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Dockerfile

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+FROM python:3.8-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    libglib2.0-0 \
+    libsm6 \
+    libxrender-dev \
+    libxext6 \
+    ffmpeg
+
+# Install Python dependencies
+COPY requirements.txt requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application files
+COPY . .
+
+# Expose port
+EXPOSE 7860
+
+CMD ["python", "app.py"]

app.py

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+import base64
+from fastapi import FastAPI, File, UploadFile
+from pydantic import BaseModel
+import numpy as np
+from PIL import Image
+import gradio as gr
+import tensorflow as tf
+
+# Load the trained model
+modelCNN = tf.keras.models.load_model('modelCNN.h5')
+
+def predict(sketch):
+    try:
+        if 'layers' in sketch:
+            # Access the sketch image from the 'layers' key
+            sketch_image = np.array(sketch['layers'][0]) * 255
+            sketch_image = Image.fromarray(sketch_image.astype('uint8')).convert('L')
+
+            # Resize the sketch to match MNIST image size
+            sketch_image = sketch_image.resize((28, 28))
+
+            # Convert the image to a numpy array
+            img_array = np.array(sketch_image).reshape(1, 28, 28, 1) / 255.0
+
+            # Make prediction
+            prediction = modelCNN.predict(img_array)[0]
+            predicted_digit = np.argmax(prediction)
+            return str(predicted_digit)
+        else:
+            # Handle the case where "layers" key is missing
+            return "No sketch data found"
+    except Exception as e:
+        # Print the exception for debugging
+        print("Error:", e)
+        # Return an error message
+        return "An error occurred"
+
+# Launch Gradio interface with FastAPI integration
+gr.Interface(fn=predict, inputs="sketchpad", outputs="label").launch()

modelCNN.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:04f7344c42077a0d81fa704453010a1aad22ff8718234f46ca3bdca86f01e646
+size 7355048

requirements.txt

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+gradio
+scikit-learn
+torchvision
+seaborn