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Phase-R_Diet

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n1hal commited on Sep 10, 2024

Commit

14a71e2

verified ·

1 Parent(s): f571b9c

Upload folder using huggingface_hub

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.gitattributes +1 -0
.github/workflows/update_space.yml +28 -0
README.md +2 -8
__pycache__/diet.cpython-39.pyc +0 -0
__pycache__/ollama.cpython-39.pyc +0 -0
__pycache__/ollama_diet.cpython-39.pyc +0 -0
custom_model.ipynb +168 -0
dataset.csv +3 -0
diet.py +32 -0
nutritionmodel.py +285 -0
prompt.txt +1 -0
response.json +55 -0

.gitattributes CHANGED Viewed

@@ -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

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+dataset.csv filter=lfs diff=lfs merge=lfs -text

.github/workflows/update_space.yml ADDED Viewed

	@@ -0,0 +1,28 @@

+name: Run Python script
+on:
+  push:
+    branches:
+      - main
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+    - name: Install Gradio
+      run: python -m pip install gradio
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+    - name: Deploy to Spaces
+      run: gradio deploy

README.md CHANGED Viewed

@@ -1,12 +1,6 @@
 ---
-title: Phase-R Diet
-emoji: 🏢
-colorFrom: red
-colorTo: green
 sdk: gradio
 sdk_version: 4.43.0
-app_file: app.py
-pinned: false
 ---
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

 ---
+title: Phase-R_Diet
+app_file: diet.py
 sdk: gradio
 sdk_version: 4.43.0
 ---

__pycache__/diet.cpython-39.pyc ADDED Viewed

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__pycache__/ollama.cpython-39.pyc ADDED Viewed

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__pycache__/ollama_diet.cpython-39.pyc ADDED Viewed

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custom_model.ipynb ADDED Viewed

