
-            col.image(img, use_container_width=True)
-            col.caption(fn)
-
-st.sidebar.markdown("---")
-st.sidebar.write("Dataset powered by Harvard Dataverse [DBW86T]")
-st.sidebar.write(f"Model: {MODEL_NAME}")
-st.sidebar.write(f"LLM: {LLM_NAME}")
-
-if __name__ == '__main__':
-    st.write()
+import os
+import streamlit as st
+from PIL import Image
+import pandas as pd
+from datetime import datetime
+from transformers import (
+    AutoFeatureExtractor,
+    AutoModelForImageClassification,
+    AutoTokenizer,
+    AutoModelForSeq2SeqLM,
+    pipeline,
+)
+import requests
+from geopy.geocoders import Nominatim
+import folium
+from streamlit_folium import st_folium
+import cv2
+import numpy as np
+
+
+st.set_page_config(page_title="Skin Cancer Dashboard", layout="wide")
+
+# --- Configuration ---
+# Ensure you have set your Hugging Face token as an environment variable:
+# export HF_TOKEN="YOUR_TOKEN_HERE"
+MODEL_NAME = "Anwarkh1/Skin_Cancer-Image_Classification"
+LLM_NAME = "google/flan-t5-xl"
+HF_TOKEN   = ".."
+DATA_DIR = "data/harvard_dataset"  # Path where you download and unpack the Harvard Dataverse dataset
+DIARY_CSV = "diary.csv"
+CANCER_DIR = r"D:\Models\googleflan-t5-xl"
+LLM_DIR = r"D:\Models\SkinCancer"
+
+# Initialize session state defaults
+if 'initialized' not in st.session_state:
+    st.session_state['label'] = None
+    st.session_state['score'] = None
+    st.session_state['mole_id'] = ''
+    st.session_state['geo_location'] = ''
+    st.session_state['chat_history'] = []
+    st.session_state['initialized'] = True
+
+# Initialize geolocator for free geocoding
+geolocator = Nominatim(user_agent="skin-dashboard", timeout = 10)
+
+# --- Load Model & Feature Extractor ---
+@st.cache_resource
+def load_image_model(token: str):
+    extractor = AutoFeatureExtractor.from_pretrained(
+        MODEL_NAME,
+        use_auth_token=token
+    )
+    model = AutoModelForImageClassification.from_pretrained(
+        MODEL_NAME,
+        use_auth_token=token
+    )
+    return pipeline(
+        "image-classification",
+        model=model,
+        feature_extractor=extractor,
+        device=0  # set to GPU index or -1 for CPU
+    )
+
+@st.cache_resource
+def load_llm(token: str):
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        LLM_NAME,
+        use_auth_token=token
+    )
+    # Use Seq2SeqLM for T5-style (text2text) models:
+    model = AutoModelForSeq2SeqLM.from_pretrained(
+        LLM_NAME,
+        use_auth_token=token,
+    )
+    return pipeline(
+        "text2text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        device_map="auto",    # or device=0 for single GPU / -1 for CPU
+        max_length=10000,
+        num_beams=5,
+        no_repeat_ngram_size=2,
+        early_stopping=True,
+
+    )
+classifier = load_image_model(HF_TOKEN) if HF_TOKEN else None
+explainer = load_llm(HF_TOKEN) if HF_TOKEN else None
+
+# --- Diary Init ----
+
+if not os.path.exists(DIARY_CSV):
+    pd.DataFrame(
+        columns=["timestamp", "image_path", "mole_id", "geo_location", "label", "score",
+                 "body_location", "prior_consultation", "pain", "itch"]
+    ).to_csv(DIARY_CSV, index=False)
+
+# --- Save entry helper
+
+def save_entry(img_path: str, mole_id: str, geo_location: str,
+               label: str, score: float,
+               body_location: str, prior_consult: str, pain: str, itch: str):
+    df = pd.read_csv(DIARY_CSV)
+    entry = {
+        "timestamp": datetime.now().isoformat(),
+        "image_path": img_path,
+        "mole_id": mole_id,
+        "geo_location": geo_location,
+        "label": label,
+        "score": float(score),
+        "body_location": body_location,
+        "prior_consultation": prior_consult,
+        "pain": pain,
+        "itch": itch
+    }
+    df.loc[len(df)] = entry
+    df.to_csv(DIARY_CSV, index=False)
+
+# --- Preprocessing Functions ---
+def remove_hair(img: np.ndarray) -> np.ndarray:
+    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (17, 17))
+    blackhat = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, kernel)
+    _, mask = cv2.threshold(blackhat, 10, 255, cv2.THRESH_BINARY)
+    return cv2.inpaint(img, mask, 1, cv2.INPAINT_TELEA)
