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Update app.py
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app.py
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@@ -2,19 +2,34 @@ import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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print("Loading the model......")
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model_name = "WICKED4950/Irisonego5"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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print("Interface getting done....")
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# Define the chatbot function
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def
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return response
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# Gradio interface
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iface = gr.Interface(fn=
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inputs="text",
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outputs="text",
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title="Your Chatbot")
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from transformers import AutoModelForCausalLM, AutoTokenizer
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print("Loading the model......")
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model_name = "WICKED4950/Irisonego5"
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strategy = tf.distribute.MirroredStrategy()
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tf.config.optimizer.set_jit(True) # Enable XLA
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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with strategy.scope():
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model = AutoModelForCausalLM.from_pretrained(model_name)
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print("Interface getting done....")
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# Define the chatbot function
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def predict(user_input):
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# Tokenize input text
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inputs = tokenizer(user_input, return_tensors="tf", padding=True, truncation=True)
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# Generate the response using the model
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response_ids = model.generate(
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inputs['input_ids'],
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max_length=128, # Set max length of response
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do_sample=True, # Sampling for variability
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top_k=15, # Consider top 50 tokens
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top_p=0.95, # Nucleus sampling
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temperature=0.8 # Adjusts creativity of response
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)
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# Decode the response
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response = tokenizer.decode(response_id[0], skip_special_tokens=True)
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return response
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# Gradio interface
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iface = gr.Interface(fn=predict,
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inputs="text",
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outputs="text",
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title="Your Chatbot")
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