Update app.py

app.py

@@ -1,146 +1,57 @@
-# # import gradio as gr
-
-# # gr.load("models/vm24bho/net_dfm_myimg").launch()
-
-
-# import gradio as gr
-
-# # Load your model (this is a placeholder; replace with your actual model loading code)
-# demo = gr.load("models/vm24bho/net_dfm_myimg")
-
-# # Customize the interface
-# css = """
-#     body { 
-#         background-color: #f5f5f5;
-#         font-family: 'Arial', sans-serif;
-#     }
-#     .container { 
-#         max-width: 800px; 
-#         margin: 0 auto; 
-#         padding: 20px; 
-#         background-color: #ffffff; 
-#         border-radius: 10px; 
-#         box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); 
-#     }
-#     .header { 
-#         text-align: center; 
-#         padding-bottom: 20px; 
-#     }
-#     h1 { 
-#         color: #333333; 
-#     }
-#     p { 
-#         color: #666666; 
-#     }
-#     .gradio-container { 
-#         display: flex; 
-#         flex-direction: column; 
-#         gap: 20px; 
-#     }
-#     button { 
-#         background-color: #007bff; 
-#         color: white; 
-#         border: none; 
-#         padding: 10px 20px; 
-#         border-radius: 5px; 
-#         cursor: pointer; 
-#         font-size: 16px; 
-#     }
-#     button:hover { 
-#         background-color: #0056b3; 
-#     }
-# """
-
-# demo.launch(
-#     title="Deepfake Detection",
-#     description="A interface for interacting with the VM24BHO model.",
-#     article="""
-#         <div class="container">
-#             <div class="header">
-#                 <h1>Welcome to the VM24BHO Model Interface</h1>
-#                 <p>Use this interface to interact with the deep learning model.</p>
-#             </div>
-#             <div class="gradio-container">
-#                 <!-- Gradio interface elements will be inserted here -->
-#             </div>
-#         </div>
-#     """,
-#     css=css
-# )
-
-
-
-
-
-
-import gradio as gr
-
-# Load your model (assuming the path is correct)
-demo = gr.load("models/vm24bho/net_dfm_myimg")
-
-# Custom CSS styling
-css = """
-    body { 
-        background-color: #f5f5f5;
-        font-family: 'Arial', sans-serif;
-    }
-    .container { 
-        max-width: 800px; 
-        margin: 0 auto; 
-        padding: 20px; 
-        background-color: #ffffff; 
-        border-radius: 10px; 
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); 
-    }
-    .header { 
-        text-align: center; 
-        padding-bottom: 20px; 
-    }
-    h1 { 
-        color: #333333; 
-    }
-    p { 
-        color: #666666; 
-    }
-    .gradio-container { 
-        display: flex; 
-        flex-direction: column; 
-        gap: 20px; 
-    }
-    button { 
-        background-color: #007bff; 
-        color: white; 
-        border: none; 
-        padding: 10px 20px; 
-        border-radius: 5px; 
-        cursor: pointer; 
-        font-size: 16px; 
-    }
-    button:hover { 
-        background-color: #0056b3; 
-    }
-"""
-
-# Creating the Gradio interface
-with gr.Blocks(css=css) as demo:
-    gr.Markdown(
-        """
-        <div class="container">
-            <div class="header">
-                <h1>Welcome to the Deepfake detection Model Interface</h1>
-                <p>Use this interface to interact with the deep learning model.</p>
-            </div>
-            <div class="gradio-container">
-                <!-- Gradio interface elements will be inserted here -->
-            </div>
-        </div>
-        """
-    )
-
-    # Assuming your model has a specific input/output interface
-    # Adjust this part according to your actual model's input/output
-    gr.Interface.load("models/vm24bho/net_dfm_myimg")
-
-# Launch the Gradio interface
-demo.launch()
-
+import streamlit as st
+from PIL import Image
+from transformers import pipeline
+
+# Set the title and sidebar
+st.set_page_config(page_title="Deepfake vs Real Image Detection", page_icon="🤖")
+
+st.title("Deepfake vs Real Image Detection")
+st.sidebar.title("Options")
+
+# Description
+st.markdown(
+    """
+    Welcome to the Deepfake vs Real Image Detection app! 
+    Upload an image and let our model determine if it is real or a deepfake.
+    """
+)
+
+# Load the pipeline
+model_name = "vm24bho/net_dfm_myimg"
+pipe = pipeline('image-classification', model=model_name)
+
+# Upload image
+st.sidebar.subheader("Upload Image")
+uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+# Add a sample image option
+if st.sidebar.button("Use Sample Image"):
+    sample_url = "https://drive.google.com/file/d/1O1mGXitkGcnu-kyISpnRBvhju3swRDP9/view?usp=drive_link"  # Replace with a valid sample image URL
+    image = Image.open(requests.get(sample_url, stream=True).raw)
+else:
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+
+# Display the uploaded image
+if uploaded_file or st.sidebar.button("Use Sample Image"):
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    # Apply the model
+    result = pipe(image)
+
+    # Display the result
+    st.subheader("Classification Result")
+    for res in result:
+        st.write(f"**{res['label']}**: {res['score']*100:.2f}%")
+else:
+    st.sidebar.info("Upload an image to get started.")
+
+# Footer
+st.sidebar.markdown(
+    """
+    ---
+    Created with ❤️ by VINAYAK MODI(https://github.com/vinayakmodi)
+    """
+)