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.gitattributes

@@ -33,3 +33,6 @@ 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
+others/demo-screenshot.png filter=lfs diff=lfs merge=lfs -text
+others/Logo.png filter=lfs diff=lfs merge=lfs -text
+others/Screen[[:space:]]Shot[[:space:]]2025-03-01[[:space:]]at[[:space:]]2.49.21[[:space:]]AM.png filter=lfs diff=lfs merge=lfs -text

batch_emails.txt

@@ -0,0 +1,5 @@
+Please review the attached quarterly report before Friday's board meeting.
+Limited time offer! 50% off all premium subscriptions - use code FLASHSALE50
+Your document 'project_proposal.pdf' is ready for download at https://docs.example.com
+WARNING: Unusual login attempt detected from new device. Verify your identity now.
+Don't forget Sarah's birthday tomorrow! Here's her wishlist: https://giftlist.com/sarah

index.ipynb

@@ -0,0 +1,304 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "53e9feaa-53de-4377-8d45-aa1f7264ae3a",
+   "metadata": {},
+   "source": [
+    "### First Neccesary libararies needs to be loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8864f53f-15d2-403c-a905-3da509cfb050",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "import transformers\n",
+    "from transformers import pipeline\n",
+    "import tf_keras as keras\n",
+    "import pandas as pd\n",
+    "import tempfile\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bb130f0-d8c8-459d-918f-84025c93bc05",
+   "metadata": {},
+   "source": [
+    "### Now we import our already pre-trained model from "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1c4e29a8-9b24-47d6-b14b-0e2a7e4d66a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertForSequenceClassification: ['bert.embeddings.position_ids']\n",
+      "- This IS expected if you are initializing TFBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing TFBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "All the weights of TFBertForSequenceClassification were initialized from the PyTorch model.\n",
+      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.\n",
+      "Device set to use 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load pre-trained spam classifier\n",
+    "spam_classifier = pipeline(\n",
+    "    \"text-classification\",\n",
+    "    model=\"mrm8488/bert-tiny-finetuned-sms-spam-detection\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cb4ab66-2833-40bd-87cb-4d712398e431",
+   "metadata": {},
+   "source": [
+    "### Since single email check is hassle we will make a function for batch classication\n",
+    "### we should assume certain file template or format so our program knows what to expect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b8ae7b3b-5273-4242-85db-b5cb622a4046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def classify_batch(file):\n",
+    "    \"\"\"Process uploaded CSV/TXT file with multiple emails\"\"\"\n",
+    "    try:\n",
+    "        results = []\n",
+    "        \n",
+    "        # Check if file exists\n",
+    "        if not file.name:\n",
+    "            raise gr.Error(\"No file uploaded\")\n",
+    "\n",
+    "        # --- CSV File Handling ---\n",
+    "        if file.name.endswith('.csv'):\n",
+    "            df = pd.read_csv(file)\n",
+    "            \n",
+    "            # Check for required email column\n",
+    "            if 'email' not in df.columns:\n",
+    "                raise gr.Error(\"CSV file must contain a column named 'email'\")\n",
+    "                \n",
+    "            emails = df['email'].tolist()\n",
+    "\n",
+    "        # --- Text File Handling ---\n",
+    "        elif file.name.endswith('.txt'):\n",
+    "            with open(file.name, 'r') as f:\n",
+    "                emails = f.readlines()\n",
+    "        \n",
+    "        # --- Unsupported Format ---\n",
+    "        else:\n",
+    "            raise gr.Error(\"Unsupported file format. Only CSV/TXT accepted\")\n",
+    "\n",
+    "        # Process emails (common for both formats)\n",
+    "        emails = emails[:100]  # Limit to 100 emails\n",
+    "        for email in emails:\n",
+    "            # Handle empty lines in text files\n",
+    "            if not email.strip():\n",
+    "                continue\n",
+    "                \n",
+    "            prediction = spam_classifier(email.strip())[0]\n",
+    "            results.append({\n",
+    "                \"email\": email.strip()[:50] + \"...\",\n",
+    "                \"label\": \"SPAM\" if prediction[\"label\"] == \"LABEL_1\" else \"HAM\",\n",
+    "                \"confidence\": f\"{prediction['score']:.4f}\"\n",
