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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+static/image.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,5 +1,5 @@
 ---
-title: fraudio
+title: fraudoo
 emoji: 🐢
 colorFrom: green
 colorTo: purple
@@ -8,18 +8,18 @@ sdk_version: 6.3.0
 app_file: app.py
 pinned: false
 license: apache-2.0
-short_description: fraudio
+short_description: fraudoo
 ---
 
 # 🛡️ Documentary Fraud Management MCP Service
 
-A robust documentary fraud detection service using Google Gemini 3 Flash and Nano Banana Pro, integrated with a Gradio UI and serving as an MCP (Model Context Protocol) server.
+A robust documentary fraud detection service using LLM Services 3 Flash and Nano Banana Pro, integrated with a Gradio UI and serving as an MCP (Model Context Protocol) server.
 
 ## ✨ Features
 
 - **Dual-Model Analysis**: 
-  - **Gemini 3 Flash**: Handles complex reasoning, entity extraction (Names, Dates, IDs), and overall fraud scoring.
-  - **Gemini Nano Banana Pro**: Performs critical visual analysis to detect clones, font mismatches, and retouching.
+  - **LLM Services 3 Flash**: Handles complex reasoning, entity extraction (Names, Dates, IDs), and overall fraud scoring.
+  - **LLM Services Nano Banana Pro**: Performs critical visual analysis to detect clones, font mismatches, and retouching.
 - **Support for Images & PDFs**: Automatically converts PDF pages to images for visual manipulation detection.
 - **Dynamic Context**: Prompts include real-time date/time (currently 2026-01-21) for accurate expiration and inconsistency checks.
 - **Historical Explorer**:
@@ -33,7 +33,7 @@ A robust documentary fraud detection service using Google Gemini 3 Flash and Nan
 
 ### Prerequisites
 - Python 3.10+
-- Google API Key (Gemini)
+- Google API Key (LLM Services)
 
 ### Installation
 1. Clone the repository and navigate to the directory.
@@ -65,7 +65,7 @@ A robust documentary fraud detection service using Google Gemini 3 Flash and Nan
 ## 🛠️ Architecture
 
 - `app.py`: Gradio interface, session management, and persistent file handling.
-- `fraud_analyzer.py`: Core logic for Gemini integration, PDF conversion, and metadata extraction.
+- `fraud_analyzer.py`: Core logic for LLM Services integration, PDF conversion, and metadata extraction.
 - `vector_service.py`: Fingerprinting and history storage using ChromaDB.
 - `uploads/`: Persistent storage for analyzed documents.
 - `chroma_db/`: Vector database storage.

app.py

@@ -12,14 +12,33 @@ import shutil
 API_KEY = os.environ.get("GOOGLE_API_KEY")
 analyzer = FraudAnalyzer(API_KEY) if API_KEY else None
 vector_db = VectorService()
-UPLOAD_DIR = "./uploads"
+UPLOAD_DIR = os.path.abspath("./uploads")
+STATIC_DIR = os.path.abspath("./static")
 os.makedirs(UPLOAD_DIR, exist_ok=True)
+os.makedirs(STATIC_DIR, exist_ok=True)
+
+def load_static_html(filename):
+    """Read a static HTML file and return its content for embedding."""
+    path = os.path.join(STATIC_DIR, filename)
+    if os.path.exists(path):
+        with open(path, "r", encoding="utf-8") as f:
+            content = f.read()
+            # Extract style blocks
+            styles = re.findall(r"(<style>.*?</style>)", content, re.DOTALL | re.IGNORECASE)
+            # Extract body content
+            body_match = re.search(r"<body[^>]*>(.*?)</body>", content, re.DOTALL | re.IGNORECASE)
+            
+            style_str = "\n".join(styles)
+            body_str = body_match.group(1) if body_match else content
+            
+            return f'<div class="static-page-container">{style_str}{body_str}</div>'
+    return f"Error: {filename} not found."
 
 def parse_flash_metrics(analysis_text):
     """Attempt to parse structured fields from Flash's response."""
     metrics = {"label": "Unknown", "amount": "0", "fraud_score": "0"}
     try:
-        # Sometimes Gemini wraps in ```json ... ```
+        # Sometimes LLM Services wraps in ```json ... ```
         clean_text = analysis_text
         json_match = re.search(r"```json\s*(\{.*?\})\s*```", analysis_text, re.DOTALL)
         if json_match:
@@ -44,7 +63,7 @@ def parse_flash_metrics(analysis_text):
 
 def process_document(file_path):
     """
-    Analyzes a document for fraud using Gemini 3 Flash and Nano Banana.
+    Analyzes a document for fraud using LLM Services 3 Flash and Nano Banana.
     Extracts structured data, detects duplicates, and generates a fraud score.
     
