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Update app.py
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app.py
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@@ -1,13 +1,10 @@
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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# Load model and tokenizer
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model_name = "Smilyai-labs/Sam-large-v1-speacil"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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# Chat function
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def respond(message, history):
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chat_prompt += f"User: {user}\nSam: {bot}\n"
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chat_prompt += f"User: {message}\nSam:"
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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reply = response[len(chat_prompt):].split("\n")[0].strip()
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# Load model and tokenizer
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model_name = "Smilyai-labs/Sam-large-v1-speacil"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# Chat function
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def respond(message, history):
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chat_prompt += f"User: {user}\nSam: {bot}\n"
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chat_prompt += f"User: {message}\nSam:"
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# Tokenize input and generate a response
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inputs = tokenizer(chat_prompt, return_tensors="pt")
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outputs = model.generate(inputs["input_ids"], max_length=200, num_return_sequences=1)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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reply = response[len(chat_prompt):].split("\n")[0].strip()
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