+    "            })\n",
+    "\n",
+    "        return pd.DataFrame(results)\n",
+    "\n",
+    "    except gr.Error as e:\n",
+    "        raise e  # Show pop-up for expected errors\n",
+    "    except Exception as e:\n",
+    "        raise gr.Error(f\"Processing error: {str(e)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ccb5108-a5d4-4f61-b363-dc4c9d25b4fb",
+   "metadata": {},
+   "source": [
+    "### We define simple function for classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1336344b-54c3-431d-8d89-c351b0c24f80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def classify_text(text):\n",
+    "    result = spam_classifier(text)[0]\n",
+    "    return {\n",
+    "        \"Spam\": result[\"score\"] if result[\"label\"] == \"LABEL_1\" else 1 - result[\"score\"],\n",
+    "        \"Ham\": result[\"score\"] if result[\"label\"] == \"LABEL_0\" else 1 - result[\"score\"]\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4559470b-1356-4f9d-b977-44bfbe117f3d",
+   "metadata": {},
+   "source": [
+    "### using gradio we will make a simple interface for our program"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "67927628-4ca2-43ac-80c3-a1f9d4771d5d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7868\n",
+      "Caching examples at: '/Users/techgarage/Projects/spamedar/.gradio/cached_examples/143'\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7868/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with gr.Blocks(title=\"Spam Classifier Pro\") as demo:\n",
+    "    gr.Markdown(\"# 📧 Welcome to Spamedar!\")\n",
+    "    \n",
+    "    \n",
+    "    with gr.Tab(\"✉️ Single Email\"):\n",
+    "        gr.Interface(\n",
+    "            description=\"<h2>Copy your email to find out if it's a is Spam or Ham👇<h2>\",\n",
+    "            fn=classify_text,\n",
+    "            inputs=gr.Textbox(label=\"Input Email\", lines=3),\n",
+    "            outputs=gr.Label(label=\"Classification\"),\n",
+    "            examples=[\n",
+    "                [\"Urgent: Verify your account details now!\"],\n",
+    "                [\"Hey, can we meet tomorrow to discuss the project?\"],\n",
+    "                [\"WINNER! You've been selected for a $1000 Walmart Gift Card!\"],\n",
+    "                [\"Your account needs verification. Click here to confirm your details.\"],\n",
+    "                [\"Meeting rescheduled to Friday 2 PM\"]\n",
+    "            ]\n",
+    "        )\n",
+    "    current_dir = os.getcwd()\n",
+    "    with gr.Tab(\"📨 Multiple Emails\"):\n",
+    "        gr.Markdown(\"## Upload email batch (CSV or TXT)\")\n",
+    "        file_input = gr.File(label=\"Upload File\", file_types=[\".csv\", \".txt\"])\n",
+    "        clear_btn = gr.Button(\"Clear Selection\", variant=\"secondary\")\n",
+    "        output_table = gr.Dataframe(\n",
+    "            headers=[\"email\", \"label\", \"confidence\"],\n",
+    "            datatype=[\"str\", \"str\", \"number\"],\n",
+    "            interactive=False,\n",
+    "            label=\"Classification Results\"\n",
+    "        )\n",
+    "        download_btn = gr.DownloadButton(label=\"Download Results\")\n",
+    "\n",
+    "        def process_file(file):\n",
+    "            \"\"\"Process file and return (display_df, download_path)\"\"\"\n",
+    "            try:\n",
+    "                if file is None:\n",
+    "                    return pd.DataFrame(), None\n",
+    "\n",
+    "                results_df = classify_batch(file)\n",
+    "                with tempfile.NamedTemporaryFile(suffix=\".csv\", delete=False) as f:\n",
+    "                    results_df.to_csv(f.name, index=False)\n",
+    "                    return results_df, f.name\n",
+    "            except Exception as e:\n",
+    "                raise gr.Error(f\"Error processing file: {str(e)}\")\n",
+    "\n",
+    "        def clear_selection():\n",
+    "            ###clear file input and results function\n",
+    "            return None, pd.DataFrame(), None\n",
+    "        \n",
+    "        file_input.upload(\n",
+    "            fn=process_file,\n",
+    "            inputs=file_input,\n",
+    "            outputs=[output_table, download_btn]  # Update both components\n",
+    "        )\n",
+    "\n",
+    "        clear_btn.click(\n",
+    "            fn=clear_selection,\n",
+    "            outputs=[file_input, output_table, download_btn]\n",
+    "        )\n",
+    "\n",
+    "        example_files= [\n",
+    "            os.path.join(os.getcwd(), \"sample_emails.csv\"),\n",
