2022_08_05-Ha-Gradio.ipynb

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   "source": [
    "import gradio as gr\n",
    "from transformers import pipeline\n",
    "import ffmpeg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f6aaaa2",
   "metadata": {},
   "source": [
    "Gradio is an open-source Python library that is used to build machine learning and data science demos and web applications."
   ]
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      "Running on local URL:  http://127.0.0.1:7860/\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
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   "source": [
    "### Text generation\n",
    "gr.close_all()\n",
    "\n",
    "#Firstly, define a function\n",
    "def predict(prompt):\n",
    "    model = pipeline(\"text-generation\")\n",
    "    completion = model(prompt)[0][\"generated_text\"]\n",
    "    return completion\n",
    "\n",
    "#Secondly, create an 'interface'\n",
    "textbox = gr.Textbox(label=\"Type your sentence here:\", placeholder=\"Hi, my name is John Doe.\", lines=2)\n",
    "demo = gr.Interface(fn=predict, inputs=textbox, outputs=\"text\")\n",
    "\n",
    "#Then launch\n",
    "demo.launch(server_port=7860)"
   ]
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      "Running on local URL:  http://127.0.0.1:7862/\n",
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   "source": [
    "### Audio transcription\n",
    "#demo.close()\n",
    "#gr.close_all()\n",
    "\n",
    "#Firstly, define a funtion\n",
    "def transcribe_audio(mic=None, file=None):\n",
    "    model = pipeline(\"automatic-speech-recognition\")\n",
    "    if mic is not None:\n",
    "        audio = mic\n",
    "    elif file is not None:\n",
    "        audio = file\n",
    "    else:\n",
    "        return \"You must either provide a mic recording or a file\"\n",
    "    transcription = model(audio)[\"text\"]\n",
    "    return transcription\n",
    "\n",
    "#Secondly, create an 'interface'\n",
    "demo2 = gr.Interface(\n",
    "    fn=transcribe_audio,\n",
    "    inputs=[\n",
    "        gr.Audio(source=\"microphone\", type=\"filepath\"),\n",
    "        gr.Audio(source=\"upload\", type=\"filepath\"),\n",
    "    ],\n",
    "    outputs=\"text\",\n",
    ")\n",
    "\n",
    "#Then launch\n",
    "demo2.launch(server_port=7862)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ac1b30e",
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   "source": [
    "### Prediction of bioassay with Random Forest example\n",
    "# Input: Cell Painting features extracted by CellProfiler\n",
    "# Output: Activity (\"Active\", or \"Inactive\") of ChEMBL assay CHEMBL1741322\n",
    "# Assay description: Cytochrome panel assay. Compounds with AC50 equal or less than 10 uM are considered active\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "dataset = pd.read_csv(\"toy_dataset_Cytochrome.csv\")\n",
    "X_train, X_test, y_train, y_test = train_test_split(dataset.iloc[:,0:-1], dataset[\"CHEMBL1741322\"], test_size=0.1)\n",
    "\n",
    "clf = RandomForestClassifier(max_depth=2, random_state=0)\n",
    "clf.fit(X_train.values, y_train.values)\n",
    "\n",
    "np.savez(\"CP_predict.npz\", X=X_test.iloc[[1,11,67,109,222],:].values, y=y_test.iloc[[1,11,67,109,222]].values)"
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "id": "904c4890",
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     "name": "stdout",
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     "text": [
      "Running on local URL:  http://127.0.0.1:7864/\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
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       "<div><iframe src=\"http://127.0.0.1:7864/\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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     "text": [
      "No model was supplied, defaulted to gpt2 (https://huggingface.co/gpt2)\n",
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    }
   ],
   "source": [
    "#demo2.close()\n",
    "#gr.close_all()\n",
    "\n",
    "def predict(cp_profile):\n",
    "    features = np.load(cp_profile)[\"X\"]\n",
    "    true_label = np.load(cp_profile)[\"y\"]\n",
    "    pred = clf.predict(features)\n",
    "    pred = [\"Active\" if i==1 else \"Inactive\" for i in pred]\n",
    "    return pred#, true_label\n",
    "    \n",
    "textbox = gr.Textbox(label=\"Path to Cell Painting features here:\", lines=2, placeholder=\"CP_predict.npz\")\n",
    "demo3 = gr.Interface(fn=predict, inputs=textbox, outputs=\"text\")\n",
    "\n",
    "demo3.launch(server_port=7864)"
   ]
  }
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