	@@ -0,0 +1,168 @@

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting unsloth@ git+https://github.com/unslothai/unsloth.git (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n",
+      "  Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-install-iyfa2a6n/unsloth_fb5a1c9f71c749ed87cb3c95c10067c7\n",
+      "  Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-install-iyfa2a6n/unsloth_fb5a1c9f71c749ed87cb3c95c10067c7\n",
+      "  Resolved https://github.com/unslothai/unsloth.git to commit 976d11a10d54383aeb7a692c69e01151a20bfd72\n",
+      "  Installing build dependencies ... \u001b[?25ldone\n",
+      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
+      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
+      "\u001b[?25hRequirement already satisfied: packaging in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (24.1)\n",
+      "Requirement already satisfied: tyro in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.8.10)\n",
+      "Requirement already satisfied: transformers>=4.43.2 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (4.43.3)\n",
+      "Requirement already satisfied: datasets>=2.16.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2.20.0)\n",
+      "Requirement already satisfied: sentencepiece>=0.2.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.2.0)\n",
+      "Requirement already satisfied: tqdm in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (4.66.4)\n",
+      "Requirement already satisfied: psutil in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (6.0.0)\n",
+      "Requirement already satisfied: wheel>=0.42.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.43.0)\n",
+      "Requirement already satisfied: numpy in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (1.23.5)\n",
+      "Requirement already satisfied: protobuf<4.0.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.20.3)\n",
+      "Requirement already satisfied: huggingface-hub in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.24.3)\n",
+      "Requirement already satisfied: hf-transfer in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.1.8)\n",
+      "Requirement already satisfied: filelock in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.15.4)\n",
+      "Requirement already satisfied: pyarrow>=15.0.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (17.0.0)\n",
+      "Requirement already satisfied: pyarrow-hotfix in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.6)\n",
+      "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.3.8)\n",
+      "Requirement already satisfied: pandas in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2.2.2)\n",
+      "Requirement already satisfied: requests>=2.32.2 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2.32.3)\n",
+      "Requirement already satisfied: xxhash in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.4.1)\n",
+      "Requirement already satisfied: multiprocess in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.70.16)\n",
+      "Requirement already satisfied: fsspec<=2024.5.0,>=2023.1.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from fsspec[http]<=2024.5.0,>=2023.1.0->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2024.5.0)\n",
+      "Requirement already satisfied: aiohttp in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.9.5)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (6.0.1)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from huggingface-hub->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (4.12.2)\n",
+      "Requirement already satisfied: regex!=2019.12.17 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers>=4.43.2->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2024.7.24)\n",
+      "Requirement already satisfied: safetensors>=0.4.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers>=4.43.2->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.4.3)\n",
+      "Requirement already satisfied: tokenizers<0.20,>=0.19 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from transformers>=4.43.2->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.19.1)\n",
+      "Requirement already satisfied: docstring-parser>=0.16 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.16)\n",
+      "Requirement already satisfied: rich>=11.1.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (13.7.1)\n",
+      "Requirement already satisfied: shtab>=1.5.6 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (1.7.1)\n",
+      "Requirement already satisfied: eval-type-backport>=0.1.3 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.2.0)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (1.2.0)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (23.1.0)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (1.4.0)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (6.0.4)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (1.9.3)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from aiohttp->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (4.0.3)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.7)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2.2.2)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2024.7.4)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from rich>=11.1.0->tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.0.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from rich>=11.1.0->tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2.18.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from pandas->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2.9.0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from pandas->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2024.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from pandas->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2024.1)\n",
+      "Requirement already satisfied: mdurl~=0.1 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.1.2)\n",
+      "Requirement already satisfied: six>=1.5 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas->datasets>=2.16.0->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (1.16.0)\n",
+      "Requirement already satisfied: xformers<0.0.27 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (0.0.26.post1)\n",
+      "Requirement already satisfied: trl<0.9.0 in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (0.8.6)\n",
+      "Requirement already satisfied: peft in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (0.12.0)\n",
+      "Requirement already satisfied: accelerate in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (0.33.0)\n",
+      "Requirement already satisfied: bitsandbytes in /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages (0.43.3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
+    "!pip install --no-deps \"xformers<0.0.27\" \"trl<0.9.0\" peft accelerate bitsandbytes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-09-02 22:16:32.351723: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2024-09-02 22:16:32.441229: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2024-09-02 22:16:32.454471: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-09-02 22:16:32.544617: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2024-09-02 22:16:33.579642: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
+      "WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n",
+      "    PyTorch 2.3.0+cu121 with CUDA 1201 (you have 2.4.0+cu121)\n",
+      "    Python  3.9.19 (you have 3.9.19)\n",
+      "  Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n",
+      "  Memory-efficient attention, SwiGLU, sparse and more won't be available.\n",
+      "  Set XFORMERS_MORE_DETAILS=1 for more details\n"
+     ]
+    },
+    {
+     "ename": "ImportError",
+     "evalue": "Unsloth: Xformers was not installed correctly.\nPlease install xformers separately first.\nThen confirm if it's correctly installed by running:\npython -m xformers.info\n\nLonger error message:\nxFormers can't load C++/CUDA extensions. xFormers was built for:\n    PyTorch 2.3.0+cu121 with CUDA 1201 (you have 2.4.0+cu121)\n    Python  3.9.19 (you have 3.9.19)\n  Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n  Memory-efficient attention, SwiGLU, sparse and more won't be available.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "File \u001b[0;32m~/miniconda3/envs/tf-gpu/lib/python3.9/site-packages/xformers/_cpp_lib.py:128\u001b[0m, in \u001b[0;36m_register_extensions\u001b[0;34m()\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 128\u001b[0m     \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_library\u001b[49m\u001b[43m(\u001b[49m\u001b[43mext_specs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43morigin\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n",