+    "            os.path.join(os.getcwd(), \"batch_emails.txt\"),\n",
+    "        ]\n",
+    "        if all(os.path.exists(f) for f in example_files):\n",
+    "            gr.Examples(\n",
+    "                examples=[[f] for f in example_files],\n",
+    "                inputs=file_input,\n",
+    "                outputs=[output_table, download_btn],\n",
+    "                fn=process_file,\n",
+    "                cache_examples=True,\n",
+    "                label=\"Click any example below to test:\"\n",
+    "            )\n",
+    "            \n",
+    "        else:\n",
+    "            print(\"Warning: Example files missing. Place these in your project root:\")\n",
+    "            print(\"- sample_emails.csv\")\n",
+    "            print(\"- batch_emails.txt\")\n",
+    "        \n",
+    "if __name__ == \"__main__\":\n",
+    "    demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18c2a4bd-0404-46ec-87b1-4f47b5802150",
+   "metadata": {},
+   "source": [
+    "### Thank you for following the guide until the end.🚀👾\n",
+    "code: Raouf Jivad(with a lil help of GPT 😃)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

others/MainNote Backup.ipynb

@@ -0,0 +1,532 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "53e9feaa-53de-4377-8d45-aa1f7264ae3a",
+   "metadata": {},
+   "source": [
+    "### First Neccesary libararies needs to be loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "8864f53f-15d2-403c-a905-3da509cfb050",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "import transformers\n",
+    "from transformers import pipeline\n",
+    "import tf_keras as keras\n",
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bb130f0-d8c8-459d-918f-84025c93bc05",
+   "metadata": {},
+   "source": [
+    "### Now we import our already pre-trained model from "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1c4e29a8-9b24-47d6-b14b-0e2a7e4d66a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertForSequenceClassification: ['bert.embeddings.position_ids']\n",
+      "- This IS expected if you are initializing TFBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing TFBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "All the weights of TFBertForSequenceClassification were initialized from the PyTorch model.\n",
+      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.\n",
+      "Device set to use 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load pre-trained spam classifier\n",
+    "spam_classifier = pipeline(\n",
+    "    \"text-classification\",\n",
+    "    model=\"mrm8488/bert-tiny-finetuned-sms-spam-detection\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8b27be80-1a6c-4c6c-a3ac-fe3b6f1378ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tempfile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cb4ab66-2833-40bd-87cb-4d712398e431",
+   "metadata": {},
+   "source": [
+    "### Since single email check is hassle we will make a function for batch classication\n",
+    "### we should assume certain file template or format so our program knows what to expect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "215fd411-623f-43ce-8775-1bbd2c130b56",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def classify_batch(file):\n",
+    "    \"\"\"Process uploaded CSV/TXT file with multiple emails\"\"\"\n",
+    "    results = []\n",
+    "    if file.name.endswith('.csv'):    # Handling the emails in CSV files format\n",
+    "        df = pd.read_csv(file)\n",
+    "        emails = df['email'].tolist()  # we assume there's a column named 'email'\n",
+    "        for idx, email in enumerate(emails):\n",
+    "            prediction = spam_classifier(email)[0]\n",
+    "            results.append({\n",
+    "                \"email\": email[:50] + \"...\",  # Truncate for display\n",
+    "                \"label\": \"SPAM\" if prediction[\"label\"] == \"LABEL_1\" else \"HAM\",\n",
+    "                \"confidence\": prediction[\"score\"]\n",
+    "            })\n",
+    "\n",
+    "    ### Now we almost do the same thing but for text files (one email per line)\n",
+    "    elif file.name.endswith('.txt'):\n",
+    "        with open(file.name, 'r') as f:\n",
+    "            emails = f.readlines()\n",
+    "            for email in emails:\n",
+    "                prediction = spam_classifier(email.strip())[0]\n",
+    "                results.append({\n",
+    "                    \"email\": email.strip()[:50] + \"...\",\n",
+    "                    \"label\": \"SPAM\" if prediction[\"label\"] == \"LABEL_1\" else \"HAM\",\n",
