Update app.py

app.py

@@ -1,173 +1,30 @@
-import uvicorn
-import base64
-import io
-from pathlib import Path
-
-# Fastai imports
+import gradio as gr
 from fastai.vision.all import *
-
-# FastHTML imports
-from fasthtml.common import *  # Imports common HTML tags, App, etc.
-from fasthtml.core import FastHTML  # Base class if needed, but App is usually sufficient
-from fasthtml.components import FileInput  # Specific component for file input
-from fastcore.utils import *  # For Upload class
-
-# --- Configuration ---
-MODEL_PATH = Path(__file__).parent / 'model.pkl'
-defaults.device = torch.device('cpu')
-
-# --- Load Fastai Learner ---
-try:
-    print(f"Attempting to load model from: {MODEL_PATH.resolve()}")
-    if not MODEL_PATH.is_file():
-        raise FileNotFoundError(f"Model file not found at calculated path: {MODEL_PATH.resolve()}")
-    learn = load_learner(MODEL_PATH)
-    print("Model loaded successfully.")
-    # Get class names (vocab) from the learner's dataloaders
-    CLASS_NAMES = learn.dls.vocab
-    print(f"Model Classes: {CLASS_NAMES}")
-except FileNotFoundError as e:
-    print(f"Error: {e}")
-    print("Please make sure 'model.pkl' is in the same directory as app.py.")
-    raise SystemExit(f"CRITICAL ERROR: Model file not found at {MODEL_PATH}. Application cannot start.")
-except Exception as e:
-    print(f"CRITICAL ERROR: An unexpected error occurred loading the model: {e}")
-    raise SystemExit(f"CRITICAL ERROR: Failed to load model. Application cannot start. Error: {e}")
-
-# --- FastHTML App Setup ---
-app = FastHTML()
-rt = app.route  # Shortcut for the route decorator
-
-# --- Helper Function for Prediction ---
-def predict_image(img_bytes: bytes):
-    """Takes image bytes, predicts using the fastai model."""
-    if not img_bytes:
-        return "Error: Image data is empty", 0.0
-    try:
-        # Create PILImage from bytes
-        img = PILImage.create(img_bytes)
-        # Get prediction from the learner
-        pred_class, pred_idx, probs = learn.predict(img)
-        # Get the confidence score for the predicted class
-        confidence = probs[pred_idx].item()
-        return pred_class, confidence
-    except Exception as e:
-        print(f"Error during prediction: {e}")
-        return f"Prediction Error: Could not process image ({e})", 0.0
-
-# --- Define Routes ---
-@rt("/")
-async def get(request):
-    """Serves the main page with the upload form."""
-    return Titled("Fastai Image Classifier",
-        Main(
-            H1("Upload an Image for Classification"),
-            # --- Form for uploading the image ---
-            Form(
-                # Positional argument: Div that contains the file input
-                Div(
-                    FileInput(name="file", id="fileInput", cls="form-control", required=True, accept="image/*"),
-                    cls="mb-3"
-                ),
-                # Positional argument: Submit button
-                Button("Classify Image", type="submit", cls="btn btn-primary"),
-                # Keyword arguments: Form attributes
-                hx_post="/predict",
-                hx_target="#results",
-                hx_swap="innerHTML",
-                hx_encoding="multipart/form-data",
-                hx_indicator="#loading-spinner",
-                id="upload-form"
-            ),
-            # --- Loading Indicator ---
-            Div(
-                Span("Loading...", cls="visually-hidden"),
-                id="loading-spinner", cls="htmx-indicator spinner-border mt-3",
-                role="status", style="display: none;"
-            ),
-            # --- Results Area ---
-            Div(
-                id="results", cls="mt-4"
-            ),
-            cls="container mt-4"
-        )
-    )
-
-@rt("/predict", methods=["POST"])
-async def post(request, file: Upload):
-    """Handles image upload, performs prediction, and returns results as an HTML fragment."""
-    if not file or not file.filename:
