55 lines
2 KiB
Python
55 lines
2 KiB
Python
import requests
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import json
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from transformers import AutoTokenizer, LlamaForCausalLM
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class API:
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# This method processes a message via transformers. (NOT FINISHED!)
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@staticmethod
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def process_text_transformers(prompt, model):
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model = LlamaForCausalLM.from_pretrained(model)
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tokenizer = AutoTokenizer.from_pretrained(model)
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inputs = tokenizer(prompt, return_tensors="pt")
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generate_ids = model.generate(inputs.input_ids, max_length=30)
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return tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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# This method processes a message via ollama
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@staticmethod
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def process_text_local(prompt, model, system):
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ollama_url = "http://localhost:11434"
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response = requests.post(
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f"{ollama_url}/api/generate", json={"model": model, "prompt": prompt, "system": system}
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)
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if response.status_code == 200:
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response_data = []
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for line in response.iter_lines():
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line_decoded = line.decode("utf-8")
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line_raw = json.loads(line_decoded)
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response_data.append(line_raw["response"])
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final_response = "".join(response_data)
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return final_response
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else:
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return "Error: " + response.text
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# This method sends a message to a certain AI.
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def send_message(self, message, model, system):
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if model == 1:
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answer = self.process_text_local(message, "phi3.5", system)
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elif model == 2:
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answer = self.process_text_local(message, "gemma2:2b", system)
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elif model == 3:
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answer = self.process_text_local(message, "qwen2:0.5b", system)
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elif model == 4:
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answer = self.process_text_local(message, "codegemma:2b", system)
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elif model == 5:
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answer = self.process_text_transformers(message, "meta-llama/Meta-Llama-3.1-8B")
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else:
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return "Invalid choice"
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return answer
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