+    "                    \"confidence\": f\"{prediction['score']:.4f}\"\n",
+    "                })\n",
+    "        ### --------------- Here we implemnt some condition for our uploaded files __________\n",
+    "        try:\n",
+    "            results = []\n",
+    "            if not file.name:\n",
+    "                raise gr.Error(\"No file uploaded\")\n",
+    "                # Handle CSV files\n",
+    "            if file.name.endswith('.csv'):\n",
+    "                df = pd.read_csv(file)\n",
+    "                if 'email' not in df.columns:\n",
+    "                    raise gr.Error(\"CSV file must contain 'email' column\")\n",
+    "                emails = df['email'].tolist()\n",
+    "            \n",
+    "        # Handle text files\n",
+    "            elif file.name.endswith('.txt'):\n",
+    "                with open(file.name, 'r') as f:\n",
+    "                    emails = f.readlines()\n",
+    "            else:\n",
+    "                raise gr.Error(\"Unsupported file format. Only CSV/TXT accepted\")\n",
+    "                \n",
+    "            # Limit to 100 emails max for demo\n",
+    "            emails = emails[:100]\n",
+    "\n",
+    "\n",
+    "        except gr.Error as e:\n",
+    "            raise e  # Re-raise Gradio errors to show pop-up\n",
+    "        except Exception as e:\n",
+    "            raise gr.Error(f\"An unexpected error occurred: {str(e)}\")\n",
+    "\n",
+    "\n",
+    "    return pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "b8ae7b3b-5273-4242-85db-b5cb622a4046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def classify_batch(file):\n",
+    "    \"\"\"Process uploaded CSV/TXT file with multiple emails\"\"\"\n",
+    "    try:\n",
+    "        results = []\n",
+    "        \n",
+    "        # Check if file exists\n",
+    "        if not file.name:\n",
+    "            raise gr.Error(\"No file uploaded\")\n",
+    "\n",
+    "        # --- CSV File Handling ---\n",
+    "        if file.name.endswith('.csv'):\n",
+    "            df = pd.read_csv(file)\n",
+    "            \n",
+    "            # Check for required email column\n",
+    "            if 'email' not in df.columns:\n",
+    "                raise gr.Error(\"CSV file must contain a column named 'email'\")\n",
+    "                \n",
+    "            emails = df['email'].tolist()\n",
+    "\n",
+    "        # --- Text File Handling ---\n",
+    "        elif file.name.endswith('.txt'):\n",
+    "            with open(file.name, 'r') as f:\n",
+    "                emails = f.readlines()\n",
+    "        \n",
+    "        # --- Unsupported Format ---\n",
+    "        else:\n",
+    "            raise gr.Error(\"Unsupported file format. Only CSV/TXT accepted\")\n",
+    "\n",
+    "        # Process emails (common for both formats)\n",
+    "        emails = emails[:100]  # Limit to 100 emails\n",
+    "        for email in emails:\n",
+    "            # Handle empty lines in text files\n",
+    "            if not email.strip():\n",
+    "                continue\n",
+    "                \n",
+    "            prediction = spam_classifier(email.strip())[0]\n",
+    "            results.append({\n",
+    "                \"email\": email.strip()[:50] + \"...\",\n",
+    "                \"label\": \"SPAM\" if prediction[\"label\"] == \"LABEL_1\" else \"HAM\",\n",
+    "                \"confidence\": f\"{prediction['score']:.4f}\"\n",
+    "            })\n",
+    "\n",
+    "        return pd.DataFrame(results)\n",
+    "\n",
+    "    except gr.Error as e:\n",
+    "        raise e  # Show pop-up for expected errors\n",
+    "    except Exception as e:\n",
+    "        raise gr.Error(f\"Processing error: {str(e)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ccb5108-a5d4-4f61-b363-dc4c9d25b4fb",
+   "metadata": {},
+   "source": [
+    "### We define simple function for classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "1336344b-54c3-431d-8d89-c351b0c24f80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def classify_text(text):\n",
+    "    result = spam_classifier(text)[0]\n",
+    "    return {\n",
+    "        \"Spam\": result[\"score\"] if result[\"label\"] == \"LABEL_1\" else 1 - result[\"score\"],\n",
+    "        \"Ham\": result[\"score\"] if result[\"label\"] == \"LABEL_0\" else 1 - result[\"score\"]\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "c428e83a-dbe6-4c91-8a05-5b550652c181",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7867\n",
