Update app.py

app.py

@@ -1,11 +1,92 @@
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
-import gradio as grad
-import ast
-mdl_name = "deepset/roberta-base-squad2"
-my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)
-def answer_question(question,context):
-    text= "{"+"'question': '"+question+"','context': '"+context+"'}"
-    di=ast.literal_eval(text)
-    response = my_pipeline(di)
-    return response
-grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()
+import gradio as gr
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+from PIL import Image
+
+# --- Configuration ---
+MODEL_PATH = "cats-vs-dogs-finetuned.keras"
+IMAGE_SIZE = (150, 150) # Adjust this to match the input size your model expects!
+CLASS_LABELS = ['Cat', 'Dog']
+
+# --- Load the Model ---
+# We load the Keras model. Hugging Face Spaces will automatically find this file
+# if you upload it to your repository.
+try:
+    model = keras.models.load_model(MODEL_PATH)
+    print(f"Model loaded successfully from {MODEL_PATH}")
+except Exception as e:
+    # If the model fails to load (e.g., during initial setup before it's uploaded),
+    # we use a placeholder function. This helps the app start.
+    print(f"Error loading model: {e}. Using a placeholder function.")
+    model = None
+
+# --- Prediction Function ---
+def predict_image(input_img_pil):
+    """
+    Predicts the class (Cat or Dog) given a PIL Image object.
+    
+    Args:
+        input_img_pil: A PIL Image object received from Gradio's Image input.
+        
+    Returns:
+        A dictionary of class labels and their probabilities (for Gradio's Label output).
+    """
+    if model is None:
+        # Placeholder behavior if model loading failed
+        return {"Error": 1.0}
+
+    # 1. Preprocessing: Resize and convert to NumPy array
+    img_resized = input_img_pil.resize(IMAGE_SIZE)
+    img_array = keras.preprocessing.image.img_to_array(img_resized)
+    
+    # 2. Rescaling and Batch dimension:
+    # Keras models usually expect input shapes like (Batch_Size, Height, Width, Channels)
+    # and often expect pixel values to be normalized (e.g., 0-1 range).
+    # Please adjust the normalization based on how your model was trained!
+    img_array = img_array / 255.0  # Common normalization step
+    img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
+
+    # 3. Prediction
+    predictions = model.predict(img_array)[0] # Get the single prediction result
+
+    # 4. Format the output for Gradio's Label component
+    # The output is expected to be a dictionary: {'label': probability, ...}
+    
+    # Assuming predictions is a 2-element array: [prob_cat, prob_dog]
+    output_dict = {
+        CLASS_LABELS[0]: float(predictions[0]),
+        CLASS_LABELS[1]: float(predictions[1])
+    }
+    
+    return output_dict
+
+
+# --- Gradio Interface Setup ---
+
+# Define the input component (Image) and output component (Label)
+image_input = gr.Image(type="pil", label="Upload a Cat or Dog Image")
+label_output = gr.Label(num_top_classes=2, label="Prediction")
+
+# Example images for users to try (place these in your Space if you use them)
+examples = [
+    # To use these, you would need to upload files named 'example_cat.jpg' and 'example_dog.jpg'
+    # 'example_cat.jpg', 
+    # 'example_dog.jpg'
+]
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=predict_image,
+    inputs=image_input,
+    outputs=label_output,
+    title="Keras Cat vs Dog Classifier",
+    description="Upload an image of a cat or dog to see the model's prediction. The model is loaded from cat-vs-dog.keras.",
+    theme=gr.themes.Soft(),
+    # Optional: Add examples if you upload them
+    # examples=examples
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()