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Update text_generator.py
Browse files- text_generator.py +20 -3
text_generator.py
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@@ -9,21 +9,38 @@ class TextGenerationTool(Tool):
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inputs = ["text"]
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outputs = ["text"]
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def __call__(self, prompt: str):
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# Replace the following line with your text generation logic
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#generated_text = f"Generated text based on the prompt: '{prompt}'"
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# Initialize the text generation pipeline
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#text_generator = pipeline("text-generation") llama mistralai/Mistral-7B-Instruct-v0.1
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text_generator = pipeline(model="gpt2")
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#text_generator = pipeline(model="meta-llama/Llama-2-7b-chat-hf")
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# Generate text based on a prompt
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generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)
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# Print the generated text
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print(generated_text)
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inputs = ["text"]
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outputs = ["text"]
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import requests
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def __call__(self, prompt: str):
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API_URL = "https://api-inference.huggingface.co/models/lukasdrg/clinical_longformer_same_tokens_220k"
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headers = {"Authorization": "Bearer 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"}
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#def query(payload):
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generated_text = requests.post(API_URL, headers=headers, json=payload)
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# return response.json()
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#output = query({
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# "inputs": "The answer to the universe is <mask>.",
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#})
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# Replace the following line with your text generation logic
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#generated_text = f"Generated text based on the prompt: '{prompt}'"
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# Initialize the text generation pipeline
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#text_generator = pipeline("text-generation") llama mistralai/Mistral-7B-Instruct-v0.1
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#text_generator = pipeline(model="gpt2")
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#text_generator = pipeline(model="meta-llama/Llama-2-7b-chat-hf")
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# Generate text based on a prompt
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#generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)
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# Print the generated text
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#print(generated_text)
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