+      "Caching examples at: '/Users/techgarage/Projects/spamedar/.gradio/cached_examples/318'\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7867/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with gr.Blocks(title=\"Spam Classifier Pro\") as demo:\n",
+    "    gr.Markdown(\"# 📧 Spam Classification System\")\n",
+    "    \n",
+    "    with gr.Tab(\"Single Email\"):\n",
+    "        gr.Interface(\n",
+    "            fn=classify_text,\n",
+    "            inputs=gr.Textbox(label=\"Input Email\", lines=3),\n",
+    "            outputs=gr.Label(label=\"Classification\"),\n",
+    "            examples=[\n",
+    "                [\"Urgent: Verify your account details now!\"],\n",
+    "                [\"Meeting rescheduled to Friday 2 PM\"]\n",
+    "            ]\n",
+    "        )\n",
+    "    current_dir = os.getcwd()\n",
+    "    with gr.Tab(\"Batch Processing\"):\n",
+    "        gr.Markdown(\"## Upload email batch (CSV or TXT)\")\n",
+    "        file_input = gr.File(label=\"Upload File\", file_types=[\".csv\", \".txt\"])\n",
+    "        clear_btn = gr.Button(\"Clear Selection\", variant=\"secondary\")\n",
+    "        output_table = gr.Dataframe(\n",
+    "            headers=[\"email\", \"label\", \"confidence\"],\n",
+    "            datatype=[\"str\", \"str\", \"number\"],\n",
+    "            interactive=False,\n",
+    "            label=\"Classification Results\"\n",
+    "        )\n",
+    "        download_btn = gr.DownloadButton(label=\"Download Results\")\n",
+    "        \n",
+    "        def process_file(file):\n",
+    "            \"\"\"Process file and return (display_df, download_path)\"\"\"\n",
+    "            try:\n",
+    "                if file is None:\n",
+    "                    return pd.DataFrame(), None\n",
+    "                    \n",
+    "                results_df = classify_batch(file)\n",
+    "                with tempfile.NamedTemporaryFile(suffix=\".csv\", delete=False) as f:\n",
+    "                    results_df.to_csv(f.name, index=False)\n",
+    "                    return results_df, f.name\n",
+    "            except Exception as e:\n",
+    "                raise gr.Error(f\"Error processing file: {str(e)}\")\n",
+    "\n",
+    "        def clear_selection():\n",
+    "            \"\"\"Clear file input and results\"\"\"\n",
+    "            return None, pd.DataFrame(), None\n",
+    "        \n",
+    "        file_input.upload(\n",
+    "            fn=process_file,\n",
+    "            inputs=file_input,\n",
+    "            outputs=[output_table, download_btn]\n",
+    "        )\n",
+    "\n",
+    "        clear_btn.click(\n",
+    "            fn=clear_selection,\n",
+    "            outputs=[file_input, output_table, download_btn]\n",
+    "        )\n",
+    "        \n",
+    "        example_files = [\n",
+    "            os.path.join(os.getcwd(), \"sample_emails.csv\"),\n",
+    "            os.path.join(os.getcwd(), \"batch_emails.txt\")\n",
+    "        ]\n",
+    "        if all(os.path.exists(f) for f in example_files):\n",
+    "            gr.Examples(\n",
+    "                examples=[[f] for f in example_files],\n",
+    "                inputs=file_input,\n",
+    "                outputs=[output_table, download_btn],\n",
+    "                fn=process_file,\n",
+    "                cache_examples=True,\n",
+    "                label=\"Click any example below to test:\"\n",
+    "            )\n",
+    "\n",
+    "        else:\n",
+    "            print(\"Warning: Example files missing. Place these in your project root:\")\n",
+    "            print(\"- sample_emails.csv\")\n",
+    "            print(\"- batch_emails.txt\")\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4559470b-1356-4f9d-b977-44bfbe117f3d",
+   "metadata": {},
+   "source": [
+    "### using gradio we will make a simple interface for our program"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "dfa7f58b-0ab8-445e-bfab-1396f2443033",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "67927628-4ca2-43ac-80c3-a1f9d4771d5d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7863\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7863/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with gr.Blocks(title=\"Spam Classifier Pro\") as demo:\n",
+    "    gr.Markdown(\"# 📧 Welcome to Spamedar!\")\n",
+    "    \n",
+    "    \n",
+    "    with gr.Tab(\"✉️ Single Email\"):\n",
+    "        gr.Interface(\n",
+    "            fn=classify_text,\n",
+    "            inputs=gr.Textbox(label=\"Input Email\", lines=3),\n",
+    "            outputs=gr.Label(label=\"Classification\"),\n",