+
+
+def preprocess(img: Image.Image, size: int = 224) -> Image.Image:
+    arr = np.array(img)
+    bgr = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
+    bgr = remove_hair(bgr)
+    bgr = cv2.bilateralFilter(bgr, d=9, sigmaColor=75, sigmaSpace=75)
+    lab = cv2.cvtColor(bgr, cv2.COLOR_BGR2LAB)
+    l, a, b = cv2.split(lab)
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+    cl = clahe.apply(l)
+    merged = cv2.merge((cl, a, b))
+    bgr = cv2.cvtColor(merged, cv2.COLOR_LAB2BGR)
+    h, w = bgr.shape[:2]
+    scale = size / max(h, w)
+    nh, nw = int(h*scale), int(w*scale)
+    resized = cv2.resize(bgr, (nw, nh), interpolation=cv2.INTER_AREA)
+    canvas = np.full((size, size, 3), 128, dtype=np.uint8)
+    top, left = (size-nh)//2, (size-nw)//2
+    canvas[top:top+nh, left:left+nw] = resized
+    rgb = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB)
+    return Image.fromarray(rgb)
+
+# -----Streamlit layout ---- 
+st.title("🩺 Skin Cancer Recognition Dashboard")
+menu = ["Scan Mole","Chat","Diary", "Dataset Explorer"]
+choice = st.sidebar.selectbox("Navigation", menu)
+
+# --- Initialize Scan a Mole ---
+if choice == "Scan Mole":
+    st.header("🔍 Scan a Mole")
+    if not classifier:
+        st.error("Missing HF_TOKEN.")
+        st.stop()
+
+    upload = st.file_uploader("Upload a skin image", type=["jpg","jpeg","png"])
+    if not upload:
+        st.info("Please upload an image to begin.")
+        st.stop()
+
+    raw = Image.open(upload).convert("RGB")
+    st.image(raw, caption="Original", use_container_width=True)
+
+    proc = preprocess(raw)
+    st.image(proc, caption="Preprocessed", use_container_width=True)
+
+    mole = st.text_input("Mole ID")
+    city = st.text_input("Geographic location")
+    body = st.selectbox("Body location", ["Face","Scalp","Neck","Chest","Back","Arm","Hand","Leg","Foot","Other"])
+    prior = st.radio("Prior consult?", ["Yes","No"], horizontal=True)
+    pain = st.radio("Pain?", ["Yes","No"], horizontal=True)
+    itch = st.radio("Itch?", ["Yes","No"], horizontal=True)
+
+    if st.button("Classify"):
+        if not mole or not city:
+            st.error("Enter ID and location.")
+        else:
+            with st.spinner("Analyzing..."):
+                out = classifier(proc)
+            lbl, scr = out[0]["label"], out[0]["score"]
+            save_dir = os.path.join("scans", f"{mole}_{datetime.now().timestamp()}.png")
+            os.makedirs(os.path.dirname(save_dir), exist_ok=True)
+            raw.save(save_dir)
+            save_entry(save_dir, mole, city, lbl, scr, body, prior, pain, itch)
+            st.session_state.update({
+                'label': lbl,
+                'score': scr,
+                'mole_id': mole,
+                'geo_location': city
+            })
+
+    if st.session_state['label']:
+        st.success(f"Prediction: {st.session_state['label']} (score {st.session_state['score']:.2f})")
+        if explainer:
+            with st.spinner("Explaining..."):
+                text = explainer(f"Explain {st.session_state['label']} and recommendation.")[0]['generated_text']
+            st.markdown("### Explanation"); st.write(text)
+
+        loc = geolocator.geocode(st.session_state['geo_location'])
+        if loc:
+            m = folium.Map([loc.latitude, loc.longitude], zoom_start=12)
+            folium.Marker([loc.latitude, loc.longitude], "You").add_to(m)
+            resp = requests.post(
+                "https://overpass-api.de/api/interpreter",
+                data={"data": f"[out:json];node(around:5000,{loc.latitude},{loc.longitude})[~\"^(amenity|healthcare)$\"~\"clinic|doctors\"];out;"}
+            )
+            for el in resp.json().get('elements', []):
+                tags = el.get('tags', {});
+                lat = el.get('lat') or el['center']['lat']; lon = el.get('lon') or el['center']['lon']
+                folium.Marker([lat, lon], tags.get('name','Clinic')).add_to(m)
+            st.markdown("### Nearby Clinics"); st_folium(m, width=700)
+
+# --- Chat Tab ---
+elif choice == "Chat":
+    st.header("💬 Follow-Up Chat")
+    if not st.session_state['label']:
+        st.info("Please perform a scan first in the 'Scan Mole' tab.")