     Args:
@@ -65,7 +84,7 @@ def process_document(file_path):
         dup_msg = f"⚠️ DUPLICATE DETECTED: {dup_result['type']}"
 
     result = analyzer.analyze_document(persistent_path)
-    metrics = parse_flash_metrics(result['gemini_analysis'])
+    metrics = parse_flash_metrics(result['llm_analysis'])
     
     doc_id = str(uuid.uuid4())[:8]
     
@@ -73,7 +92,7 @@ def process_document(file_path):
     formatted_score = f"{score_val}/100"
     
     meta = result['metadata']
-    meta['gemini_analysis'] = result['gemini_analysis']
+    meta['llm_analysis'] = result['llm_analysis']
     meta['filename'] = filename
     meta['label'] = metrics['label']
     meta['amount'] = metrics['amount']
@@ -82,7 +101,7 @@ def process_document(file_path):
     
     vector_db.add_document(persistent_path, doc_id, metadata={k: str(v) for k, v in meta.items() if v is not None})
 
-    return f"ID: {doc_id} | {dup_msg}", result['gemini_analysis'], json.dumps(result['metadata'], indent=2), doc_id, persistent_path, get_history_df()
+    return f"ID: {doc_id} | {dup_msg}", result['llm_analysis'], json.dumps(result['metadata'], indent=2), doc_id, persistent_path, get_history_df()
 
 def get_history_df():
     """
@@ -143,13 +162,14 @@ def retrieve_document(doc_id):
         return f"Not found: {doc_id}", None, None, None
     
     meta = doc['metadata']
-    analysis = meta.get('gemini_analysis', "No analysis.")
+    # Fallback for historical 'gemini_analysis' key
+    analysis = meta.get('llm_analysis', meta.get('gemini_analysis', "No analysis."))
     file_path = meta.get('file_path')
     
     if not os.path.exists(file_path):
         return f"Error: File missing at {file_path}", analysis, "{}", None
 
-    display_meta = {k: v for k, v in meta.items() if k not in ['gemini_analysis', 'file_path']}
+    display_meta = {k: v for k, v in meta.items() if k not in ['llm_analysis', 'gemini_analysis', 'file_path']}
     return f"Retrieved: {meta.get('filename')}", analysis, json.dumps(display_meta, indent=2), file_path
 
 css = """
@@ -157,6 +177,9 @@ body { background-color: #f0f2f5; font-family: 'Inter', sans-serif; }
 .container { max-width: 1000px; margin: auto; padding: 20px; }
 .header { text-align: center; margin-bottom: 40px; }
 .result-box { background: white; border-radius: 8px; padding: 20px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); }
+.footer-links { text-align: center; padding: 20px; border-top: 1px solid #e2e8f0; margin-top: 40px; }
+.footer-links a { margin: 0 15px; text-decoration: none; color: #4f46e5; font-weight: 600; }
+.help-card { background: white; padding: 2rem; border-radius: 15px; border-left: 5px solid #4f46e5; margin-bottom: 1rem; }
 """
 
 with gr.Blocks() as demo:
@@ -204,6 +227,24 @@ with gr.Blocks() as demo:
                     detail_analysis = gr.Markdown()
                     detail_meta = gr.Code(language="json")
 
+        with gr.TabItem("Help & Legal", id=3):
+            with gr.Column(elem_classes="container"):
+                gr.Markdown("## 🐢 Fraudoo Support & Legal")
+                
+                with gr.Tabs():
+                    with gr.TabItem("📧 Support"):
+                        gr.HTML(load_static_html("support.html"))
+                    with gr.TabItem("⚖️ Privacy Policy"):
+                        gr.HTML(load_static_html("privacy.html"))
+                    with gr.TabItem("📜 Terms of Service"):
+                        gr.HTML(load_static_html("terms.html"))
+
+    gr.HTML(f"""
+    <div class="footer-links">
+        <span style="color: #64748b; margin-left: 20px;">© 2026 Fraudoo 🐢</span>
+    </div>
+    """)
+
     # Events
     submit_btn.click(
         fn=process_document,
@@ -233,4 +274,9 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     # Ensure UPLOAD_DIR exists and is used
-    demo.launch(mcp_server=True, theme=gr.themes.Soft(), css=css)
+    demo.launch(
+        mcp_server=True, 
+        theme=gr.themes.Soft(), 
+        css=css,
+        allowed_paths=[STATIC_DIR, UPLOAD_DIR]
+    )