+    "            examples=[\n",
+    "                [\"Urgent: Verify your account details now!\"],\n",
+    "                [\"Hey, can we meet tomorrow to discuss the project?\"],\n",
+    "                [\"WINNER! You've been selected for a $1000 Walmart Gift Card!\"],\n",
+    "                [\"Your account needs verification. Click here to confirm your details.\"],\n",
+    "                [\"Meeting rescheduled to Friday 2 PM\"]\n",
+    "            ]\n",
+    "        )\n",
+    "    \n",
+    "    with gr.Tab(\"📨 Multiple Emails\"):\n",
+    "        gr.Markdown(\"## Upload email batch (CSV or TXT)\")\n",
+    "        file_input = gr.File(label=\"Upload File\", file_types=[\".csv\", \".txt\"])\n",
+    "        output_table = gr.Dataframe(\n",
+    "            headers=[\"email\", \"label\", \"confidence\"],\n",
+    "            datatype=[\"str\", \"str\", \"number\"],\n",
+    "            interactive=False,\n",
+    "            label=\"Classification Results\"\n",
+    "        )\n",
+    "        download_btn = gr.DownloadButton(label=\"Download Results\")\n",
+    "\n",
+    "        def process_file(file):\n",
+    "            \"\"\"Process file and return (display_df, download_path)\"\"\"\n",
+    "            results_df = classify_batch(file)\n",
+    "            \n",
+    "            with tempfile.NamedTemporaryFile(suffix=\".csv\", delete=False) as f:\n",
+    "                results_df.to_csv(f.name, index=False)\n",
+    "                return results_df, f.name\n",
+    "        \n",
+    "        file_input.upload(\n",
+    "            fn=process_file,\n",
+    "            inputs=file_input,\n",
+    "            outputs=[output_table, download_btn]  # Update both components\n",
+    "    )\n",
+    "        \n",
+    "        gr.Examples(\n",
+    "            examples=[\n",
+    "                [\"sample_emails.csv\"],\n",
+    "                [\"batch_emails.txt\"]\n",
+    "            ],\n",
+    "            inputs=file_input\n",
+    "        )\n",
+    "if __name__ == \"__main__\":\n",
+    "    demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "188e3c31-38ef-4191-8b24-5487724466bd",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true,
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'sample_emails.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[17], line 31\u001b[0m\n\u001b[1;32m     23\u001b[0m         download_btn \u001b[38;5;241m=\u001b[39m gr\u001b[38;5;241m.\u001b[39mFile(label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownload Results\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     25\u001b[0m         file_input\u001b[38;5;241m.\u001b[39mupload(\n\u001b[1;32m     26\u001b[0m             fn\u001b[38;5;241m=\u001b[39mclassify_batch,\n\u001b[1;32m     27\u001b[0m             inputs\u001b[38;5;241m=\u001b[39mfile_input,\n\u001b[1;32m     28\u001b[0m             outputs\u001b[38;5;241m=\u001b[39moutput_table\n\u001b[1;32m     29\u001b[0m         )\n\u001b[0;32m---> 31\u001b[0m         \u001b[43mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mExamples\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[43m            \u001b[49m\u001b[43mexamples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[1;32m     33\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msample_emails.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     34\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_emails.txt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m     35\u001b[0m \u001b[43m            \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     36\u001b[0m \u001b[43m            \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfile_input\u001b[49m\n\u001b[1;32m     37\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m     40\u001b[0m     demo\u001b[38;5;241m.\u001b[39mlaunch()\n",
+      "File \u001b[0;32m~/anaconda3/envs/grad/lib/python3.10/site-packages/gradio/helpers.py:57\u001b[0m, in \u001b[0;36mcreate_examples\u001b[0;34m(examples, inputs, outputs, fn, cache_examples, cache_mode, examples_per_page, _api_mode, label, elem_id, run_on_click, preprocess, postprocess, api_name, batch, example_labels, visible)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mcreate_examples\u001b[39m(\n\u001b[1;32m     37\u001b[0m     examples: \u001b[38;5;28mlist\u001b[39m[Any] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mlist\u001b[39m[Any]] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m     38\u001b[0m     inputs: Component \u001b[38;5;241m|\u001b[39m Sequence[Component],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     54\u001b[0m     visible: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m     55\u001b[0m ):\n\u001b[1;32m     56\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Top-level synchronous function that creates Examples. Provided for backwards compatibility, i.e. so that gr.Examples(...) can be used to create the Examples component.