+      "File \u001b[0;32m~/miniconda3/envs/tf-gpu/lib/python3.9/site-packages/torch/_ops.py:1295\u001b[0m, in \u001b[0;36m_Ops.load_library\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m   1291\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m dl_open_guard():\n\u001b[1;32m   1292\u001b[0m     \u001b[38;5;66;03m# Import the shared library into the process, thus running its\u001b[39;00m\n\u001b[1;32m   1293\u001b[0m     \u001b[38;5;66;03m# static (global) initialization code in order to register custom\u001b[39;00m\n\u001b[1;32m   1294\u001b[0m     \u001b[38;5;66;03m# operators with the JIT.\u001b[39;00m\n\u001b[0;32m-> 1295\u001b[0m     \u001b[43mctypes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCDLL\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1296\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloaded_libraries\u001b[38;5;241m.\u001b[39madd(path)\n",
+      "File \u001b[0;32m~/miniconda3/envs/tf-gpu/lib/python3.9/ctypes/__init__.py:382\u001b[0m, in \u001b[0;36mCDLL.__init__\u001b[0;34m(self, name, mode, handle, use_errno, use_last_error, winmode)\u001b[0m\n\u001b[1;32m    381\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m handle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 382\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle \u001b[38;5;241m=\u001b[39m \u001b[43m_dlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    383\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "\u001b[0;31mOSError\u001b[0m: /home/nihaltm/miniconda3/envs/tf-gpu/lib/python3.9/site-packages/xformers/_C.so: undefined symbol: _ZN3c1010Dispatcher17runRecordFunctionERN2at14RecordFunctionESt17reference_wrapperIKNS_14FunctionSchemaEENS_11DispatchKeyE",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mxFormersInvalidLibException\u001b[0m               Traceback (most recent call last)",
+      "File \u001b[0;32m~/miniconda3/envs/tf-gpu/lib/python3.9/site-packages/unsloth/models/_utils.py:253\u001b[0m\n\u001b[1;32m    252\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 253\u001b[0m     \u001b[43m_register_extensions\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Check if C++ modules are loaded correctly\u001b[39;00m\n\u001b[1;32m    254\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m error:\n",
+      "File \u001b[0;32m~/miniconda3/envs/tf-gpu/lib/python3.9/site-packages/xformers/_cpp_lib.py:130\u001b[0m, in \u001b[0;36m_register_extensions\u001b[0;34m()\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m--> 130\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m xFormersInvalidLibException(build_metadata) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m build_metadata\n",
+      "\u001b[0;31mxFormersInvalidLibException\u001b[0m: xFormers can't load C++/CUDA extensions. xFormers was built for:\n    PyTorch 2.3.0+cu121 with CUDA 1201 (you have 2.4.0+cu121)\n    Python  3.9.19 (you have 3.9.19)\n  Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n  Memory-efficient attention, SwiGLU, sparse and more won't be available.",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_dataset\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TrainingArguments, TextStreamer\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01munsloth\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchat_templates\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_chat_template\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01munsloth\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastLanguageModel, is_bfloat16_supported\n",
+      "File \u001b[0;32m~/miniconda3/envs/tf-gpu/lib/python3.9/site-packages/unsloth/__init__.py:154\u001b[0m\n\u001b[1;32m    144\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    145\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnsloth: CUDA is not linked properly.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\\\n\u001b[1;32m    146\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry running `python -m bitsandbytes` then `python -m xformers.info`\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\\\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    150\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnsloth will still run for now, but maybe it might crash - let\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms hope it works!\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    151\u001b[0m         )\n\u001b[1;32m    152\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 154\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m    155\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msave\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m    156\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchat_templates\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n",
+      "File \u001b[0;32m~/miniconda3/envs/tf-gpu/lib/python3.9/site-packages/unsloth/models/__init__.py:15\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# See the License for the specific language governing permissions and\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mloader\u001b[39;00m  \u001b[38;5;28;01mimport\u001b[39;00m FastLanguageModel\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mllama\u001b[39;00m   \u001b[38;5;28;01mimport\u001b[39;00m FastLlamaModel\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmistral\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastMistralModel\n",
+      "File \u001b[0;32m~/miniconda3/envs/tf-gpu/lib/python3.9/site-packages/unsloth/models/loader.py:15\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# See the License for the specific language governing permissions and\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_bfloat16_supported, HAS_FLASH_ATTENTION, HAS_FLASH_ATTENTION_SOFTCAPPING\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mllama\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastLlamaModel, logger\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmistral\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastMistralModel\n",
+      "File \u001b[0;32m~/miniconda3/envs/tf-gpu/lib/python3.9/site-packages/unsloth/models/_utils.py:255\u001b[0m\n\u001b[1;32m    253\u001b[0m     _register_extensions() \u001b[38;5;66;03m# Check if C++ modules are loaded correctly\u001b[39;00m\n\u001b[1;32m    254\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[0;32m--> 255\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[1;32m    256\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnsloth: Xformers was not installed correctly.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\\\n\u001b[1;32m    257\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease install xformers separately first.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\\\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThen confirm if it\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms correctly installed by running:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\\\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython -m xformers.info\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLonger error message:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(error)\n\u001b[1;32m    261\u001b[0m     )\n\u001b[1;32m    262\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    263\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mxformers\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfmha\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mxformers\u001b[39;00m\n",
+      "\u001b[0;31mImportError\u001b[0m: Unsloth: Xformers was not installed correctly.\nPlease install xformers separately first.\nThen confirm if it's correctly installed by running:\npython -m xformers.info\n\nLonger error message:\nxFormers can't load C++/CUDA extensions. xFormers was built for:\n    PyTorch 2.3.0+cu121 with CUDA 1201 (you have 2.4.0+cu121)\n    Python  3.9.19 (you have 3.9.19)\n  Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n  Memory-efficient attention, SwiGLU, sparse and more won't be available."
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from trl import SFTTrainer\n",
+    "from datasets import load_dataset\n",
+    "from transformers import TrainingArguments, TextStreamer\n",
+    "from unsloth.chat_templates import get_chat_template\n",
+    "from unsloth import FastLanguageModel, is_bfloat16_supported"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tf-gpu",
+   "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": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

