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Update app.py
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app.py
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from flask import Flask, request, jsonify
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from huggingface_hub import HfApi
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app = Flask(__name__)
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api = HfApi()
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@app.route('/search_datasets', methods=['GET'])
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def search_datasets():
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@app.route('/run_inference', methods=['POST'])
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def run_inference():
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if __name__ == '__main__':
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app.run(debug=False)
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from flask import Flask, request, jsonify
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from huggingface_hub import HfApi
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from transformers import pipeline
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import logging
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app = Flask(__name__)
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api = HfApi()
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger()
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@app.route('/search_datasets', methods=['GET'])
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def search_datasets():
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try:
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query = request.args.get('query')
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if not query:
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logger.error("No query provided for dataset search.")
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return jsonify({"error": "No query parameter provided"}), 400
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logger.info(f"Searching datasets with query: {query}")
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datasets = api.list_datasets(search=query, full=True)
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return jsonify(datasets)
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except Exception as e:
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logger.error(f"Failed to search datasets: {str(e)}")
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return jsonify({"error": str(e)}), 500
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@app.route('/run_inference', methods=['POST'])
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def run_inference():
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try:
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model_id = request.json.get('model_id')
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inputs = request.json.get('inputs')
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if not model_id or not inputs:
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logger.error("Model ID or inputs missing in the request.")
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return jsonify({"error": "Model ID or inputs missing in the request"}), 400
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logger.info(f"Running inference using model: {model_id}")
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model_pipeline = pipeline(task="text-generation", model=model_id)
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results = model_pipeline(inputs)
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return jsonify(results)
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except Exception as e:
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logger.error(f"Failed to run inference: {str(e)}")
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return jsonify({"error": str(e)}), 500
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if __name__ == '__main__':
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app.run(debug=False)
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