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 57\u001b[0m     examples_obj \u001b[38;5;241m=\u001b[39m \u001b[43mExamples\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     58\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexamples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexamples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     60\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     61\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     62\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_examples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     63\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexamples_per_page\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexamples_per_page\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_api_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_api_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     67\u001b[0m \u001b[43m        \u001b[49m\u001b[43melem_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43melem_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     68\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_on_click\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_on_click\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     69\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreprocess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     70\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpostprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpostprocess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[43m        \u001b[49m\u001b[43mapi_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexample_labels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexample_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvisible\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvisible\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     75\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_initiated_directly\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     76\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     77\u001b[0m     examples_obj\u001b[38;5;241m.\u001b[39mcreate()\n\u001b[1;32m     78\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m examples_obj\n",
+      "File \u001b[0;32m~/anaconda3/envs/grad/lib/python3.10/site-packages/gradio/helpers.py:294\u001b[0m, in \u001b[0;36mExamples.__init__\u001b[0;34m(self, examples, inputs, outputs, fn, cache_examples, cache_mode, examples_per_page, _api_mode, label, elem_id, run_on_click, preprocess, postprocess, api_name, batch, example_labels, visible, _initiated_directly)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39msamples:\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m index, example \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnon_none_examples):\n\u001b[1;32m    293\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnon_none_processed_examples[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39msamples[index]] \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 294\u001b[0m             \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_processed_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexample\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    295\u001b[0m         )\n\u001b[1;32m    297\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache_examples \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlazy\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    298\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m    299\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWill cache examples in \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mutils\u001b[38;5;241m.\u001b[39mabspath(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcached_folder)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m directory at first use.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    300\u001b[0m         end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    301\u001b[0m     )\n",
