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app.py

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+import io, json
+import numpy as np
+import streamlit as st
+import torch
+from PIL import Image
+import torchvision.transforms as T
+import timm
+import requests
+
+# -----------------------------
+# CONFIG
+# -----------------------------
+MODEL_PATH = "skin_model.pth"
+CLASSES_PATH = "classes.json"
+
+TIMM_MODEL_NAME = "efficientnet_b0"
+IMG_SIZE = 224
+TOPK = 3
+
+# Ollama (FREE local LLM). If you don't want LLM, set USE_LLM=False
+USE_LLM = True
+OLLAMA_URL = "http://localhost:11434/api/generate"
+OLLAMA_MODEL = "phi3:mini"   # or "mistral:7b", "llama3.1:8b"
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# -----------------------------
+# Severity rules (simple demo)
+# -----------------------------
+SEVERITY_RULES = {
+    "tumor_malignant": ("urgent", True),
+    "bullous": ("urgent", True),
+    "systemic": ("urgent", True),
+    "bacterial": ("doctor_soon", True),
+    "autoimmune": ("doctor_soon", True),
+    "infestation_bite": ("doctor_soon", True),
+    "drug_exanthem": ("doctor_soon", True),
+
+    "fungal": ("monitor", False),
+    "viral": ("monitor", False),
+    "eczema_dermatitis": ("monitor", False),
+    "psoriasis_lichen": ("monitor", False),
+    "tumor_benign": ("monitor", False),
+    "hives": ("monitor", False),
+    "pigment": ("monitor", False),
+    "hair_nail": ("monitor", False),
+
+    "acne_rosacea": ("self_care", False),
+}
+
+def severity_from_label(label: str, symptoms: str):
+    sev, consult = SEVERITY_RULES.get(label, ("monitor", False))
+    s = symptoms.lower()
+    red_flags = ["fever", "bleeding", "pus", "spreading fast", "severe pain", "difficulty breathing", "black", "rapidly growing"]
+    if any(k in s for k in red_flags):
+        sev, consult = "urgent", True
+    return sev, consult
+
+# -----------------------------
+# Load model + classes (cached)
+# -----------------------------
+@st.cache_resource
+def load_model_and_classes():
+    with open(CLASSES_PATH, "r") as f:
+        classes = json.load(f)
+
+    num_classes = len(classes)
+
+    model = timm.create_model(TIMM_MODEL_NAME, pretrained=False, num_classes=num_classes)
+    state = torch.load(MODEL_PATH, map_location="cpu")
+    model.load_state_dict(state, strict=True)
+    model.to(DEVICE)
+    model.eval()
+    return model, classes
+
+# EfficientNet preprocessing (same as training)
+transform = T.Compose([
+    T.Resize((IMG_SIZE, IMG_SIZE)),
+    T.ToTensor(),
+    T.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225)),
+])
+
+@torch.no_grad()
+def predict_image(model, pil_img, classes):
+    x = transform(pil_img.convert("RGB")).unsqueeze(0).to(DEVICE)
+    logits = model(x)                     # raw scores
+    probs = torch.softmax(logits, dim=1).squeeze(0)  # convert to probabilities
+
+    topk = torch.topk(probs, k=min(TOPK, len(classes)))
+    results = []
+    for idx, score in zip(topk.indices.tolist(), topk.values.tolist()):
+        results.append({"label": classes[idx], "confidence": float(score)})
+    return results
+
+def call_ollama(prompt: str) -> str:
+    payload = {
+        "model": OLLAMA_MODEL,
+        "prompt": prompt,
+        "stream": False,
+        "options": {"temperature": 0.3}
+    }
+    r = requests.post(OLLAMA_URL, json=payload, timeout=60)
+    r.raise_for_status()
+    return r.json().get("response", "").strip()
+
+def build_prompt(symptoms, top3, severity, doctor_consult):
+    return f"""
+You are a health assistant for a university hackathon demo.
+Be careful and do NOT diagnose with certainty.
+
+User symptoms:
+{symptoms}
+
+Image model top-3 predictions:
+{top3}
