Update app.py

app.py

@@ -1,6 +1,7 @@
 import os
 import gradio as gr
 import google.generativeai as genai
+from PIL import Image
 
 # Configure the Gemini API with environment variable
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
@@ -12,107 +13,63 @@ genai.configure(api_key=GOOGLE_API_KEY)
 # Use Gemini 1.5 Flash model
 model = genai.GenerativeModel('gemini-1.5-flash-latest')
 
-def predict_banknote_authenticity(security_thread, watermark, microtext, ink_color_shift, paper_texture, printing_quality, holographic_features, serial_number, uv_features, edge_smoothness):
-    # Validate inputs
-    inputs = [security_thread, watermark, microtext, ink_color_shift, paper_texture, printing_quality, holographic_features, serial_number, uv_features, edge_smoothness]
-    if not all(inputs):
-        return "Error: Please provide answers to all questions."
+def authenticate_banknote(image):
+    if image is None:
+        return "Error: Please upload or capture a banknote image."
     
-    # Format user responses
-    user_data = f"""
-    Security Thread: {security_thread}
-    Watermark: {watermark}
-    Microtext: {microtext}
-    Ink Color Shift: {ink_color_shift}
-    Paper Texture: {paper_texture}
-    Printing Quality: {printing_quality}
-    Holographic Features: {holographic_features}
-    Serial Number Consistency: {serial_number}
-    UV Features: {uv_features}
-    Edge Smoothness: {edge_smoothness}
-    """
-
-    prompt = f"""
-    You are an assistant predicting whether a banknote is authentic or counterfeit based on user responses to 10 questions about its physical and visual characteristics. 
-    Classify the banknote as "Authentic" or "Fake" and provide a brief reason for your classification.
+    # Resize image for consistency (optional, Gemini handles various sizes)
+    image = image.resize((224, 224))
+    
+    prompt = """
+    You are an image analysis assistant tasked with determining whether a banknote image is authentic or counterfeit and identifying its denomination (worth). 
+    Analyze the image for security features (e.g., security thread, watermark, microtext, color-shifting ink, holograms, printing quality) to determine authenticity. 
+    Identify the denomination based on visual elements like currency symbols, colors, numbers, or portraits.
     Return the response in this format:
     
     Prediction: [Authentic / Fake]
-    Reason: [Brief explanation]
+    Reason for Authenticity: [Brief explanation]
+    Denomination: [Value, e.g., $10, €20]
+    Reason for Denomination: [Brief explanation]
 
     Examples:
-    User Data:
-    Security Thread: Visible and shifts color
-    Watermark: Clear and visible when held to light
-    Microtext: Legible under magnification
-    Ink Color Shift: Changes color when tilted
-    Paper Texture: Crisp and unique
-    Printing Quality: Sharp and clear
-    Holographic Features: Present and changes with angle
-    Serial Number Consistency: Uniform and aligned
-    UV Features: Glows correctly under UV light
-    Edge Smoothness: Smooth and precise
+    Image Description: A banknote with a visible security thread that shifts color, a clear watermark visible when held to light, legible microtext under magnification, color-shifting ink, crisp paper texture, sharp printing, and a holographic feature that changes with angle. The note has a green color scheme and a portrait of a historical figure with "$100" printed.
     Prediction: Authentic
-    Reason: The presence of a color-shifting security thread, clear watermark, legible microtext, color-changing ink, crisp paper texture, sharp printing, holographic features, consistent serial numbers, correct UV glow, and smooth edges are strong indicators of an authentic banknote.
+    Reason for Authenticity: The presence of a color-shifting security thread, clear watermark, legible microtext, color-shifting ink, crisp paper, sharp printing, and holographic features are strong indicators of an authentic banknote.
+    Denomination: $100
+    Reason for Denomination: The green color scheme, portrait, and "$100" text are consistent with a U.S. $100 note.
 
-    User Data:
-    Security Thread: Not visible
-    Watermark: Absent or blurry
-    Microtext: Illegible or absent
-    Ink Color Shift: No color change
-    Paper Texture: Rough or ordinary
-    Printing Quality: Blurry or uneven
-    Holographic Features: Absent
-    Serial Number Consistency: Misaligned or inconsistent
-    UV Features: No glow or incorrect glow
-    Edge Smoothness: Rough or irregular
+    Image Description: A banknote with no visible security thread, a blurry watermark, illegible microtext, no color-shifting ink, rough paper texture, and blurry printing. The note has a blue color scheme and "€50" printed.
     Prediction: Fake
-    Reason: The absence of a security thread, watermark, microtext, color-shifting ink, and holographic features, along with rough paper, blurry printing, inconsistent serial numbers, incorrect UV response, and irregular edges, strongly suggest a counterfeit banknote.
+    Reason for Authenticity: The absence of a security thread, blurry watermark, illegible microtext, lack of color-shifting ink, rough paper, and blurry printing suggest a counterfeit banknote.
+    Denomination: €50
+    Reason for Denomination: The blue color scheme and "€50" text indicate a Euro €50 note, despite its counterfeit nature.
 
