Youtube_Huggingface_Inference_API.ipynb

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        "<a href=\"https://colab.research.google.com/github/PradipNichite/Youtube-Tutorials/blob/main/Youtube_Huggingface_Inference_API.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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              "[[{'label': 'LABEL_0', 'score': 0.002866477705538273},\n",
              "  {'label': 'LABEL_1', 'score': 0.018881773576140404},\n",
              "  {'label': 'LABEL_2', 'score': 0.9782518148422241}]]"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import requests\n",
        "\n",
        "API_URL = \"https://api-inference.huggingface.co/models/cardiffnlp/twitter-roberta-base-sentiment\"\n",
        "headers = {\"Authorization\": \"Bearer hf_EriWcQYZIQNwIbfeXGfJslkA\"}\n",
        "\n",
        "def query(payload):\n",
        "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
        "\treturn response.json()\n",
        "\t\n",
        "output = query({\n",
        "\t\"inputs\": \"I like you. I love you\",\n",
        "})\n",
        "output"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output[0]"
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              "[{'label': 'LABEL_0', 'score': 0.002866477705538273},\n",
              " {'label': 'LABEL_1', 'score': 0.018881773576140404},\n",
              " {'label': 'LABEL_2', 'score': 0.9782518148422241}]"
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          "metadata": {},
          "execution_count": 2
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      "cell_type": "code",
      "source": [
        "sorted_output = sorted(output[0], key=lambda d: d['score'],reverse=True) \n",
        "sorted_output"
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              "[{'label': 'LABEL_2', 'score': 0.9782518148422241},\n",
              " {'label': 'LABEL_1', 'score': 0.018881773576140404},\n",
              " {'label': 'LABEL_0', 'score': 0.002866477705538273}]"
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          "metadata": {},
          "execution_count": 3
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      "cell_type": "code",
      "source": [
        "prediction = sorted_output[0]\n",
        "prediction"
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        "id": "GxO7pU4qRVvo",
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            "text/plain": [
              "{'label': 'LABEL_2', 'score': 0.9782518148422241}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "label_mapping = {\n",
        "    'LABEL_0':'Negative',\n",
        "    'LABEL_1':'Neutral',\n",
        "    'LABEL_2':'Positive'\n",
        "}"
      ],
      "metadata": {
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      "execution_count": 5,
      "outputs": []
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        "label_mapping[prediction['label']]"
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            "text/plain": [
              "'Positive'"
            ],
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          "execution_count": 6
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