Resolve merge conflicts

.gitattributes

@@ -0,0 +1,2 @@
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,18 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install minimal system dependencies
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application files
+COPY . .
+
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

Google_Search_tool.py

@@ -0,0 +1,41 @@
+import os
+import logging
+from googleapiclient.discovery import build
+from dotenv import load_dotenv
+
+load_dotenv()
+
+class GoogleSearch:
+    """Performs Google Custom Search API queries."""
+    def __init__(self):
+        self.api_key = os.getenv("GOOGLE_API_KEY")
+        self.cse_id = os.getenv("GOOGLE_CSE_ID")
+        self.service = None
+        if self.api_key and self.cse_id:
+            try:
+                self.service = build("customsearch", "v1", developerKey=self.api_key)
+                logging.info("✅ Google Custom Search initialized.")
+            except Exception as e:
+                logging.error(f"❌ CSE init failed: {e}")
+        else:
+            logging.warning("⚠️ GOOGLE_API_KEY or GOOGLE_CSE_ID not set; search disabled.")
+
+    def search(self, queries: list, num_results: int = 1) -> list:
+        if not self.service:
+            return []
+        out = []
+        for q in queries:
+            try:
+                resp = self.service.cse().list(q=q, cx=self.cse_id, num=num_results).execute()
+                items = resp.get("items", [])
+                formatted = [
+                    {"title": it.get("title"), "link": it.get("link"), "snippet": it.get("snippet")}
+                    for it in items
+                ]
+                out.append({"query": q, "results": formatted})
+            except Exception as e:
+                logging.error(f"❌ Search error for '{q}': {e}")
+                out.append({"query": q, "results": []})
+        return out
+
+google_search = GoogleSearch()

README.md

@@ -1,59 +1,14 @@
 ---
-title: HexAI Demo
-emoji: 🔥
-colorFrom: pink
-colorTo: gray
-sdk: static
-pinned: false
-app_build_command: npm run build
-app_file: dist/index.html
-short_description: Demo with limited version
+title: RxGuard Prescription Validator
+emoji: ⚕️
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.36.0
+app_file: app.py
+hf_oauth: true
+hardware:
+  accelerator: T4
+  cpu: 2
+  memory: 16
 ---
-
-# Svelte + TS + Vite
-
-This template should help get you started developing with Svelte and TypeScript in Vite.
-
-## Recommended IDE Setup
-
-[VS Code](https://code.visualstudio.com/) + [Svelte](https://marketplace.visualstudio.com/items?itemName=svelte.svelte-vscode).
-
-## Need an official Svelte framework?
-
-Check out [SvelteKit](https://github.com/sveltejs/kit#readme), which is also powered by Vite. Deploy anywhere with its serverless-first approach and adapt to various platforms, with out of the box support for TypeScript, SCSS, and Less, and easily-added support for mdsvex, GraphQL, PostCSS, Tailwind CSS, and more.
-
-## Technical considerations
-
-**Why use this over SvelteKit?**
-
-- It brings its own routing solution which might not be preferable for some users.
-- It is first and foremost a framework that just happens to use Vite under the hood, not a Vite app.
-
-This template contains as little as possible to get started with Vite + TypeScript + Svelte, while taking into account the developer experience with regards to HMR and intellisense. It demonstrates capabilities on par with the other `create-vite` templates and is a good starting point for beginners dipping their toes into a Vite + Svelte project.
-
-Should you later need the extended capabilities and extensibility provided by SvelteKit, the template has been structured similarly to SvelteKit so that it is easy to migrate.
-
-**Why `global.d.ts` instead of `compilerOptions.types` inside `jsconfig.json` or `tsconfig.json`?**
-
-Setting `compilerOptions.types` shuts out all other types not explicitly listed in the configuration. Using triple-slash references keeps the default TypeScript setting of accepting type information from the entire workspace, while also adding `svelte` and `vite/client` type information.
-
-**Why include `.vscode/extensions.json`?**
-
-Other templates indirectly recommend extensions via the README, but this file allows VS Code to prompt the user to install the recommended extension upon opening the project.
-
-**Why enable `allowJs` in the TS template?**
-
-While `allowJs: false` would indeed prevent the use of `.js` files in the project, it does not prevent the use of JavaScript syntax in `.svelte` files. In addition, it would force `checkJs: false`, bringing the worst of both worlds: not being able to guarantee the entire codebase is TypeScript, and also having worse typechecking for the existing JavaScript. In addition, there are valid use cases in which a mixed codebase may be relevant.
-
-**Why is HMR not preserving my local component state?**
-
-HMR state preservation comes with a number of gotchas! It has been disabled by default in both `svelte-hmr` and `@sveltejs/vite-plugin-svelte` due to its often surprising behavior. You can read the details [here](https://github.com/rixo/svelte-hmr#svelte-hmr).
-
-If you have state that's important to retain within a component, consider creating an external store which would not be replaced by HMR.
-
-```ts
-// store.ts
-// An extremely simple external store
-import { writable } from "svelte/store";
-export default writable(0);
-```

