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app.py

@@ -3,7 +3,6 @@ import tensorflow as tf
 from huggingface_hub import hf_hub_download
 from tensorflow.keras.preprocessing import image
 import numpy as np
-import matplotlib.pyplot as plt
 
 # Step 1: Download the model from the Hugging Face Hub
 model_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/my_tensorflow_model", filename="my_model.h5")
@@ -39,7 +38,7 @@ def predict_image(img):
     # Determine label
     result_label = "Real" if real_confidence > fake_confidence else "Fake"
     
-    # Return results as text and an explanation
+    # Return results
     result_text = f"The model predicts this image is '{result_label}' with {max(real_confidence, fake_confidence):.2f}% confidence."
     explanation = f"Real Confidence: {real_confidence:.2f}% | Fake Confidence: {fake_confidence:.2f}%"
     
@@ -48,10 +47,10 @@ def predict_image(img):
 # Step 5: Define the Gradio interface
 interface = gr.Interface(
     fn=predict_image,
-    inputs=gr.inputs.Image(type="pil", label="Upload an Image"),
+    inputs=gr.Image(type="pil", label="Upload an Image"),
     outputs=[
-        gr.outputs.Textbox(label="Prediction Result"),
-        gr.outputs.Textbox(label="Confidence Scores")
+        gr.Textbox(label="Prediction Result"),
+        gr.Textbox(label="Confidence Scores")
     ],
     title="Deepfake Image Detector",
     description="Upload an image, and the model will classify whether it is a 'real' or 'fake' image using deep learning."