fraud_analyzer.py

@@ -1,5 +1,5 @@
 import os
-import google.generativeai as genai
+from google import genai
 from PIL import Image
 import io
 import puremagic
@@ -9,11 +9,11 @@ from datetime import datetime
 
 class FraudAnalyzer:
     def __init__(self, api_key):
-        genai.configure(api_key=api_key)
-        # Gemini 3 Flash for reasoning and extraction
-        self.model_flash = genai.GenerativeModel('gemini-3-flash-preview') 
+        self.client = genai.Client(api_key=api_key)
+        # LLM Services 3 Flash for reasoning and extraction
+        self.model_flash = 'gemini-3-flash-preview' 
         # Nano Banana for specific image-centric analysis
-        self.model_nano = genai.GenerativeModel('nano-banana-pro-preview')
+        self.model_nano = 'nano-banana-pro-preview'
 
     def analyze_document(self, file_path):
         """Perform visual and structural analysis + data extraction."""
@@ -56,7 +56,10 @@ class FraudAnalyzer:
 
         if image_input:
             # Dual analysis for images (or PDF converted to image)
-            visual_resp = self.model_nano.generate_content([visual_prompt, image_input])
+            visual_resp = self.client.models.generate_content(
+                model=self.model_nano,
+                contents=[visual_prompt, image_input]
+            )
             
             # Flash for Extraction & Combined Reasoning
             content_to_flash = [
@@ -68,17 +71,23 @@ class FraudAnalyzer:
                 content_to_flash.append(f"Extracted Text: {text_content}")
             content_to_flash.append(image_input)
             
-            combined_resp = self.model_flash.generate_content(content_to_flash)
+            combined_resp = self.client.models.generate_content(
+                model=self.model_flash,
+                contents=content_to_flash
+            )
             
             return {
-                "gemini_analysis": f"Visual Report (Nano Banana):\n{visual_resp.text}\n\nFinal Reasoning (Flash):\n{combined_resp.text}",
+                "llm_analysis": f"Visual Report (Nano Banana):\n{visual_resp.text}\n\nFinal Reasoning (Flash):\n{combined_resp.text}",
                 "metadata": metadata
             }
         else:
             # Fallback for text-only or unsupported image conversion
-            combined_resp = self.model_flash.generate_content([extraction_prompt, f"Metadata: {metadata}", f"Text: {text_content}"])
+            combined_resp = self.client.models.generate_content(
+                model=self.model_flash,
+                contents=[extraction_prompt, f"Metadata: {metadata}", f"Text: {text_content}"]
+            )
             return {
-                "gemini_analysis": f"Reasoning (Flash):\n{combined_resp.text}",
+                "llm_analysis": f"Reasoning (Flash):\n{combined_resp.text}",
                 "metadata": metadata
             }
 

mcp_server.py

@@ -28,14 +28,14 @@ def validate_document(file_path: str) -> str:
     if dup_result:
         dup_info = f"\n[!] DUPLICATE DETECTED: This document matches a previously seen file ({dup_result['type']} match).\n"
 
-    # 2. Perform Gemini & Metadata analysis
+    # 2. Perform LLM Services & Metadata analysis
     analysis = analyzer.analyze_document(file_path)
     
     # 3. Store in vector DB for future checks
     doc_id = os.path.basename(file_path)
     vector_db.add_document(file_path, doc_id)
 
-    return f"{dup_info}\n--- Analysis ---\n{analysis['gemini_analysis']}\n\n--- Metadata ---\n{analysis['metadata']}"
+    return f"{dup_info}\n--- Analysis ---\n{analysis['llm_analysis']}\n\n--- Metadata ---\n{analysis['metadata']}"
 
 @mcp.tool()
 def list_history() -> str:

requirements.txt

@@ -28,9 +28,9 @@ google-api-core==2.29.0
 google-api-python-client==2.188.0
 google-auth==2.48.0rc0
 google-auth-httplib2==0.3.0
-google-generativeai==0.8.6
+google-genai==1.62.0
 googleapis-common-protos==1.72.0
-gradio==6.3.0
+gradio==6.5.1
 gradio_client==2.0.3
 groovy==0.1.2
 grpcio==1.76.0
@@ -106,6 +106,7 @@ safehttpx==0.1.7
 semantic-version==2.10.0
 shellingham==1.5.4
 six==1.17.0
+sniffio==1.3.1
 sse-starlette==3.2.0
 starlette==0.50.0
 sympy==1.14.0
@@ -124,6 +125,6 @@ uvicorn==0.40.0
 uvloop==0.22.1
 watchfiles==1.1.1
 websocket-client==1.9.0
-websockets==16.0
+websockets==15.0.1
 zipp==3.23.0
 zope.interface==8.2