app.py

@@ -0,0 +1,137 @@
+import os
+import streamlit as st
+from PIL import Image
+from config import (
+    STATIC_DIR,
+    UPLOADS_DIR,
+    HF_TOKEN,
+    GOOGLE_API_KEY,
+    GOOGLE_CSE_ID,
+    GEMINI_API_KEY,
+    DEVICE
+)
+
+# ─── App Configuration ────────────────────────────────────────────────────
+st.set_page_config(
+    page_title="RxGuard Prescription Validator",
+    page_icon="⚕️",
+    layout="wide",
+    menu_items={
+        'Get Help': 'https://github.com/your-repo',
+        'About': "RxGuard v1.0 - Advanced Prescription Validation"
+    }
+)
+
+# ─── Session State ────────────────────────────────────────────────────────
+if "analysis_result" not in st.session_state:
+    st.session_state.analysis_result = None
+if "uploaded_filename" not in st.session_state:
+    st.session_state.uploaded_filename = None
+
+# ─── UI Components ────────────────────────────────────────────────────────
+def show_service_status():
+    """Displays service connectivity status."""
+    cols = st.columns(4)
+    with cols[0]:
+        st.metric("HuggingFace", "✅" if HF_TOKEN else "❌")
+    with cols[1]:
+        st.metric("Google API", "✅" if GOOGLE_API_KEY else "❌")
+    with cols[2]:
+        st.metric("Gemini", "✅" if GEMINI_API_KEY else "❌")
+    with cols[3]:
+        st.metric("Device", DEVICE.upper())
+
+def display_patient_info(info: dict):
+    """Displays patient information in a formatted card."""
+    with st.container(border=True):
+        st.subheader("👤 Patient Details")
+        cols = st.columns(2)
+        with cols[0]:
+            st.markdown(f"**Name:** {info.get('Name', 'Not detected')}")
+            st.markdown(f"**Age:** {info.get('Age', 'N/A')}")
+        with cols[1]:
+            st.markdown(f"**Date:** {info.get('Date', 'N/A')}")
+            st.markdown(f"**Physician:** {info.get('PhysicianName', 'N/A')}")
+
+def display_medications(medications: list):
+    """Displays medication information with verification."""
+    st.subheader("💊 Medications")
+    if not medications:
+        st.warning("No medications detected in prescription")
+        return
+        
+    for med in medications:
+        with st.expander(f"{med.get('drug_raw', 'Unknown Medication')}"):
+            cols = st.columns([1, 2])
+            with cols[0]:
+                st.markdown(f"""
+                **Dosage:** `{med.get('dosage', 'N/A')}`  
+                **Frequency:** `{med.get('frequency', 'N/A')}`
+                """)
+            
+            with cols[1]:
+                if verification := med.get("verification"):
+                    if dosage := verification.get("standard_dosage"):
+                        st.success(f"**Standard Dosage:** {dosage}")
+                    if side_effects := verification.get("side_effects"):
+                        st.warning(f"**Side Effects:** {side_effects}")
+                    if interactions := verification.get("interactions"):
+                        st.error(f"**Interactions:** {interactions}")
+
+# ─── Main Application ─────────────────────────────────────────────────────
+def main():
+    st.title("⚕️ RxGuard Prescription Validator")
+    st.caption("AI-powered prescription verification system")
+    
+    show_service_status()
+    
+    # Only enable upload if required services are available
+    if all([HF_TOKEN, GOOGLE_API_KEY, GEMINI_API_KEY]):
+        uploaded_file = st.file_uploader(
+            "Upload prescription image (PNG/JPG/JPEG):",
+            type=["png", "jpg", "jpeg"],
+            help="Clear image of the prescription"
+        )
+        
+        if uploaded_file and uploaded_file.name != st.session_state.uploaded_filename:
+            with st.status("Analyzing prescription...", expanded=True) as status:
+                try:
+                    # Store the uploaded file
+                    st.session_state.uploaded_filename = uploaded_file.name
+                    file_path = os.path.join(UPLOADS_DIR, uploaded_file.name)
+                    
+                    with open(file_path, "wb") as f:
+                        f.write(uploaded_file.getvalue())
+                    
+                    # Import processing function only when needed
+                    from validate_prescription import extract_prescription_info
+                    st.session_state.analysis_result = extract_prescription_info(file_path)
+                    
+                    status.update(label="Analysis complete!", state="complete", expanded=False)
+                except Exception as e:
+                    st.error(f"Processing failed: {str(e)}")
+                    st.session_state.analysis_result = {"error": str(e)}
+                    status.update(label="Analysis failed", state="error")
+    
+    # Display results if available
+    if st.session_state.analysis_result:
+        result = st.session_state.analysis_result
+        
+        if result.get("error"):
+            st.error(f"❌ Error: {result['error']}")
+        else:
+            tab1, tab2 = st.tabs(["Patient Information", "Medication Details"])
+            
+            with tab1:
+                if uploaded_file:
+                    st.image(uploaded_file, use_column_width=True)
+                display_patient_info(result["info"])
+            
+            with tab2:
+                display_medications(result["info"].get("Medications", []))
+                
+                if st.toggle("Show technical details"):
+                    st.json(result.get("debug_info", {}))
+
+if __name__ == "__main__":
+    main()

