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Update app.py
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app.py
CHANGED
@@ -1,11 +1,9 @@
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import gradio as gr
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from transformers import AutoTokenizer, AutoModel, GPT2LMHeadModel, GPT2Tokenizer
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import torch
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from sklearn.metrics.pairwise import cosine_similarity
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import numpy as np
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# Load the bi-encoder model and tokenizer
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bi_encoder_model_name = "
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bi_tokenizer = AutoTokenizer.from_pretrained(bi_encoder_model_name)
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bi_model = AutoModel.from_pretrained(bi_encoder_model_name)
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@@ -20,28 +18,36 @@ def encode_text(text):
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# Ensure the output is 2D by averaging the last hidden state along the sequence dimension
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return outputs.last_hidden_state.mean(dim=1).detach().numpy()
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def generate_response(user_input):
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#
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# Generate a response using GPT-2
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gpt2_inputs = gpt2_tokenizer.encode(
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gpt2_outputs = gpt2_model.generate(
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generated_text = gpt2_tokenizer.decode(gpt2_outputs[0], skip_special_tokens=True)
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return generated_text
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def chatbot(user_input):
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return response
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# Create the Gradio interface
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iface = gr.Interface(
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fn=chatbot,
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inputs=gr.Textbox(lines=2, placeholder="Enter your message here..."),
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outputs="text",
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title="Dynamic Response Chatbot",
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description="A chatbot using a bi-encoder model to understand the input and GPT-2 to generate dynamic responses."
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)
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# Launch the interface
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@@ -49,3 +55,4 @@ iface.launch()
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import gradio as gr
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from transformers import AutoTokenizer, AutoModel, GPT2LMHeadModel, GPT2Tokenizer
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import torch
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# Load the bi-encoder model and tokenizer
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bi_encoder_model_name = "sentence-transformers/all-MiniLM-L6-v2"
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bi_tokenizer = AutoTokenizer.from_pretrained(bi_encoder_model_name)
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bi_model = AutoModel.from_pretrained(bi_encoder_model_name)
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# Ensure the output is 2D by averaging the last hidden state along the sequence dimension
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return outputs.last_hidden_state.mean(dim=1).detach().numpy()
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def generate_response(user_input, context_embedding):
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# Combine user input with context embedding for GPT-2 input
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combined_input = user_input + " " + context_embedding
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# Generate a response using GPT-2 with adjusted parameters
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gpt2_inputs = gpt2_tokenizer.encode(combined_input, return_tensors='pt')
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gpt2_outputs = gpt2_model.generate(
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gpt2_inputs,
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max_length=150,
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num_return_sequences=1,
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temperature=0.5,
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top_p=0.9,
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repetition_penalty=1.2
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)
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generated_text = gpt2_tokenizer.decode(gpt2_outputs[0], skip_special_tokens=True)
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return generated_text
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def chatbot(user_input, context=""):
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context_embedding = encode_text(context) if context else ""
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response = generate_response(user_input, context_embedding)
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return response
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# Create the Gradio interface
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iface = gr.Interface(
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fn=chatbot,
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inputs=[gr.Textbox(lines=2, placeholder="Enter your message here..."), gr.Textbox(lines=2, placeholder="Enter context here (optional)...")],
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outputs="text",
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title="Context-Aware Dynamic Response Chatbot",
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description="A chatbot using a bi-encoder model to understand the input context and GPT-2 to generate dynamic responses."
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)
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# Launch the interface
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