Update app.py

app.py

@@ -1,57 +1,49 @@
-import gradio as gr
+import json
+import numpy as np
 from PIL import Image, ImageOps
-import torch
-from transformers import CLIPProcessor, CLIPModel
-
-# ─── 1) Carrega modelo e processor CLIP fine-tuned ───
-MODEL_ID = "Keetawan/clip-vit-large-patch14-plant-disease-finetuned"
-processor = CLIPProcessor.from_pretrained(MODEL_ID)
-model     = CLIPModel.from_pretrained(MODEL_ID)
-
-# ─── 2) Labels que o modelo conhece ───
-HF_LABELS = [
-    "Grape leaf with Black rot",
-    "Grape leaf with Esca (Black Measles)",
-    "Grape leaf with Leaf blight (Isariopsis Leaf Spot)",
-    "Healthy Grape leaf"
-]
-# Mapeamento para as tuas classes curtas
-MAP = {
-    "Grape leaf with Black rot": "Black Rot",
-    "Grape leaf with Esca (Black Measles)": "ESCA",
-    "Grape leaf with Leaf blight (Isariopsis Leaf Spot)": "Leaf Blight",
-    "Healthy Grape leaf": "Healthy"
-}
-
-def predict(img: Image.Image):
-    # Pré-processamento igual ao notebook
-    img = ImageOps.exif_transpose(img).convert("RGB")
-    img = img.resize((224,224))
-
-    # Zero-shot inference CLIP
-    inputs = processor(text=HF_LABELS, images=img, return_tensors="pt", padding=True)
-    outputs = model(**inputs)
-    probs   = outputs.logits_per_image.softmax(dim=1)[0].tolist()
-
-    # Constrói dicionário label→prob
-    mapping = { MAP[HF_LABELS[i]]: probs[i] for i in range(len(probs)) }
-    # Escolhe a classe de maior probabilidade
-    best = max(mapping, key=mapping.get)
-
-    # Formata as probabilidades
-    prob_lines = "\n".join(f"{lbl}: {mapping[lbl]:.2f}" for lbl in ["Healthy","Leaf Blight","Black Rot","ESCA"])
-    return best, prob_lines
-
-# ─── 3) UI Gradio ───────────────────────────────────────
+import gradio as gr
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+
+# —————————————————————————————————
+# 1) Carregar o modelo e o label_map
+MODEL_PATH = "cropvision_model.keras"   # ou ".h5" se for esse o caso
+CLASS_ORDER = json.load(open("label_map.json"))
+
+model = load_model(MODEL_PATH)
+
+# —————————————————————————————————
+# 2) Função de prediction
+IMG_SIZE = (224, 224)
+
+def predict(image: Image.Image):
+    # 2.1 Corrige rotação EXIF e dimensiona
+    img = ImageOps.exif_transpose(image).convert("RGB")
+    img = ImageOps.fit(img, IMG_SIZE, Image.Resampling.LANCZOS)
+
+    # 2.2 Converte para array e normaliza
+    arr = img_to_array(img) / 255.0
+    arr = np.expand_dims(arr, 0)  # shape (1,224,224,3)
+
+    # 2.3 Inferência
+    probs = model.predict(arr)[0]  # array de 4 valores
+    idx   = int(np.argmax(probs))
+    label = CLASS_ORDER[idx]
+
+    # 2.4 Mapeia todas as probabilidades
+    mapping = {CLASS_ORDER[i]: float(probs[i]) for i in range(len(probs))}
+    # Formata saída
+    prob_lines = "\n".join(f"{lbl}: {mapping[lbl]:.2f}" for lbl in CLASS_ORDER)
+    return label, prob_lines
+
+# —————————————————————————————————
+# 3) Interface Gradio
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil", label="Carrega uma folha"),
-    outputs=[
-      gr.Textbox(label="Classe predita"),
-      gr.Textbox(label="Probabilidades")
-    ],
-    title="CropVision – CLIP Zero-Shot Fine-Tuned",
-    description="Healthy / Leaf Blight / Black Rot / ESCA"
+    outputs=[gr.Textbox(label="Classe"), gr.Textbox(label="Probabilidades")],
+    title="CropVision",
+    description="Classifica folhas de videira em Healthy, Leaf Blight, Black Rot ou ESCA"
 )
 
 if __name__ == "__main__":