dataset.csv ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:1e11c527e8b59cc589ddbf83ae39268b3a8d161107bf462af7c1d59d4dbc090c
+size 300394762

diet.py ADDED Viewed

	@@ -0,0 +1,32 @@

+import gradio as gr
+import asyncio
+import ollama
+import json
+def chat(message):
+    messages = [{"role": "user", "content": message}]
+    response = ollama.chat(model='llama3', messages=messages, stream=True)
+    for chunk in response:
+        yield chunk['message']['content']
+def gradio_chat(message, history):
+    response = ""
+    for chunk in chat(message):
+        response += chunk
+        yield response
+iface = gr.ChatInterface(
+    fn=gradio_chat,
+    title="Diet Plan Generator",
+    description="Enter your preferences to generate a personalized diet plan.",
+    examples=[
+        "Generate a meal plan for weight loss with a daily activity level being no activity or very less exercise. Target calories: 2000 kcal. Macro Distribution: 80 g protein, 220 g of carbs and 32 g of fat. The foods should mainly belong to Indian cuisine and should be strictly vegetarian.",
+        "Create a high-protein diet plan for muscle gain. Target calories: 2500 kcal. Macro Distribution: 150 g protein, 250 g of carbs and 70 g of fat. Include Mediterranean cuisine options.",
+    ],
+    retry_btn=None,
+    undo_btn="Delete Previous",
+    clear_btn="Clear",
+)
+if __name__ == "__main__":
+    iface.launch(share = True)