+
+Severity decision:
+severity={severity}, doctor_consult={doctor_consult}
+
+Explain in simple English:
+- What top prediction means
+- What to do now (safe steps)
+- When to see a doctor (based on severity + red flags)
+- Ask 2 follow-up questions
+Add: "Not medical advice"
+""".strip()
+
+# -----------------------------
+# Streamlit UI
+# -----------------------------
+st.set_page_config(page_title="Skin Disease Demo", page_icon="🧴", layout="centered")
+
+st.title("🧴 Skin Disease Prediction Demo")
+st.write("Upload a skin image + type symptoms text. The model shows **Top-3 predictions** and a simple severity suggestion.")
+
+model, classes = load_model_and_classes()
+
+st.caption(f"Running on **{DEVICE.upper()}** | Model: {TIMM_MODEL_NAME} | Classes: {len(classes)}")
+
+img_file = st.file_uploader("Upload skin image (jpg/png)", type=["jpg", "jpeg", "png"])
+symptoms = st.text_area("Symptoms (example: itchy red patch, burning, spreading, fever?)", height=100)
+
+colA, colB = st.columns(2)
+with colA:
+    use_llm = st.checkbox("Use LLM explanation (Ollama)", value=USE_LLM)
+with colB:
+    st.write("")
+
+if img_file is not None:
+    pil_img = Image.open(io.BytesIO(img_file.read()))
+    st.image(pil_img, caption="Uploaded Image", use_container_width=True)
+
+    if st.button("Predict"):
+        top3 = predict_image(model, pil_img, classes)
+        top1 = top3[0]
+        severity, doctor_consult = severity_from_label(top1["label"], symptoms)
+
+        st.subheader("✅ Prediction")
+        st.write(f"**Top-1:** `{top1['label']}`  —  **Confidence:** `{top1['confidence']*100:.2f}%`")
+
+        # Confidence bar
+        st.progress(min(int(top1["confidence"] * 100), 100))
+
+        st.subheader("Top-3 (recommended in demo)")
+        for i, item in enumerate(top3, start=1):
+            st.write(f"**{i}.** `{item['label']}` — `{item['confidence']*100:.2f}%`")
+
+        st.subheader("⚠️ Severity suggestion (rule-based)")
+        st.write(f"**Severity:** `{severity}`")
+        st.write(f"**Doctor consult needed?** `{doctor_consult}`")
+
+        st.info("This is a demo/education tool. Not medical advice.")
+
+        # LLM explanation
+        if use_llm:
+            st.subheader("🧠 LLM Explanation (simple language)")
+            try:
+                prompt = build_prompt(symptoms, top3, severity, doctor_consult)
+                explanation = call_ollama(prompt)
+                st.write(explanation)
+            except Exception as e:
+                st.warning(f"LLM not available. Reason: {e}")
+                st.write("Tip: Start Ollama + pull a model (phi3:mini).")
+else:
+    st.warning("Upload an image to start.")

classes.json

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+["acne_rosacea", "autoimmune", "bacterial", "bullous", "drug_exanthem", "eczema_dermatitis", "fungal", "hair_nail", "hives", "infestation_bite", "pigment", "psoriasis_lichen", "systemic", "tumor_benign", "tumor_malignant", "viral"]

metrics.json

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+{
+  "train_mode": "GROUPED_8",
+  "top1_accuracy": 0.6904047976011994,
+  "top3_accuracy": 0.8405797101449275,
+  "f1_macro": 0.6349998743100523,
+  "f1_weighted": 0.687291371392067,
+  "classes": [
+    "acne_rosacea",
+    "autoimmune",
+    "bacterial",
+    "bullous",
+    "drug_exanthem",
+    "eczema_dermatitis",
+    "fungal",
+    "hair_nail",
+    "hives",
+    "infestation_bite",
+    "pigment",
+    "psoriasis_lichen",
+    "systemic",
+    "tumor_benign",
+    "tumor_malignant",
+    "viral"
+  ]
+}

skin_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:543f630f52f752fbe8e26fad84bbea764bc8ca8cefb689e493d7cc8d02cb4bc9
+size 16407865