apt-packages.txt

@@ -0,0 +1,13 @@
+libgl1
+libgl1-mesa-glx
+libglib2.0-0
+tesseract-ocr
+tesseract-ocr-hin
+git
+git-lfs
+curl
+libssl-dev
+libffi-dev
+python3-dev
+build-essential
+libsqlite3-dev

config.py

@@ -0,0 +1,37 @@
+import os
+import torch
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# ─── Directory Configuration ────────────────────────────────────────────────
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+STATIC_DIR = os.path.join(BASE_DIR, 'static')
+os.makedirs(STATIC_DIR, exist_ok=True)
+
+# ─── API Secrets ────────────────────────────────────────────────────────────
+HF_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN")  # For Hugging Face models
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")  # For Gemini and Custom Search
+GOOGLE_CSE_ID = os.getenv("GOOGLE_CSE_ID")  # For medication verification
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")  # Alternative Gemini auth
+HALODOC_API_KEY = os.getenv("HALODOC_API_KEY")  # Future integration
+
+# ─── Model Configuration ────────────────────────────────────────────────────
+HF_MODELS = {
+    "donut": "naver-clova-ix/donut-base-finetuned-cord-v2",
+    "phi3": "microsoft/phi-3-mini-4k-instruct",
+}
+GEMINI_MODEL_NAME = "gemini-1.5-flash"  # Balanced for speed and accuracy
+
+# ─── Processing Parameters ─────────────────────────────────────────────────
+LEV_THRESH = 0.75  # Levenshtein similarity threshold
+SIG_THRESH = 0.65  # Signature verification threshold
+
+# ─── File Paths ───────────────────────────────────────────────────────────
+DB_PATH = os.path.join(STATIC_DIR, "rxguard.db")
+UPLOADS_DIR = os.path.join(STATIC_DIR, "uploads")
+os.makedirs(UPLOADS_DIR, exist_ok=True)
+
+# ─── Hardware Configuration ────────────────────────────────────────────────
+DEVICE = "cpu"  # Force CPU for Hugging Face Spaces compatibility
+USE_GPU = False