nutritionmodel.py ADDED Viewed

	@@ -0,0 +1,285 @@

+# import numpy as np
+# import pandas as pd
+# from sklearn.model_selection import train_test_split
+# from sklearn.neural_network import MLPRegressor
+# from sklearn.preprocessing import StandardScaler
+# # Step 1: Create random food data
+# def create_random_food_data(num_samples=100):
+#     np.random.seed(42)
+#     areas = ['American', 'Mexican', 'Italian', 'Indian', 'Chinese']
+#     categories = ['Beef', 'Chicken', 'Vegetarian', 'Seafood', 'Pork']
+#     vegetarian_categories = ['Vegetarian']
+#     data = []
+#     for _ in range(num_samples):
+#         area = np.random.choice(areas)
+#         category = np.random.choice(categories)
+#         ingredients_count = np.random.randint(3, 10)
+#         calories = np.random.randint(200, 600)
+#         protein = np.random.randint(10, 40)
+#         carbs = np.random.randint(20, 70)
+#         fats = np.random.randint(5, 30)
+#         data.append([area, category, ingredients_count, calories, protein, carbs, fats])
+#     df = pd.DataFrame(data, columns=['Area', 'Category', 'IngredientsCount', 'Calories', 'Protein', 'Carbs', 'Fats'])
+#     return df
+# # Step 2: Preprocess the Data
+# def preprocess_data(df):
+#     features = df[['Area', 'Category', 'IngredientsCount']]
+#     targets = df[['Calories', 'Protein', 'Carbs', 'Fats']]
+#     # Encode categorical variables
+#     features = pd.get_dummies(features, columns=['Area', 'Category'])
+#     return features, targets
+# # Step 3: Train the MLP Model
+# def train_mlp_model(X, y):
+#     # Split data into training and test sets
+#     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+#     # Standardize the features
+#     scaler = StandardScaler()
+#     X_train_scaled = scaler.fit_transform(X_train)
+#     X_test_scaled = scaler.transform(X_test)
+#     # Initialize and train the MLP model
+#     mlp_model = MLPRegressor(hidden_layer_sizes=(100, 50), max_iter=500, random_state=42)
+#     mlp_model.fit(X_train_scaled, y_train)
+#     return mlp_model, scaler
+# # Step 4: Generate Diet Plan
+# def generate_diet_plan(mlp_model, scaler, total_calories, num_meals, region, diet_preference, foods_df):
+#     meal_names = ['Breakfast', 'Morning Snack', 'Lunch', 'Afternoon Snack', 'Dinner']
+#     calorie_distribution = [0.25, 0.10, 0.35, 0.10, 0.20]
+#     # Adjust the distribution if number of meals is less than 5
+#     if num_meals < 5:
+#         calorie_distribution = calorie_distribution[:num_meals]
+#         calorie_distribution = [x / sum(calorie_distribution) for x in calorie_distribution]
+#     elif num_meals > 5:
+#         # Evenly distribute the remaining calories across the extra meals
+#         extra_meals = num_meals - 5
+#         extra_meal_calories = (sum(calorie_distribution) - 1) / extra_meals
+#         calorie_distribution.extend([extra_meal_calories] * extra_meals)
+#         calorie_distribution = [x / sum(calorie_distribution) for x in calorie_distribution]
+#         meal_names.extend([f'Extra Meal {i+1}' for i in range(extra_meals)])
+#     diet_plan = []
+#     # Filter foods based on the user's region and diet preference
+#     if diet_preference == 'Vegetarian':
+#         region_foods = foods_df[(foods_df['Area'] == region) & (foods_df['Category'] == 'Vegetarian')]
+#     else:
+#         region_foods = foods_df[foods_df['Area'] == region]
+#     for i in range(num_meals):
+#         # Randomly select a food from the filtered region and preference
+#         food = region_foods.sample(1).iloc[0]
+#         # Adjust the portion to meet the meal calorie requirement
+#         portion_factor = (total_calories * calorie_distribution[i]) / food['Calories']
+#         diet_plan.append({
+#             'Meal': meal_names[i % len(meal_names)],
+#             'Food': food['Category'],
+#             'Area': food['Area'],
+#             'IngredientsCount': food['IngredientsCount'],