-    User Data:
-    Security Thread: Visible but no color shift
-    Watermark: Faintly visible
-    Microtext: Partially legible
-    Ink Color Shift: No color change
-    Paper Texture: Crisp and unique
-    Printing Quality: Sharp and clear
-    Holographic Features: Absent
-    Serial Number Consistency: Uniform and aligned
-    UV Features: Glows correctly under UV light
-    Edge Smoothness: Smooth and precise
-    Prediction: Fake
-    Reason: While some features like paper texture, printing quality, serial number consistency, UV glow, and edge smoothness are present, the lack of a color-shifting security thread, faint watermark, partially legible microtext, absent holographic features, and no color-shifting ink suggest a counterfeit banknote.
+    Image Description: A banknote with a security thread, clear watermark, legible microtext, and color-shifting ink. The note has a purple color scheme and "£20" printed.
+    Prediction: Authentic
+    Reason for Authenticity: The presence of a security thread, clear watermark, legible microtext, and color-shifting ink are consistent with an authentic banknote.
+    Denomination: £20
+    Reason for Denomination: The purple color scheme and "£20" text are consistent with a British £20 note.
 
-    User Data:
-    {user_data}
+    Analyze the provided image.
     Prediction:
-    Reason:
+    Reason for Authenticity:
+    Denomination:
+    Reason for Denomination:
     """
 
     try:
-        response = model.generate_content(prompt)
+        response = model.generate_content([prompt, image])
         return response.text.strip()
     except Exception as e:
         return f"Error: {str(e)}\nTip: Ensure your API key is valid at https://aistudio.google.com/"
 
 # Define Gradio interface
 iface = gr.Interface(
-    fn=predict_banknote_authenticity,
-    inputs=[
-        gr.Dropdown(choices=["Visible and shifts color", "Visible but no color shift", "Not visible"], label="1. Is the security thread visible and does it shift color when tilted?"),
-        gr.Dropdown(choices=["Clear and visible when held to light", "Faintly visible", "Absent or blurry"], label="2. Is there a clear watermark visible when held to light?"),
-        gr.Dropdown(choices=["Legible under magnification", "Partially legible", "Illegible or absent"], label="3. Is microtext legible under magnification?"),
-        gr.Dropdown(choices=["Changes color when tilted", "No color change"], label="4. Does the ink in specific areas (e.g., denomination) change color when tilted?"),
-        gr.Dropdown(choices=["Crisp and unique", "Rough or ordinary"], label="5. Does the paper have a crisp, unique texture?"),
-        gr.Dropdown(choices=["Sharp and clear", "Blurry or uneven"], label="6. Is the printing quality sharp and clear?"),
-        gr.Dropdown(choices=["Present and changes with angle", "Absent"], label="7. Are holographic features present and do they change with angle?"),
-        gr.Dropdown(choices=["Uniform and aligned", "Misaligned or inconsistent"], label="8. Are the serial numbers uniform and aligned?"),
-        gr.Dropdown(choices=["Glows correctly under UV light", "No glow or incorrect glow"], label="9. Do specific elements glow correctly under UV light?"),
-        gr.Dropdown(choices=["Smooth and precise", "Rough or irregular"], label="10. Are the edges of the note smooth and precise?")
-    ],
-    outputs=gr.Textbox(label="Banknote Authentication Prediction"),
-    title="Banknote Authenticity Predictor",
-    description="Answer 10 questions about a banknote's characteristics to predict whether it is authentic or counterfeit using the Gemini API. Set your GOOGLE_API_KEY in the Hugging Face Space settings. Note: This is for demonstration purposes only and not for real-world banking use."
+    fn=authenticate_banknote,
+    inputs=gr.Image(type="pil", label="Upload or capture a banknote image", sources=["upload", "webcam"]),
+    outputs=gr.Textbox(label="Banknote Authentication and Denomination Result"),
+    title="Banknote Authenticity and Denomination Predictor",
+    description="Upload or capture a banknote image to determine if it is authentic or counterfeit and identify its denomination using the Gemini API. Set your GOOGLE_API_KEY in the Hugging Face Space settings. Note: This is for demonstration purposes only and not for real-world banking use."
 )
 
 # Launch the interface