-        return Div(
-            P("No file uploaded. Please select an image file.", cls="alert alert-warning mt-3"),
-            id="results"
-        )
-
-    allowed_extensions = {'.png', '.jpg', '.jpeg', '.gif', '.bmp', '.webp'}
-    file_ext = Path(file.filename).suffix.lower()
-    if file_ext not in allowed_extensions:
-         return Div(
-             P(f"Invalid file type: '{file_ext}'. Please upload an image ({', '.join(allowed_extensions)}).", cls="alert alert-danger mt-3"),
-             id="results"
-         )
-
-    print(f"Received file: {file.filename}, Content-Type: {file.content_type}, Size: {file.size}")
-
-    try:
-        img_bytes = await file.read()  # Read the file content asynchronously
-        if not img_bytes:
-             return Div(
-                 P("Uploaded file appears to be empty.", cls="alert alert-warning mt-3"),
-                 id="results"
-             )
-    except Exception as e:
-        print(f"Error reading uploaded file: {e}")
-        return Div(
-            P(f"Error reading uploaded file: {e}", cls="alert alert-danger mt-3"),
-            id="results"
-        )
-
-    # --- Perform Prediction ---
-    prediction, confidence = predict_image(img_bytes)
-
-    # Encode image to base64 for preview (only if prediction was successful)
-    img_src = None
-    if "Error" not in str(prediction):
-        try:
-            img_base64 = base64.b64encode(img_bytes).decode('utf-8')
-            content_type = file.content_type if (file.content_type and file.content_type.startswith('image/')) else 'image/jpeg'
-            img_src = f"data:{content_type};base64,{img_base64}"
-        except Exception as e:
-            print(f"Error encoding image to base64: {e}")
-
-    result_cls = "alert alert-danger" if "Error" in str(prediction) else "alert alert-success"
-
-    return Div(
-        # Display image preview if available
-        (Img(src=img_src, alt="Uploaded Image Preview", 
-             style="max-width: 300px; max-height: 300px; margin-top: 15px; margin-bottom: 10px; display: block; border: 1px solid #ddd;")
-         if img_src else P("Preview not available.")),
-        # Display prediction results or error message
-        Div(
-            P(Strong("Prediction: "), f"{prediction}"),
-            (P(Strong("Confidence: "), f"{confidence:.4f}") if "Error" not in str(prediction) else None),
-            cls=f"{result_cls} mt-3",
-            role="alert"
-        ),
-        id="results",
-        hx_swap_oob="true"
-    )
-
-# --- Add CSS/JS Headers ---
-app.sheets.append(
-    Link(href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.2/dist/css/bootstrap.min.css", rel="stylesheet",
-         integrity="sha384-T3c6CoIi6uLrA9TneNEoa7RxnatzjcDSCmG1MXxSR1GAsXEV/Dwwykc2MPK8M2HN", crossorigin="anonymous")
-)
-app.hdrs.append(
-    Script(src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.2/dist/js/bootstrap.bundle.min.js",
-           integrity="sha384-C6RzsynM9kWDrMNeT87bh95OGNyZPhcTNXj1NW7RuBCsyN/o0jlpcV8Qyq46cDfL", crossorigin="anonymous")
+import skimage
+
+
+learn = load_learner('model.pkl')
+#from huggingface_hub import from_pretrained_fastai
+#learn = from_pretrained_fastai("devdatanalytics/commonbean")
+
+labels = learn.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+#title = "Common beans diseases classfier"
+#description = "An app for Common beans diseases Classisfication"
+#article="<p style='text-align: center'>The app identifies and classifies common beans diseases: Anthracnose and Bean rust.</p>"
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(),
+    outputs=gr.Label(num_top_classes=3)
 )
 
-# --- Run the App (for local development) ---
-if __name__ == "__main__":
-    print("Starting Uvicorn server for local development...")
-    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)
+# Enable the queue to handle POST requests
+interface.queue(api_open=True)
+
+# Launch the interface
+interface.launch()