data/interactions.csv

@@ -0,0 +1,3 @@
+cui1,cui2,severity,advice
+1191,6801,moderate,"Monitor blood glucose closely; aspirin may enhance the hypoglycemic effect of metformin."
+4241,1113,moderate,"Ascorbic acid can increase the absorption of iron. While often beneficial, monitor for iron overload in susceptible patients."

data/rxnorm_names.csv

@@ -0,0 +1,13 @@
+name,cui
+Aspirin,1191
+Acetaminophen,161
+Amoxicillin,723
+Metformin,6801
+Lisinopril,29046
+Ferrous Sulfate,4241
+Ascorbic Acid,1113
+Calcium Carbonate / Vitamin D3,12345
+Clonazepam,2623
+Meganeuron,54321
+Creatine,9876
+Salbutamol,9648

drug_interaction_detection/interaction_checker.py

@@ -0,0 +1,34 @@
+# drug_interaction_detection/interaction_checker.py
+import sqlite3
+from config import DB_PATH
+
+class InteractionChecker:
+    def __init__(self, db_path: str = DB_PATH):
+        self.conn = sqlite3.connect(db_path)
+        self.conn.row_factory = sqlite3.Row
+
+    def find(self, cuis: list[str]) -> list[dict]:
+        if len(cuis) < 2: return []
+        
+        # Create a list of all unique pairs
+        pairs = []
+        for i in range(len(cuis)):
+            for j in range(i + 1, len(cuis)):
+                # Ensure consistent order for querying (e.g., smaller CUI first)
+                pair = tuple(sorted((cuis[i], cuis[j])))
+                pairs.append(pair)
+        
+        if not pairs: return []
+
+        # Query for all pairs at once
+        placeholders = ", ".join(["(?, ?)"] * len(pairs))
+        flat_params = [item for pair in pairs for item in pair]
+        
+        query = f"""
+            SELECT cui1, cui2, severity, advice 
+            FROM interactions 
+            WHERE (cui1, cui2) IN ({placeholders})
+        """
+        
+        rows = self.conn.execute(query, flat_params).fetchall()
+        return [{"pair": (r["cui1"], r["cui2"]), "severity": r["severity"], "advice": r["advice"]} for r in rows]

earnest-trilogy-465710-e7-6cc7bbbddb97.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "earnest-trilogy-465710-e7",
+  "private_key_id": "6cc7bbbddb970f16876e3f4120ff18be704f5ee5",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nJUzmpT+C4PQCHMepVFoaLw==\n-----END PRIVATE KEY-----\n",
+  "client_email": "aihackathon@earnest-trilogy-465710-e7.iam.gserviceaccount.com",
+  "client_id": "102524806684057807383",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/aihackathon%40earnest-trilogy-465710-e7.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

index.html

@@ -1,13 +0,0 @@
-<!doctype html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8" />
-    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Vite + Svelte + TS</title>
-  </head>
-  <body>
-    <div id="app"></div>
-    <script type="module" src="/src/main.ts"></script>
-  </body>
-</html>

models/signature_sia.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ba80e2f5c688e9fd9fc766a85f266d0a47201def65265ef294168546a2814de
+size 804888

package.json

@@ -1,20 +0,0 @@
-{
-  "name": "svelte",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "preview": "vite preview",
-    "check": "svelte-check --tsconfig ./tsconfig.app.json && tsc -p tsconfig.node.json"
-  },
-  "devDependencies": {
-    "@sveltejs/vite-plugin-svelte": "^5.0.3",
-    "@tsconfig/svelte": "^5.0.4",
-    "svelte": "^5.28.1",
-    "svelte-check": "^4.1.6",
-    "typescript": "~5.8.3",
-    "vite": "^6.3.5"
-  }
-}

