Add application file

main.py

@@ -1,97 +0,0 @@
-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)