static/privacy.html

@@ -0,0 +1,89 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Privacy Policy - Fraudoo</title>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&display=swap" rel="stylesheet">
+    <style>
+        :root {
+            --primary: #10b981;
+            --secondary: #3b82f6;
+            --bg: #f8fafc;
+            --text: #1e293b;
+        }
+
+        body {
+            font-family: 'Inter', sans-serif;
+            background: linear-gradient(135deg, #f0fdf4 0%, #e0f2fe 100%);
+            color: var(--text);
+            margin: 0;
+            padding: 2rem;
+            display: flex;
+            justify-content: center;
+        }
+
+        .container {
+            background: rgba(255, 255, 255, 0.9);
+            backdrop-filter: blur(10px);
+            padding: 3rem;
+            border-radius: 20px;
+            box-shadow: 0 10px 25px rgba(0, 0, 0, 0.05);
+            max-width: 800px;
+            width: 100%;
+            line-height: 1.6;
+        }
+
+        h1 {
+            color: var(--primary);
+            text-align: center;
+            margin-bottom: 2rem;
+        }
+
+        h2 {
+            color: var(--secondary);
+            margin-top: 2rem;
+        }
+
+        .back-btn {
+            display: inline-block;
+            margin-top: 2rem;
+            padding: 0.75rem 1.5rem;
+            background: var(--text);
+            color: white;
+            text-decoration: none;
+            border-radius: 10px;
+            font-weight: 600;
+        }
+    </style>
+</head>
+
+<body>
+    <div class="container">
+        <h1>🐢 Privacy Policy</h1>
+        <p>Last Updated: February 11, 2026</p>
+
+        <h2>1. Data Collection</h2>
+        <p>At Fraudoo, we prioritize the privacy of your sensitive documents. When you upload a document for analysis,
+            the file is processed locally and sent to LLM Services APIs for visual and textual analysis.</p>
+
+        <h2>2. Use of Documents</h2>
+        <p>Documents are stored in a local <code>uploads/</code> directory on your host machine to enable historical
+            retrieval. We do not transmit this data to any third party other than the necessary API calls to Google for
+            analysis.</p>
+
+        <h2>3. Data Deletion</h2>
+        <p>Users have the right to delete their analysis history at any time using the dashboard's delete feature, which
+            removes both the database record and the stored file.</p>
+
+        <h2>4. Third-Party Services</h2>
+        <p>The analysis is powered by LLM Services. Please refer to <a href="https://policies.google.com/privacy"
+                target="_blank">Google's Privacy Policy</a> for details on how they handle data processed through their
+            AI models.</p>
+
+        <a href="/" class="back-btn">← Back to App</a>
+    </div>
+</body>
+
+</html>