+    else:
+        lbl = st.session_state['label']
+        scr = st.session_state['score']
+        mid = st.session_state['mole_id']
+        gloc = st.session_state['geo_location']
+        st.markdown(f"**Context:** prediction for **{mid}** at **{gloc}** is **{lbl}** (confidence {scr:.2f}).")
+
+        # New user message comes first for immediate loop
+        user_q = st.chat_input("Ask a follow-up question:", key="chat_input")
+        if user_q and explainer:
+            st.session_state['chat_history'].append({'role':'user','content':user_q})
+            system_p = "You are a dermatology assistant. Provide concise medical advice without clarifying questions."
+            tpl = (
+                f"{system_p}\nContext: prediction is {lbl} with confidence {scr:.2f}.\n"
+                f"User: {user_q}\nAssistant:"
+            )
+            with st.spinner("Generating response..."):
+                reply = explainer(tpl)[0]['generated_text']
+            st.session_state['chat_history'].append({'role':'assistant','content':reply})
+
+        # Display the updated chat history
+        for msg in st.session_state['chat_history']:
+            prefix = 'You' if msg['role']=='user' else 'AI'
+            st.markdown(f"**{prefix}:** {msg['content']}")
+
+
+# --- Diary Page ---
+elif choice == "Diary":
+    st.header("📖 Skin Cancer Diary")
+    df = pd.read_csv(DIARY_CSV)
+    df['timestamp'] = pd.to_datetime(df['timestamp'])
+    if df.empty:
+        st.info("No diary entries yet.")
+    else:
+        mole_ids = sorted(df['mole_id'].unique())
+        sel = st.selectbox("Select Mole to View", ['All'] + mole_ids, key="diary_sel")
+        if sel == 'All':
+            # Display moles in columns (max 3 per row)
+            chunks = [mole_ids[i:i+3] for i in range(0, len(mole_ids), 3)]
+            for group in chunks:
+                cols = st.columns(len(group))
+                for col, mid in zip(cols, group):
+                    with col:
+                        st.subheader(mid)
+                        entries = df[df['mole_id'] == mid].sort_values('timestamp')
+                        # Show image timeline
+                        for _, row in entries.iterrows():
+                            if os.path.exists(row['image_path']):
+                                st.image(
+                                    row['image_path'],
+                                    width=150,
+                                    caption=f"{row['timestamp'].strftime('%Y-%m-%d')} — {row['score']:.2f}"
+                                )
+                        st.write(f"Total scans: {len(entries)}")
+        else:
+            # Detailed view for a single mole
+            entries = df[df['mole_id'] == sel].sort_values('timestamp')
+            if entries.empty:
+                st.warning(f"No entries for {sel}.")
+            else:
+                # Score over time
+                st.line_chart(entries.set_index('timestamp')['score'])
+                st.markdown("#### Image Timeline")
+                for _, row in entries.iterrows():
+                    if os.path.exists(row['image_path']):
+                        st.image(
+                            row['image_path'],
+                            width=200,
+                            caption=(
+                                f"{row['timestamp'].strftime('%Y-%m-%d %H:%M')} — "
+                                f"Score: {row['score']:.2f}"
+                            )
+                        )
+                st.markdown("#### Details")
+                st.dataframe(
+                    entries[
+                        ['timestamp','geo_location','label','score',
+                         'body_location','prior_consultation','pain','itch']
+                    ]
+                    .rename(columns={
+                        'timestamp':'Time','geo_location':'Location',
+                        'label':'Diagnosis','score':'Confidence',
+                        'body_location':'Body Part','prior_consultation':'Prior Consult',
+                        'pain':'Pain','itch':'Itch'
+                    })
+                    .sort_values('Time', ascending=False)
+                )
+
+else:
+    st.header("📂 Dataset Explorer")
+    st.write("Preview images from the Harvard Skin Cancer Dataset")
+
+    # pick up to 15 image files
+    image_files = [
+    f for f in os.listdir(DATA_DIR)
+    if os.path.isfile(os.path.join(DATA_DIR, f))
+    and f.lower().endswith((".jpg", ".jpeg", ".png"))
+    ][:15]
+
+    for i in range(0, len(image_files), 3):
+        cols = st.columns(3)
+        for col, fn in zip(cols, image_files[i : i + 3]):
+            path = os.path.join(DATA_DIR, fn)
+            img = Image.open(path)
+            col.image(img, use_container_width=True)
+            col.caption(fn)
+
+st.sidebar.markdown("---")
+st.sidebar.write("Dataset powered by Harvard Dataverse [DBW86T]")
+st.sidebar.write(f"Model: {MODEL_NAME}")
+st.sidebar.write(f"LLM: {LLM_NAME}")
+
+if __name__ == '__main__':
+    st.write()