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Runtime error
Update huggingface_inference_node.py
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huggingface_inference_node.py
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@@ -4,11 +4,9 @@ from datetime import datetime
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import anthropic
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from groq import Groq
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from openai import OpenAI
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huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
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groq_api_key = os.getenv("GROQ_API_KEY")
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sambanova_api_key = os.getenv("SAMBANOVA_API_KEY")
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class LLMInferenceNode:
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api_key=huggingface_token,
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)
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self.groq_client = Groq(api_key=groq_api_key)
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self.sambanova_client = OpenAI(
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api_key=sambanova_api_key,
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base_url="https://api.sambanova.ai/v1",
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)
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def generate(
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@@ -108,35 +103,6 @@ You are allowed to make up film and branding names, and do them like 80's, 90's
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output = response.choices[0].message.content.strip()
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elif provider == "OpenAI":
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openai_client = OpenAI(api_key=api_key)
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response = openai_client.chat.completions.create(
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model=model or "gpt-4",
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max_tokens=1024,
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temperature=0.7,
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messages=[
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{"role": "system", "content": system_message},
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{"role": "user", "content": user_message},
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],
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)
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output = response.choices[0].message.content.strip()
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elif provider == "Anthropic":
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anthropic_client = anthropic.Anthropic(api_key=api_key)
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response = anthropic_client.messages.create(
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model=model or "claude-3-5-sonnet-20240620",
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max_tokens=1024,
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temperature=0.7,
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system=system_message,
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messages=[
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{
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"role": "user",
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"content": [{"type": "text", "text": user_message}],
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}
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],
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)
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output = response.content[0].text
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elif provider == "Groq":
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response = self.groq_client.chat.completions.create(
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model=model or "llama-3.1-70b-versatile",
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@@ -148,15 +114,6 @@ You are allowed to make up film and branding names, and do them like 80's, 90's
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],
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)
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output = response.choices[0].message.content.strip()
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elif provider == "SambaNova":
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response = self.sambanova_client.chat.completions.create(
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model=model or "Meta-Llama-3.1-70B-Instruct",
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max_tokens=1024,
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temperature=0.7,
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messages=[
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{"role": "system", "content": system_message},
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{"role": "user", "content": user_message},
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],
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)
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output = response.choices[0].message.content.strip()
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import anthropic
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from groq import Groq
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huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
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groq_api_key = os.getenv("GROQ_API_KEY")
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class LLMInferenceNode:
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api_key=huggingface_token,
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)
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self.groq_client = Groq(api_key=groq_api_key)
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)
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def generate(
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output = response.choices[0].message.content.strip()
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elif provider == "Groq":
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response = self.groq_client.chat.completions.create(
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model=model or "llama-3.1-70b-versatile",
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],
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)
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output = response.choices[0].message.content.strip()
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],
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)
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output = response.choices[0].message.content.strip()
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