+#             'Calories': food['Calories'] * portion_factor,
+#             'Protein': food['Protein'] * portion_factor,
+#             'Carbs': food['Carbs'] * portion_factor,
+#             'Fats': food['Fats'] * portion_factor
+#         })
+#     return diet_plan
+# # Main Function
+# if __name__ == "__main__":
+#     # Create random food data
+#     foods_df = create_random_food_data()
+#     # Preprocess the data
+#     X, y = preprocess_data(foods_df)
+#     # Train the MLP model
+#     mlp_model, scaler = train_mlp_model(X, y)
+#     # Get user input
+#     total_calories = float(input("Enter the total daily calories you want to consume: "))
+#     num_meals = int(input("Enter the number of meals per day: "))
+#     region = input("Enter your region (American, Mexican, Italian, Indian, Chinese): ")
+#     diet_preference = input("Enter your diet preference (Vegetarian, Non-Vegetarian): ")
+#     # Generate and print the diet plan
+#     diet_plan = generate_diet_plan(mlp_model, scaler, total_calories, num_meals, region, diet_preference, foods_df)
+#     for meal in diet_plan:
+#         print(f"{meal['Meal']}: {meal['Food']} ({meal['Area']}) - {meal['Calories']:.2f} kcal, "
+#               f"{meal['Protein']:.2f}g protein, {meal['Carbs']:.2f}g carbs, {meal['Fats']:.2f}g fats")
+import requests
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
+# Step 1: Fetch Food Data from TheMealDB API
+def fetch_mealdb_data():
+    meals = []
+    for letter in 'abcdefghijklmnopqrstuvwxyz':
+        url = f'https://www.themealdb.com/api/json/v1/1/search.php?f={letter}'
+        response = requests.get(url)
+        data = response.json()
+        if data['meals']:
+            meals.extend(data['meals'])
+    return meals
+# Step 2: Preprocess the Data
+# def preprocess_data(meals):
+#     features = []
+#     targets = []
+#     for meal in meals:
+#         area = meal['strArea'] if meal['strArea'] else 'Unknown'
+#         category = meal['strCategory'] if meal['strCategory'] else 'Unknown'
+#         meal_type = np.random.choice(['Breakfast', 'Lunch', 'Dinner', 'Snack'])
+#         ingredients_count = sum([1 for i in range(1, 21) if meal[f'strIngredient{i}']])
+#         # Example target values (you can replace these with real data)
+#         calories = np.random.randint(200, 600)
+#         protein = np.random.randint(10, 40)
+#         carbs = np.random.randint(20, 70)
+#         fats = np.random.randint(5, 30)
+#         features.append([area, category, meal_type, ingredients_count])
+#         targets.append([calories, protein, carbs, fats])
+#     feature_df = pd.DataFrame(features, columns=['Area', 'Category', 'MealType', 'IngredientsCount'])
+#     target_df = pd.DataFrame(targets, columns=['Calories', 'Protein', 'Carbs', 'Fats'])
+#     # Encode categorical variables
+#     feature_df = pd.get_dummies(feature_df, columns=['Area', 'Category', 'MealType'])
+#     return feature_df, target_df
+def preprocess_data(meals):
+    features = []
+    targets = []
+    for meal in meals:
+        area = meal['strArea'] if meal['strArea'] else 'Unknown'
+        category = meal['strCategory'] if meal['strCategory'] else 'Unknown'
+        meal_type = np.random.choice(['Breakfast', 'Lunch', 'Dinner', 'Snack'])
+        ingredients_count = sum([1 for i in range(1, 21) if meal[f'strIngredient{i}']])
+        meal_name = meal['strMeal'] if meal['strMeal'] else 'Unknown'  # Add meal name
+        # Example target values (replace with real data if available)
+        calories = np.random.randint(200, 600)
+        protein = np.random.randint(10, 40)
+        carbs = np.random.randint(20, 70)
+        fats = np.random.randint(5, 30)
+        # Include meal_name in features
+        features.append([meal_name, area, category, meal_type, ingredients_count])
+        targets.append([calories, protein, carbs, fats])
