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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+import io
+import os
+
+# ══════════════════════════════════════════════════════════════
+# Flask App Setup
+# ══════════════════════════════════════════════════════════════
+app = Flask(__name__)
+CORS(app)  # Allow mobile app to connect
+
+# ══════════════════════════════════════════════════════════════
+# Configuration
+# ══════════════════════════════════════════════════════════════
+MODEL_PATH   = "resnet18_rice_v2.pth"   # ← your saved model
+CLASS_NAMES  = ["ClassA-Drought", "ClassB-PestInfestation", "ClassC-Healthy"]
+THRESHOLD    = 0.80                      # below this = uncertain
+IMG_SIZE     = 224
+device       = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# ══════════════════════════════════════════════════════════════
+# Load Model Once at Startup
+# ══════════════════════════════════════════════════════════════
+def load_model():
+    model = models.resnet18(weights=None)
+    model.fc = nn.Linear(model.fc.in_features, len(CLASS_NAMES))
+    checkpoint = torch.load(MODEL_PATH, map_location=device)
+    model.load_state_dict(checkpoint["model_state_dict"])
+    model = model.to(device)
+    model.eval()
+    print(f"✅ Model loaded from {MODEL_PATH}")
+    print(f"✅ Classes: {CLASS_NAMES}")
+    return model
+
+model = load_model()
+
+# ══════════════════════════════════════════════════════════════
+# Image Transform
+# ══════════════════════════════════════════════════════════════
+transform = transforms.Compose([
+    transforms.Resize((IMG_SIZE, IMG_SIZE)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                          std=[0.229, 0.224, 0.225])
+])
+
+# ══════════════════════════════════════════════════════════════
+# Routes
+# ══════════════════════════════════════════════════════════════
+
+# Health check — test if API is running
+@app.route("/", methods=["GET"])
+def home():
+    return jsonify({
+        "status"  : "running",
+        "message" : "Rice Stress Detection API",
+        "classes" : CLASS_NAMES,
+        "model"   : "ResNet18 — 99.66% accuracy"
+    })
+
+# Main prediction endpoint
+@app.route("/predict", methods=["POST"])
+def predict():
+    # ── Check if image was sent ─────────────────────────────
+    if "image" not in request.files:
+        return jsonify({
+            "error": "No image provided. Send image as form-data with key 'image'"
+        }), 400
+
+    file = request.files["image"]
+
+    # ── Check file is valid ─────────────────────────────────
+    if file.filename == "":
+        return jsonify({"error": "Empty filename"}), 400
+
+    allowed = {"jpg", "jpeg", "png", "bmp", "webp"}
+    ext = file.filename.rsplit(".", 1)[-1].lower()
+    if ext not in allowed:
+        return jsonify({"error": f"File type .{ext} not allowed. Use jpg/png"}), 400
+
+    try:
+        # ── Read and process image ──────────────────────────
+        image_bytes = file.read()
+        image       = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        tensor      = transform(image).unsqueeze(0).to(device)
+
+        # ── Run prediction ──────────────────────────────────
+        with torch.no_grad():
+            outputs    = model(tensor)
+            probs      = torch.softmax(outputs, dim=1)[0]
+            confidence, predicted = torch.max(probs, 0)
+
+        predicted_class = CLASS_NAMES[predicted.item()]
+        conf_value      = confidence.item()
+        is_uncertain    = conf_value < THRESHOLD
+
+        # ── Build response ──────────────────────────────────
+        all_probs = {
+            CLASS_NAMES[i]: round(probs[i].item() * 100, 2)
+            for i in range(len(CLASS_NAMES))
+        }
+
+        # Clean class name for display
+        display_name = predicted_class.replace("ClassA-", "")\
+                                      .replace("ClassB-", "")\
+                                      .replace("ClassC-", "")
+
+        response = {
+            "predicted_class" : predicted_class,
+            "display_name"    : display_name,
+            "confidence"      : round(conf_value * 100, 2),
+            "is_uncertain"    : is_uncertain,
+            "all_probabilities": all_probs,
+            "recommendation"  : get_recommendation(display_name, is_uncertain)
+        }
+
+        return jsonify(response), 200
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+
+# ══════════════════════════════════════════════════════════════
+# Recommendation Logic
+# ══════════════════════════════════════════════════════════════
+def get_recommendation(class_name, is_uncertain):
+    if is_uncertain:
+        return "Low confidence — recommend manual inspection by agronomist"
+
+    recommendations = {
+        "Drought": (
+            "Rice plant shows drought stress. "
+            "Recommended actions: increase irrigation frequency, "
+            "check soil moisture levels, consider mulching."
+        ),
+        "PestInfestation": (
+            "Rice plant shows pest infestation signs. "
+            "Recommended actions: inspect leaves for insects, "
+            "apply appropriate pesticide, monitor surrounding plants."
+        ),
+        "Healthy": (
+            "Rice plant appears healthy. "
+            "Continue regular monitoring and maintenance."
+        )
+    }
+    return recommendations.get(class_name, "No recommendation available")
+
+
+# ══════════════════════════════════════════════════════════════
+# Run Server
+# ══════════════════════════════════════════════════════════════
+if __name__ == "__main__":
+   port = int(os.environ.get("PORT", 7860))
+print("🌾 Rice Stress Detection API starting...")
+app.run(debug=False, host="0.0.0.0", port=port)

requirements.txt

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+flask==3.0.0
+flask-cors==4.0.0
+torch==2.1.0
+torchvision==0.16.0
+Pillow==10.0.0

resnet18_rice_v2.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3288b9d17663c0fbc42a2d6d9609659544d019ac42ca129e602ad018804d19a8
+size 128778411