prescription_validation/fuzzy_match.py

@@ -0,0 +1,44 @@
+# prescription_validation/fuzzy_match.py
+import sqlite3
+import re
+from rapidfuzz.distance import Levenshtein
+from config import DB_PATH, LEV_THRESH
+
+class RxLookup:
+    def __init__(self, db_path: str = DB_PATH):
+        self.conn = sqlite3.connect(db_path)
+        self.conn.row_factory = sqlite3.Row
+        self.drugs = self.conn.execute("SELECT name, cui FROM drugs").fetchall()
+
+    def _clean_token(self, token: str) -> str:
+        """Removes dosage, form factor, and non-alpha characters."""
+        cleaned = token.lower()
+        cleaned = re.sub(r'(\d+)\s*(mg|ml|mcg|tab|cap|#)', '', cleaned)
+        cleaned = re.sub(r'[^a-z]', '', cleaned)
+        return cleaned
+
+    def match(self, token: str) -> tuple[str | None, str | None]:
+        if not token:
+            return (None, None)
+            
+        cleaned_token = self._clean_token(token)
+        if not cleaned_token:
+            return (None, None)
+
+        best_match = None
+        min_distance = float('inf')
+
+        for row in self.drugs:
+            name, cui = row["name"], row["cui"]
+            cleaned_db_name = self._clean_token(name)
+            
+            distance = Levenshtein.distance(cleaned_token, cleaned_db_name)
+            
+            if distance < min_distance:
+                min_distance = distance
+                best_match = (name, cui)
+
+        if best_match and min_distance / len(cleaned_token) < LEV_THRESH:
+            return best_match
+
+        return (None, None)

prescription_validation/zone_detector.py

@@ -0,0 +1,27 @@
+# prescription_validation/zone_detector.py
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+class ZoneDetector:
+    def __init__(self, model_path: str = "models/signature_model.pt"):
+        try:
+            self.model = YOLO(model_path)
+            print(f"✅ Loaded local YOLO model from '{model_path}'")
+        except Exception as e:
+            self.model = None
+            print(f"❌ Failed to load local YOLO model from '{model_path}'. Please ensure the model file exists.")
+
+    def detect(self, img: np.ndarray) -> list[dict]:
+        if not self.model: return []
+        rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        results = self.model(rgb_img, verbose=False)
+        detections = []
+        for box in results[0].boxes:
+            x1, y1, x2, y2 = box.xyxy[0].tolist()
+            class_id = int(box.cls[0])
+            detections.append({
+                "label": self.model.names[class_id],
+                "bbox": tuple(map(int, [x1, y1, x2, y2])),
+            })
+        return detections

requirements.txt

@@ -0,0 +1,23 @@
+# Core
+streamlit==1.36.0
+python-dotenv==1.0.1
+
+# AI & Vision
+# Using Google's recommended versions for Gemini and Vision
+google-generativeai==0.7.1
+google-cloud-vision==3.7.3
+torch==2.3.1
+pillow==10.3.0
+transformers==4.41.0
+
+# OCR
+paddleocr==2.7.3
+# Using the CPU version of paddlepaddle for broader compatibility on HF Spaces
+paddlepaddle==2.6.1
+
+# Utils
+numpy==1.26.4
+requests==2.32.3
+opencv-python-headless==4.10.0.84
+scikit-image==0.22.0
+pytz==2024.1

scripts/build_interactions_db.py

@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+import sqlite3, csv, argparse
+import sys, os
+sys.path.append(os.getcwd())
+from config import DB_PATH, INTERACTIONS_CSV
+
+def build_db(db_path: str, csv_path: str):
+    conn = sqlite3.connect(db_path)
+    c = conn.cursor()
+    c.execute("DROP TABLE IF EXISTS interactions")
+    c.execute("CREATE TABLE interactions(cui1 TEXT, cui2 TEXT, severity TEXT, advice TEXT, UNIQUE(cui1, cui2))")
+    
+    with open(csv_path, newline='', encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            # Ensure consistent order to avoid duplicate entries like (A,B) and (B,A)
+            cui1, cui2 = sorted((row['cui1'], row['cui2']))
+            c.execute(
+                "INSERT OR IGNORE INTO interactions(cui1, cui2, severity, advice) VALUES(?,?,?,?)",
+                (cui1, cui2, row['severity'], row['advice'])
+            )
+    conn.commit()
+    conn.close()
+
+if __name__ == "__main__":
+    build_db(DB_PATH, INTERACTIONS_CSV)
+    print(f"✅ Interactions DB built at {DB_PATH}")