static/support.html

@@ -0,0 +1,107 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Customer Support - Fraudoo</title>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&display=swap" rel="stylesheet">
+    <style>
+        :root {
+            --primary: #4f46e5;
+            --secondary: #ec4899;
+            --bg: #f8fafc;
+            --text: #1e293b;
+        }
+
+        body {
+            font-family: 'Inter', sans-serif;
+            background: linear-gradient(135deg, #f8fafc 0%, #e2e8f0 100%);
+            color: var(--text);
+            margin: 0;
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            min-height: 100vh;
+        }
+
+        .container {
+            background: rgba(255, 255, 255, 0.8);
+            backdrop-filter: blur(10px);
+            padding: 3rem;
+            border-radius: 20px;
+            box-shadow: 0 10px 25px rgba(0, 0, 0, 0.05);
+            max-width: 600px;
+            width: 90%;
+            border: 1px solid rgba(255, 255, 255, 0.3);
+        }
+
+        h1 {
+            background: linear-gradient(to right, var(--primary), var(--secondary));
+            -webkit-background-clip: text;
+            background-clip: text;
+            -webkit-text-fill-color: transparent;
+            margin-bottom: 2rem;
+            text-align: center;
+        }
+
+        .support-item {
+            margin-bottom: 1.5rem;
+            border-bottom: 1px solid #e2e8f0;
+            padding-bottom: 1rem;
+        }
+
+        .support-item:last-child {
+            border-bottom: none;
+        }
+
+        .support-item strong {
+            display: block;
+            margin-bottom: 0.5rem;
+            color: var(--primary);
+        }
+
+        .back-btn {
+            display: inline-block;
+            margin-top: 2rem;
+            padding: 0.75rem 1.5rem;
+            background: var(--primary);
+            color: white;
+            text-decoration: none;
+            border-radius: 10px;
+            font-weight: 600;
+            transition: transform 0.2s;
+        }
+
+        .back-btn:hover {
+            transform: translateY(-2px);
+        }
+    </style>
+</head>
+
+<body>
+    <div class="container">
+        <h1>🐢 Customer Support</h1>
+        <p>Need help with Fraudoo? Our team is here to ensure your document validation experience is seamless.</p>
+
+        <div class="support-item">
+            <strong>📧 Email Support</strong>
+            <p>Reach out to us at <a href="mailto:support@fraudoo.tech">support@fraudoo.tech</a>. We typically respond
+                within 24 hours.</p>
+        </div>
+
+        <div class="support-item">
+            <strong>📚 Documentation</strong>
+            <p>Check out our README for local setup instructions and API integration tips.</p>
+        </div>
+
+        <div class="support-item">
+            <strong>🕒 Availability</strong>
+            <p>Monday - Friday: 9 AM - 6 PM UTC</p>
+        </div>
+
+        <a href="/" class="back-btn">← Back to App</a>
+    </div>
+</body>
+
+</html>

static/terms.html

@@ -0,0 +1,87 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Terms of Service - Fraudoo</title>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&display=swap" rel="stylesheet">
+    <style>
+        :root {
+            --primary: #8b5cf6;
+            --secondary: #d946ef;
+            --bg: #f8fafc;
+            --text: #1e293b;
+        }
+
+        body {
+            font-family: 'Inter', sans-serif;
+            background: linear-gradient(135deg, #f5f3ff 0%, #fdf2f8 100%);
+            color: var(--text);
+            margin: 0;
+            padding: 2rem;
+            display: flex;
+            justify-content: center;
+        }
+
+        .container {
+            background: rgba(255, 255, 255, 0.9);
+            backdrop-filter: blur(10px);
+            padding: 3rem;
+            border-radius: 20px;
+            box-shadow: 0 10px 25px rgba(0, 0, 0, 0.05);
+            max-width: 800px;
+            width: 100%;
+            line-height: 1.6;
+        }
+
+        h1 {
+            color: var(--primary);
+            text-align: center;
+            margin-bottom: 2rem;
+        }
+
+        h2 {
+            color: var(--secondary);
+            margin-top: 1.5rem;
+        }
+
+        .back-btn {
+            display: inline-block;
+            margin-top: 2rem;
+            padding: 0.75rem 1.5rem;
+            background: var(--primary);
+            color: white;
+            text-decoration: none;
+            border-radius: 10px;
+            font-weight: 600;
+        }
+    </style>
+</head>
+
+<body>
+    <div class="container">
+        <h1>🐢 Terms of Service</h1>
+        <p>By using Fraudoo, you agree to the following terms.</p>
+
+        <h2>1. Acceptable Use</h2>
+        <p>You may use this tool for legitimate document verification purposes. Any attempt to reverse-engineer the
+            fraud detection logic or bypass security measures is prohibited.</p>
+
+        <h2>2. AI-Generated Content</h2>
+        <p>Fraudoo uses LLM Services models. While highly advanced, AI analysis can result in false positives or
+            negatives. Users must perform their own due diligence before making decisions based on the analysis.</p>
+
+        <h2>3. Liability</h2>
+        <p>Fraudoo is provided "as is" without warranty. We are not liable for any financial or legal consequences
+            resulting from the interpretation of document scores or reasoning produced by the service.</p>
+
+        <h2>4. Data Sovereignty</h2>
+        <p>You are responsible for ensuring you have the legal right to process the documents you upload to this
+            service.</p>
+
+        <a href="/" class="back-btn">← Back to App</a>
+    </div>
+</body>
+
+</html>

vector_service.py

@@ -6,7 +6,7 @@ import hashlib
 class VectorService:
     def __init__(self, db_path="./chroma_db"):
         self.client = chromadb.PersistentClient(path=db_path)
-        # Use a simple default embedding function or Gemini if needed
+        # Use a simple default embedding function or LLM Services if needed
         self.ef = embedding_functions.DefaultEmbeddingFunction()
         self.collection = self.client.get_or_create_collection(
             name="document_fingerprints",