Add application file

main.py

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+from fastapi import FastAPI, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
+from transformers import pipeline
+from PIL import Image
+from PIL import Image
+import numpy as np
+import uvicorn
+from tensorflow.keras.models import load_model
+from transformers import (AutoTokenizer)
+import os
+
+# === Inisialisasi FastAPI ===
+app = FastAPI()
+
+# === CORS (opsional) ===
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# === Load Model Klasifikasi Gambar ===
+model_path = os.path.join(os.path.dirname(__file__), "saved_model", "multidisease_model.h5")
+if not os.path.exists(model_path):
+    raise FileNotFoundError(f"Model tidak ditemukan di path: {model_path}")
+image_model = load_model(model_path)
+
+
+# Label (ubah sesuai model Anda)
+label_map = {
+    0: "BacterialBlight",
+    1: "Blast",
+    2: "Brownspot",
+    3: "Healthy",
+    4: "Leaf_Scald",
+    5: "Tungro",
+}
+label_descriptions = {
+    "BacterialBlight": "Penyakit akibat bakteri yang menyebabkan bercak air dan layu.",
+    "Blast": "Penyakit jamur yang menyerang leher malai dan daun.",
+    "Brownspot": "Terdapat bercak coklat bulat di permukaan daun.",
+    "Healthy": "Tanaman padi dalam kondisi sehat tanpa gejala penyakit.",
+    "Leaf_Scald": "Daun mengering dari ujung dan terbakar karena patogen atau cuaca ekstrem.",
+    "Tungro": "Penyakit virus yang membuat daun menguning dan pertumbuhan terhambat.",
+}
+
+# === Load Chatbot Pipeline ===
+chatbot = pipeline(
+    "text-generation",
+    model="ARusDian/AgroLens-Chatbot",
+    tokenizer="ARusDian/AgroLens-Chatbot",
+)
+
+
+def preprocess_image(image: Image.Image):
+    image = image.resize((224, 224))  # sesuaikan ukuran input model
+    img_array = np.array(image) / 255.0  # normalisasi
+    img_array = np.expand_dims(img_array, axis=0)  # tambahkan batch dimensi
+    return img_array
+
+
+# === Endpoint: Klasifikasi Gambar ===
+@app.post("/predict-image")
+async def predict_image(file: UploadFile = File(...)):
+    image = Image.open(file.file).convert("RGB")
+    input_tensor = preprocess_image(image)
+
+    pred = np.argmax(image_model.predict(input_tensor), axis=1)[0]
+    label = label_map.get(pred, "Tidak dikenal")
+    description = label_descriptions.get(label, "-")
+
+    return {"prediction": label, "description": description}
+
+
+# === Endpoint: Chatbot Deskripsi Penyakit ===
+@app.post("/chatbot")
+async def describe(prompt: dict):
+    text = prompt["prompt"]
+    tokenizer = AutoTokenizer.from_pretrained("ARusDian/AgroLens-Chatbot")
+    result = chatbot(
+        text,
+        max_new_tokens=120,
+        temperature=0.5,
+        top_p=0.85,
+        repetition_penalty=1.4,
+        no_repeat_ngram_size=5,
+        do_sample=True,
+        eos_token_id=tokenizer.eos_token_id,
+    )[0]["generated_text"]
+    return {"response": result}
+
+
+# === Run (opsional) ===
+if __name__ == "__main__":
+    uvicorn.run("main:app", host="0.0.0.0", port=8000)

requirements.txt

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+fastapi
+uvicorn
+pillow
+numpy
+tensorflow
+transformers
+torch
+python-multipart
+tf-keras

saved_model/multidisease_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8a58caeeab7a7a80b91c0fd594bc6e3d9cf866a2c25db67535bc599379e2f0f4
+size 5174560