scripts/build_rxnorm_db.py

@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+import sqlite3, csv, argparse
+from metaphone import doublemetaphone
+import sys, os
+sys.path.append(os.getcwd())
+from config import DB_PATH, RXNORM_CSV
+
+def build_db(db_path: str, csv_path: str):
+    conn = sqlite3.connect(db_path)
+    c = conn.cursor()
+    c.execute("DROP TABLE IF EXISTS drugs")
+    c.execute("DROP TABLE IF EXISTS metaphone")
+    c.execute("CREATE TABLE drugs(name TEXT PRIMARY KEY, cui TEXT)")
+    c.execute("CREATE TABLE metaphone(meta TEXT, name TEXT, cui TEXT)")
+
+    with open(csv_path, newline='', encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            nm, cui = row['name'], row['cui']
+            c.execute("INSERT OR IGNORE INTO drugs VALUES(?,?)", (nm, cui))
+            meta = doublemetaphone(nm)[0]
+            c.execute("INSERT INTO metaphone VALUES(?,?,?)", (meta, nm, cui))
+    conn.commit()
+    conn.close()
+
+if __name__ == "__main__":
+    build_db(DB_PATH, RXNORM_CSV)
+    print(f"✅ RxNorm DB built at {DB_PATH}")

scripts/create_dummy_signature_model.py

@@ -0,0 +1,32 @@
+#!/usr/bin/env python3
+import os
+import tensorflow as tf
+from tensorflow.keras.layers import Input, Flatten, Dense, Subtract
+from tensorflow.keras.models import Model
+import sys
+sys.path.append(os.getcwd())
+from config import SIA_MODEL_PATH
+
+def build_dummy_siamese():
+    inp_a = Input(shape=(224, 224, 1), name="img_a")
+    inp_b = Input(shape=(224, 224, 1), name="img_b")
+    encoder_input = Input(shape=(224, 224, 1))
+    x = Flatten()(encoder_input)
+    x = Dense(16, activation="relu")(x)
+    encoder = Model(encoder_input, x)
+    encoded_a = encoder(inp_a)
+    encoded_b = encoder(inp_b)
+    distance = Subtract()([encoded_a, encoded_b])
+    model = Model(inputs=[inp_a, inp_b], outputs=distance)
+    return model
+
+if __name__ == "__main__":
+    print("Building and converting dummy Siamese model...")
+    model = build_dummy_siamese()
+    converter = tf.lite.TFLiteConverter.from_keras_model(model)
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    tflite_model = converter.convert()
+    os.makedirs(os.path.dirname(SIA_MODEL_PATH), exist_ok=True)
+    with open(SIA_MODEL_PATH, "wb") as f:
+        f.write(tflite_model)
+    print(f"✅ Dummy TFLite signature model saved to '{SIA_MODEL_PATH}'")

