Commit
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695f28c
1
Parent(s):
7c1d475
random seed
Browse files- llm_inference.py +8 -2
llm_inference.py
CHANGED
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@@ -1,4 +1,5 @@
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import os
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from groq import Groq
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from openai import OpenAI
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@@ -22,8 +23,6 @@ class LLMInferenceNode:
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"""
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Generates a prompt based on the provided seed, prompt type, and custom input.
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"""
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import random
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-
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random.seed(dynamic_seed)
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if custom_input and custom_input.strip():
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prompt = custom_input
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@@ -176,6 +175,10 @@ Your output is only the caption itself, no comments or extra formatting. The cap
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else:
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user_message = f"{base_prompt}\nDescription: {input_text}"
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# Select the appropriate provider
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if provider == "Hugging Face":
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response = self.huggingface_client.chat.completions.create(
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@@ -187,6 +190,7 @@ Your output is only the caption itself, no comments or extra formatting. The cap
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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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@@ -199,6 +203,7 @@ Your output is only the caption itself, no comments or extra formatting. The cap
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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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@@ -211,6 +216,7 @@ Your output is only the caption itself, no comments or extra formatting. The cap
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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 os
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import random # Import the random module
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from groq import Groq
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from openai import OpenAI
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"""
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Generates a prompt based on the provided seed, prompt type, and custom input.
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"""
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random.seed(dynamic_seed)
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if custom_input and custom_input.strip():
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prompt = custom_input
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else:
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user_message = f"{base_prompt}\nDescription: {input_text}"
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# Generate a random seed
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seed = random.randint(0, 10000)
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print(f"Generated seed: {seed}") # Debug print
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# Select the appropriate provider
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if provider == "Hugging Face":
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response = self.huggingface_client.chat.completions.create(
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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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seed=seed # Pass the seed parameter
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)
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output = response.choices[0].message.content.strip()
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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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seed=seed # Pass the seed parameter
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)
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output = response.choices[0].message.content.strip()
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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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seed=seed # Pass the seed parameter
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)
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output = response.choices[0].message.content.strip()
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