+    feature_df = pd.DataFrame(features, columns=['MealName', 'Area', 'Category', 'MealType', 'IngredientsCount'])
+    target_df = pd.DataFrame(targets, columns=['Calories', 'Protein', 'Carbs', 'Fats'])
+    # Encode categorical variables except MealName
+    feature_df = pd.get_dummies(feature_df, columns=['Area', 'Category', 'MealType'])
+    return feature_df, target_df
+# Step 3: Train the MLP Model
+def train_mlp_model(X, y):
+    numeric_X = X.select_dtypes(include=[np.number])
+    X_train, X_test, y_train, y_test = train_test_split(numeric_X, y, test_size=0.2, random_state=42)
+    scaler = StandardScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+    mlp_model = MLPRegressor(hidden_layer_sizes=(150, 100, 50), activation='relu', solver='adam', max_iter=1000, random_state=42)
+    mlp_model.fit(X_train_scaled, y_train)
+    y_pred = mlp_model.predict(X_test_scaled)
+    mse = mean_squared_error(y_test, y_pred)
+    r2 = r2_score(y_test, y_pred)
+    mae = mean_absolute_error(y_test, y_pred)
+    print(f"Model Performance:")
+    print(f"Mean Squared Error (MSE): {mse:.2f}")
+    print(f"R-squared (R²): {r2:.2f}")
+    print(f"Mean Absolute Error (MAE): {mae:.2f}")
+    return mlp_model, scaler
+# Step 4: Generate Diet Plan
+def generate_diet_plan(mlp_model, scaler, total_calories, num_meals, region, diet_preference, foods_df):
+    meal_names = ['Breakfast', 'Morning Snack', 'Lunch', 'Afternoon Snack', 'Dinner']
+    calorie_distribution = [0.25, 0.10, 0.35, 0.10, 0.20]
+    if num_meals < 5:
+        calorie_distribution = calorie_distribution[:num_meals]
+        calorie_distribution = [x / sum(calorie_distribution) for x in calorie_distribution]
+    elif num_meals > 5:
+        extra_meals = num_meals - 5
+        extra_meal_calories = (sum(calorie_distribution) - 1) / extra_meals
+        calorie_distribution.extend([extra_meal_calories] * extra_meals)
+        calorie_distribution = [x / sum(calorie_distribution) for x in calorie_distribution]
+        meal_names.extend([f'Extra Meal {i+1}' for i in range(extra_meals)])
+    diet_plan = []
+    if diet_preference == 'Vegetarian':
+        region_foods = foods_df[(foods_df['Area'] == region) & (foods_df['Category'] == 'Vegetarian')]
+    else:
+        region_foods = foods_df[foods_df['Area'] == region]
+    for i in range(num_meals):
+        meal_type = meal_names[i % len(meal_names)]
+        meal_foods = region_foods[region_foods['MealType'] == meal_type]
+        if meal_foods.empty:
+            continue
+        food = meal_foods.sample(1).iloc[0]
+        portion_factor = (total_calories * calorie_distribution[i]) / food['Calories']
+        diet_plan.append({
+            'Meal': meal_type,
+            'Food': food['Category'],
+            'Area': food['Area'],
+            'IngredientsCount': food['IngredientsCount'],
+            'Calories': food['Calories'] * portion_factor,
+            'Protein': food['Protein'] * portion_factor,
+            'Carbs': food['Carbs'] * portion_factor,
+            'Fats': food['Fats'] * portion_factor
+        })
+    return diet_plan
+# Main Function
+if __name__ == "__main__":
+    meals = fetch_mealdb_data()
+    X, y = preprocess_data(meals)
+    mlp_model, scaler = train_mlp_model(X, y)
+    total_calories = float(input("Enter the total daily calories you want to consume: "))
+    num_meals = int(input("Enter the number of meals per day: "))
+    region = input("Enter your region (American, Mexican, Italian, Indian, Chinese): ")
+    diet_preference = input("Enter your diet preference (Vegetarian, Non-Vegetarian): ")
+    diet_plan = generate_diet_plan(mlp_model, scaler, total_calories, num_meals, region, diet_preference, X)
+    for meal in diet_plan:
+        print(f"{meal['Meal']}: {meal['Food']} ({meal['Area']}) - {meal['Calories']:.2f} kcal, "
+              f"{meal['Protein']:.2f}g protein, {meal['Carbs']:.2f}g carbs, {meal['Fats']:.2f}g fats")