+      "File \u001b[0;32m~/anaconda3/envs/grad/lib/python3.10/site-packages/gradio/helpers.py:328\u001b[0m, in \u001b[0;36mExamples._get_processed_example\u001b[0;34m(self, example)\u001b[0m\n\u001b[1;32m    324\u001b[0m sub \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m component, sample \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\n\u001b[1;32m    326\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minputs_with_examples, example, strict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    327\u001b[0m ):\n\u001b[0;32m--> 328\u001b[0m     prediction_value \u001b[38;5;241m=\u001b[39m \u001b[43mcomponent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpostprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43msample\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    329\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(prediction_value, (GradioRootModel, GradioModel)):\n\u001b[1;32m    330\u001b[0m         prediction_value \u001b[38;5;241m=\u001b[39m prediction_value\u001b[38;5;241m.\u001b[39mmodel_dump()\n",
+      "File \u001b[0;32m~/anaconda3/envs/grad/lib/python3.10/site-packages/gradio/components/file.py:223\u001b[0m, in \u001b[0;36mFile.postprocess\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ListFiles(\n\u001b[1;32m    210\u001b[0m         root\u001b[38;5;241m=\u001b[39m[\n\u001b[1;32m    211\u001b[0m             FileData(\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    217\u001b[0m         ]\n\u001b[1;32m    218\u001b[0m     )\n\u001b[1;32m    219\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m FileData(\n\u001b[1;32m    221\u001b[0m         path\u001b[38;5;241m=\u001b[39mvalue,\n\u001b[1;32m    222\u001b[0m         orig_name\u001b[38;5;241m=\u001b[39mPath(value)\u001b[38;5;241m.\u001b[39mname,\n\u001b[0;32m--> 223\u001b[0m         size\u001b[38;5;241m=\u001b[39m\u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mst_size,\n\u001b[1;32m    224\u001b[0m     )\n",
+      "File \u001b[0;32m~/anaconda3/envs/grad/lib/python3.10/pathlib.py:1097\u001b[0m, in \u001b[0;36mPath.stat\u001b[0;34m(self, follow_symlinks)\u001b[0m\n\u001b[1;32m   1092\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mstat\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m, follow_symlinks\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m   1093\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1094\u001b[0m \u001b[38;5;124;03m    Return the result of the stat() system call on this path, like\u001b[39;00m\n\u001b[1;32m   1095\u001b[0m \u001b[38;5;124;03m    os.stat() does.\u001b[39;00m\n\u001b[1;32m   1096\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1097\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_accessor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfollow_symlinks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_symlinks\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'sample_emails.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "demo = gr.Interface(\n",
+    "    fn=classify_text,\n",
+    "    inputs=gr.Textbox(label=\"Email/Message\", placeholder=\"Enter text here...\"),\n",
+    "    outputs=gr.Label(label=\"Classification Results\"),\n",
+    "    title=\"Spamedar\",\n",
+    "    description=\"Copy your email to find out if it's a is Spam or Ham.👇\",\n",
+    "    examples=[\n",
+    "        [\"Hey, can we meet tomorrow to discuss the project?\"],\n",
+    "        [\"WINNER! You've been selected for a $1000 Walmart Gift Card!\"],\n",
+    "        [\"Your account needs verification. Click here to confirm your details.\"]\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73be7578-bc18-4af7-8a00-52b6ee4b21e9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

requirements.txt

@@ -0,0 +1,4 @@
+gradio>=3.0
+transformers>=4.30
+torch>=2.0
+pandas>=1.0

sample_emails.csv

@@ -0,0 +1,6 @@
+email
+"Hi John, just wanted to follow up about tomorrow's meeting. Please confirm your attendance."
+Congratulations! You've won a free cruise vacation! Click here to claim your prize >>>
+Your Amazon order #456789 has shipped. Track your package: https://amzn.track/456789
+URGENT: Your bank account requires immediate verification. Login now to avoid suspension.
+Reminder: Team lunch today at 1 PM in the main conference room.

sample_emailsNoColumn.csv

@@ -0,0 +1,6 @@
+dfsfsd
+"Hi John, just wanted to follow up about tomorrow's meeting. Please confirm your attendance."
+Congratulations! You've won a free cruise vacation! Click here to claim your prize >>>
+Your Amazon order #456789 has shipped. Track your package: https://amzn.track/456789
+URGENT: Your bank account requires immediate verification. Login now to avoid suspension.
+Reminder: Team lunch today at 1 PM in the main conference room.