signature_verification/signature_detector.py

@@ -0,0 +1,40 @@
+# signature_verification/signature_detector.py
+import cv2
+import numpy as np
+
+class SignatureDetector:
+    """
+    Detects and crops a signature from a prescription image using refined OpenCV contour analysis.
+    This method is fast, offline, and does not require a pre-trained model.
+    """
+    def crop(self, img: np.ndarray) -> np.ndarray | None:
+        if img is None: return None
+
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+        _, thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+
+        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if not contours: return None
+
+        height, width = img.shape[:2]
+        significant_contours = []
+        for cnt in contours:
+            x, y, w, h = cv2.boundingRect(cnt)
+            # Filter for contours that are reasonably large and in the bottom 70% of the page
+            if w > 50 and h > 15 and y > height * 0.3:
+                if w < width * 0.8 and h < height * 0.4:
+                    significant_contours.append(cnt)
+
+        if not significant_contours: return None
+
+        largest_contour = max(significant_contours, key=cv2.contourArea)
+        x, y, w, h = cv2.boundingRect(largest_contour)
+        
+        padding = 15
+        x1 = max(0, x - padding)
+        y1 = max(0, y - padding)
+        x2 = min(width, x + w + padding)
+        y2 = min(height, y + h + padding)
+        
+        return img[y1:y2, x1:x2]

signature_verification/signature_generator.py

@@ -0,0 +1,50 @@
+import os
+import logging
+import numpy as np
+import cv2
+from dotenv import load_dotenv
+from huggingface_hub import InferenceClient, HfFolder
+from PIL import Image
+
+load_dotenv()
+
+# Acquire token
+hf_token = os.getenv("HUGGING_FACE_HUB_TOKEN") or HfFolder.get_token()
+if not hf_token:
+    print("❌ No Hugging Face token found. Signature generation will fail.")
+    CLIENT = None
+else:
+    print("✅ Using Hugging Face token for signature generation.")
+    CLIENT = InferenceClient(token=hf_token)
+
+MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0"
+
+def generate_signatures(name: str, num_variations: int = 3) -> list:
+    """Generates multiple signature variations for a given name."""
+    if CLIENT is None:
+        return []
+
+    prompts = [
+        f"A clean, elegant, handwritten signature of the name '{name}' on a plain white background. Cursive, professional.",
+        f"Calligraphy signature of '{name}'. Black ink on white paper. Minimalist, artistic.",
+        f"A doctor's signature for '{name}'. Quick, scribbled, but legible. Official-looking script."
+    ]
+    images = []
+    for i in range(num_variations):
+        prompt = prompts[i % len(prompts)]
+        logging.info(f"Generating signature variation {i+1} for '{name}'…")
+        try:
+            pil_img = CLIENT.text_to_image(
+                prompt,
+                model=MODEL_ID,
+                negative_prompt=(
+                    "photograph, text, multiple signatures, watermark, blurry, colorful, background"
+                ),
+                guidance_scale=8.0
+            )
+            np_img = np.array(pil_img)
+            cv_img = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)
+            images.append(cv_img)
+        except Exception as e:
+            logging.error(f"❌ Signature generation failed: {e}")
+    return images

signature_verification/signature_siamese.py

@@ -0,0 +1,34 @@
+# signature_verification/signature_siamese.py
+import cv2
+import numpy as np
+import tensorflow as tf
+from config import SIA_MODEL_PATH, SIG_THRESH
+
+class SignatureSiamese:
+    def __init__(self, model_path: str = SIA_MODEL_PATH):
+        try:
+            self.interp = tf.lite.Interpreter(model_path=model_path)
+            self.interp.allocate_tensors()
+            ids = self.interp.get_input_details()
+            self.in1, self.in2 = ids[0]['index'], ids[1]['index']
+            self.out_idx = self.interp.get_output_details()[0]['index']
+            print(f"✅ Loaded TFLite Siamese model from {model_path}")
+        except Exception as e:
+            self.interp = None
+            print(f"❌ Failed to load Siamese model from {model_path}: {e}")
+
+    def _prep(self, img: np.ndarray) -> np.ndarray:
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if img.ndim == 3 else img
+        r = cv2.resize(gray, (224, 224))
+        return r.reshape(1, 224, 224, 1).astype(np.float32) / 255.0
+
+    def verify(self, img1: np.ndarray, img2: np.ndarray) -> tuple[bool, float]:
+        if not self.interp:
+            return False, float('inf')
+        inp1, inp2 = self._prep(img1), self._prep(img2)
+        self.interp.set_tensor(self.in1, inp1)
+        self.interp.set_tensor(self.in2, inp2)
+        self.interp.invoke()
+        diff = self.interp.get_tensor(self.out_idx)
+        dist = np.linalg.norm(diff)
+        return dist < SIG_THRESH, dist

