Fix: Set FLASK_APP=app:app for Hugging Face

Dockerfile

@@ -1,19 +1,16 @@
 FROM python:3.11
 
 WORKDIR /app
-
-# Copy all code
 COPY . .
 
-# Install dependencies
 RUN pip install --upgrade pip && pip install -r requirements.txt
 
-# Expose port for Hugging Face
 EXPOSE 7860
 
-# Run Flask
-ENV FLASK_APP=app.py
+# Fix here 👇
+ENV FLASK_APP=app:app
 ENV FLASK_RUN_HOST=0.0.0.0
 ENV FLASK_RUN_PORT=7860
+
 CMD ["flask", "run"]
 

app.py

@@ -0,0 +1,132 @@
+from flask import Flask, request, jsonify, render_template, send_from_directory
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+from huggingface_hub import InferenceClient
+from PIL import Image
+import torch
+import os
+
+app = Flask(__name__)
+
+# =======================================
+# 🔐 Hugging Face LLM Token + InferenceClient
+# =======================================
+HUGGINGFACE_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")
+
+client = InferenceClient(
+    model="mistralai/Mistral-7B-Instruct-v0.1",
+    token=HUGGINGFACE_TOKEN
+)
+
+# =======================================
+# 🧠 Load Skin Disease Model
+# =======================================
+print("Loading skin condition classifier...")
+model_name = "Jayanth2002/dinov2-base-finetuned-SkinDisease"
+image_model = AutoModelForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Class labels
+class_names = [
+    'Basal Cell Carcinoma', 'Darier_s Disease', 'Epidermolysis Bullosa Pruriginosa',
+    'Hailey-Hailey Disease', 'Herpes Simplex', 'Impetigo', 'Larva Migrans',
+    'Leprosy Borderline', 'Leprosy Lepromatous', 'Leprosy Tuberculoid', 'Lichen Planus',
+    'Lupus Erythematosus Chronicus Discoides', 'Melanoma', 'Molluscum Contagiosum',
+    'Mycosis Fungoides', 'Neurofibromatosis', 'Papilomatosis Confluentes And Reticulate',
+    'Pediculosis Capitis', 'Pityriasis Rosea', 'Porokeratosis Actinic', 'Psoriasis',
+    'Tinea Corporis', 'Tinea Nigra', 'Tungiasis', 'actinic keratosis', 'dermatofibroma',
+    'nevus', 'pigmented benign keratosis', 'seborrheic keratosis', 'squamous cell carcinoma',
+    'vascular lesion'
+]
+
+# =======================================
+# 🌐 Frontend Routes
+# =======================================
+@app.route("/")
+def index():
+    return render_template("index.html")
+
+@app.route("/upload")
+def upload():
+    return render_template("upload.html")
+
+@app.route("/result")
+def result():
+    return render_template("result.html")  # Notice: matches the filename "results.html" instead of "result.html"
+
+# =======================================
+# 📸 /analyze Route
+# =======================================
+@app.route('/analyze', methods=['POST'])
+def analyze():
+    if 'image' not in request.files:
+        return jsonify({"error": "No image uploaded"}), 400
+
+    image_file = request.files['image']
+    image = Image.open(image_file.stream).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        logits = image_model(**inputs).logits
+        probs = torch.softmax(logits, dim=-1)[0]
+
+    top_idx = torch.argmax(probs).item()
+    top_conf = probs[top_idx].item()
+    prediction = class_names[top_idx]
+
+    top_conditions = sorted(
+        zip(class_names, probs.tolist()),
+        key=lambda x: x[1],
+        reverse=True
+    )[:5]
+
+    return jsonify({
+        "prediction": prediction,
+        "confidence": round(top_conf, 4),
+        "topConditions": [(name, round(prob, 4)) for name, prob in top_conditions],
+        "description": f"{prediction} is a skin condition. Please consult a medical professional.",
+        "recommendations": [
+            "Take a clearer image if unsure.",
+            "Consider visiting a dermatologist.",
+            "Avoid self-diagnosis or self-treatment."
+        ]
+    })
+
+# =======================================
+# 💬 /ask Route
+# =======================================
+@app.route('/ask', methods=['POST'])
+def ask():
+    data = request.json
+    question = data.get("question", "")
+    condition = data.get("condition", "")
+
+    if not question:
+        return jsonify({"answer": "Please ask a valid question."}), 400
+
+    messages = [
+        {
+            "role": "user",
+            "content": f"A user may have {condition}. They asked: '{question}'. Respond like a helpful AI medical assistant."
+        }
+    ]
+
+    try:
+        response = client.chat_completion(
+            messages=messages,
+            max_tokens=200
+        )
+        answer = response.choices[0]["message"]["content"]
+        return jsonify({"answer": answer.strip()})
+    except Exception as e:
+        return jsonify({"answer": f"Error communicating with Hugging Face: {e}"}), 500
+
+# Add route for placeholder images if needed
+@app.route('/api/placeholder/<width>/<height>')  
+def placeholder(width, height):
+    # This is a simple implementation - you might want to generate an actual placeholder image
+    # For now, we'll just serve a static placeholder
+    return send_from_directory('static/images', 'placeholder.jpg')
+
+
+if __name__ == '__main__':
+    app.run(debug=True)