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+ Generate a meal plan for weight loss with a daily activity level being no activity or very less exercise. Target calories: 2000 kcal. Macro Distribution: 80 g protein, 220 g of carbs and 32 g of fat. Give this in the form a table with days: [Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday] with meals being mandatorily [Breakfast, Afternoon Snack, Lunch, Evening Snack, Dinner]. Don't tell anything other than the table in the form of json as mentioned. The foods should mainly belong to Indian cuisine and should be strictly vegetarian.

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+Here is a sample meal plan for weight loss with a daily activity level being no activity or very less exercise:
+{
+"Monday": {
+"Breakfast": "Oatmeal with almond milk, banana, and walnuts (400 calories, 25g protein, 60g carbs, 10g fat)",
+"Afternoon Snack": "Carrot sticks with hummus (100 calories, 5g protein, 20g carbs, 10g fat)",
+"Lunch": "Vegetable biryani with tofu and a side of raita (500 calories, 30g protein, 70g carbs, 15g fat)",
+"Evening Snack": "Cucumber slices with tomato soup (150 calories, 5g protein, 25g carbs, 10g fat)",
+"Dinner": "Spinach and paneer stuffed bell peppers (550 calories, 35g protein, 40g carbs, 20g fat)"
+},
+"Tuesday": {
+"Breakfast": "Whole wheat toast with avocado spread, cherry tomatoes, and scrambled eggs (350 calories, 20g protein, 30g carbs, 15g fat)",
+"Afternoon Snack": "Roasted chana chaat with a sprinkle of cumin powder (120 calories, 5g protein, 25g carbs, 10g fat)",
+"Lunch": "Lentil soup with whole wheat roti and a side salad (450 calories, 20g protein, 60g carbs, 15g fat)",
+"Evening Snack": "Grilled vegetable skewers with a dollop of low-fat yogurt (200 calories, 10g protein, 30g carbs, 10g fat)",
+"Dinner": "Chana masala with brown rice and mixed vegetables (500 calories, 25g protein, 70g carbs, 20g fat)"
+},
+"Wednesday": {
+"Breakfast": "Flaxseed porridge with almond milk, banana, and a sprinkle of cinnamon (350 calories, 10g protein, 50g carbs, 15g fat)",
+"Afternoon Snack": "Cauliflower florets with a side of low-fat curd (100 calories, 5g protein, 20g carbs, 10g fat)",
+"Lunch": "Vegetable quinoa bowl with roasted sweet potatoes and a dollop of yogurt (500 calories, 25g protein, 60g carbs, 15g fat)",
+"Evening Snack": "Baked apple slices with cinnamon and a sprinkle of almonds (150 calories, 5g protein, 30g carbs, 10g fat)",
+"Dinner": "Palak paneer with brown rice and mixed vegetables (550 calories, 35g protein, 40g carbs, 20g fat)"
+},
+"Thursday": {
+"Breakfast": "Omelette with spinach, mushrooms, and whole wheat toast (250 calories, 15g protein, 10g carbs, 10g fat)",
+"Afternoon Snack": "Hummus with carrot and cucumber sticks (150 calories, 5g protein, 20g carbs, 10g fat)",
+"Lunch": "Chickpea salad with mixed greens, cherry tomatoes, and a side of whole wheat crackers (450 calories, 20g protein, 60g carbs, 15g fat)",
+"Evening Snack": "Roasted bell pepper slices with hummus (120 calories, 5g protein, 25g carbs, 10g fat)",
+"Dinner": "Vegetable korma with brown rice and mixed vegetables (500 calories, 30g protein, 70g carbs, 20g fat)"
+},
+"Friday": {
+"Breakfast": "Whole wheat paratha with low-fat paneer and a side of mixed greens salad (350 calories, 20g protein, 30g carbs, 15g fat)",
+"Afternoon Snack": "Cucumber slices with a sprinkle of cumin powder (50 calories, 5g protein, 10g carbs, 0g fat)",
+"Lunch": "Lentil and vegetable curry with brown rice and a side salad (500 calories, 25g protein, 70g carbs, 20g fat)",
+"Evening Snack": "Grilled vegetable skewers with a dollop of low-fat yogurt (200 calories, 10g protein, 30g carbs, 10g fat)",
+"Dinner": "Chana masala with brown rice and mixed vegetables (500 calories, 25g protein, 70g carbs, 20g fat)"
+},
+"Saturday": {
+"Breakfast": "Avocado toast on whole wheat bread with scrambled eggs and cherry tomatoes (350 calories, 15g protein, 30g carbs, 15g fat)",
+"Afternoon Snack": "Roasted chana chaat with a sprinkle of cumin powder (120 calories, 5g protein, 25g carbs, 10g fat)",
+"Lunch": "Vegetable biryani with tofu and a side of mixed greens salad (500 calories, 30g protein, 70g carbs, 20g fat)",
+"Evening Snack": "Baked apple slices with cinnamon and a sprinkle of almonds (150 calories, 5g protein, 30g carbs, 10g fat)",
+"Dinner": "Spinach and paneer stuffed bell peppers (550 calories, 35g protein, 40g carbs, 20g fat)"
+},
+"Sunday": {
+"Breakfast": "Oatmeal with almond milk, banana, and walnuts (400 calories, 25g protein, 60g carbs, 10g fat)",
+"Afternoon Snack": "Carrot sticks with hummus (100 calories, 5g protein, 20g carbs, 10g fat)",
+"Lunch": "Chickpea salad with mixed greens, cherry tomatoes, and a side of whole wheat crackers (450 calories, 20g protein, 60g carbs, 15g fat)",
+"Evening Snack": "Grilled vegetable skewers with a dollop of low-fat yogurt (200 calories, 10g protein, 30g carbs, 10g fat)",
+"Dinner": "Vegetable korma with brown rice and mixed vegetables (500 calories, 30g protein, 70g carbs, 20g fat)"
+}
+}
+Please note that the calorie, protein, carbohydrate, and fat content of each meal is approximate and may vary based on specific ingredients and portion sizes. It's always a good idea to consult with a registered dietitian or healthcare professional for personalized nutrition advice.