src/App.svelte

@@ -1,47 +0,0 @@
-<script lang="ts">
-  import svelteLogo from './assets/svelte.svg'
-  import viteLogo from '/vite.svg'
-  import Counter from './lib/Counter.svelte'
-</script>
-
-<main>
-  <div>
-    <a href="https://vite.dev" target="_blank" rel="noreferrer">
-      <img src={viteLogo} class="logo" alt="Vite Logo" />
-    </a>
-    <a href="https://svelte.dev" target="_blank" rel="noreferrer">
-      <img src={svelteLogo} class="logo svelte" alt="Svelte Logo" />
-    </a>
-  </div>
-  <h1>Vite + Svelte</h1>
-
-  <div class="card">
-    <Counter />
-  </div>
-
-  <p>
-    Check out <a href="https://github.com/sveltejs/kit#readme" target="_blank" rel="noreferrer">SvelteKit</a>, the official Svelte app framework powered by Vite!
-  </p>
-
-  <p class="read-the-docs">
-    Click on the Vite and Svelte logos to learn more
-  </p>
-</main>
-
-<style>
-  .logo {
-    height: 6em;
-    padding: 1.5em;
-    will-change: filter;
-    transition: filter 300ms;
-  }
-  .logo:hover {
-    filter: drop-shadow(0 0 2em #646cffaa);
-  }
-  .logo.svelte:hover {
-    filter: drop-shadow(0 0 2em #ff3e00aa);
-  }
-  .read-the-docs {
-    color: #888;
-  }
-</style>

src/app.css

@@ -1,79 +0,0 @@
-:root {
-  font-family: system-ui, Avenir, Helvetica, Arial, sans-serif;
-  line-height: 1.5;
-  font-weight: 400;
-
-  color-scheme: light dark;
-  color: rgba(255, 255, 255, 0.87);
-  background-color: #242424;
-
-  font-synthesis: none;
-  text-rendering: optimizeLegibility;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-
-a {
-  font-weight: 500;
-  color: #646cff;
-  text-decoration: inherit;
-}
-a:hover {
-  color: #535bf2;
-}
-
-body {
-  margin: 0;
-  display: flex;
-  place-items: center;
-  min-width: 320px;
-  min-height: 100vh;
-}
-
-h1 {
-  font-size: 3.2em;
-  line-height: 1.1;
-}
-
-.card {
-  padding: 2em;
-}
-
-#app {
-  max-width: 1280px;
-  margin: 0 auto;
-  padding: 2rem;
-  text-align: center;
-}
-
-button {
-  border-radius: 8px;
-  border: 1px solid transparent;
-  padding: 0.6em 1.2em;
-  font-size: 1em;
-  font-weight: 500;
-  font-family: inherit;
-  background-color: #1a1a1a;
-  cursor: pointer;
-  transition: border-color 0.25s;
-}
-button:hover {
-  border-color: #646cff;
-}
-button:focus,
-button:focus-visible {
-  outline: 4px auto -webkit-focus-ring-color;
-}
-
-@media (prefers-color-scheme: light) {
-  :root {
-    color: #213547;
-    background-color: #ffffff;
-  }
-  a:hover {
-    color: #747bff;
-  }
-  button {
-    background-color: #f9f9f9;
-  }
-}

src/lib/Counter.svelte

@@ -1,10 +0,0 @@
-<script lang="ts">
-  let count: number = $state(0)
-  const increment = () => {
-    count += 1
-  }
-</script>
-
-<button onclick={increment}>
-  count is {count}
-</button>

src/main.ts

@@ -1,9 +0,0 @@
-import { mount } from 'svelte'
-import './app.css'
-import App from './App.svelte'
-
-const app = mount(App, {
-  target: document.getElementById('app')!,
-})
-
-export default app

src/vite-env.d.ts

@@ -1,2 +0,0 @@
-/// <reference types="svelte" />
-/// <reference types="vite/client" />