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app_script = """ |
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import gradio as gr |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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# Load the model and tokenizer |
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model_path = "Ozaii/TinyWali1.1B" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained(model_path) |
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# Ensure the model is in evaluation mode and on the correct device |
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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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model.eval() |
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# Define Generation Parameters and Function with Enhanced Context Management |
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def generate_response(user_input, chat_history): |
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max_context_length = 750 # Specify the maximum context length |
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max_response_length = 150 # Specify the maximum response length |
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# Prepare the prompt with chat history |
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prompt = "" |
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for message in chat_history: |
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if message[0] is not None: |
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prompt += f"User: {message[0]}\n" |
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if message[1] is not None: |
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prompt += f"Assistant: {message[1]}\n" |
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prompt += f"User: {user_input}\nAssistant:" |
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# Ensure the context does not exceed the maximum context length |
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prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False) |
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if len(prompt_tokens) > max_context_length: |
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prompt_tokens = prompt_tokens[-max_context_length:] |
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prompt = tokenizer.decode(prompt_tokens, clean_up_tokenization_spaces=True) |
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inputs = tokenizer(prompt, return_tensors="pt").to(device) |
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# Generate response |
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with torch.no_grad(): |
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outputs = model.generate( |
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inputs.input_ids, |
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max_length=len(inputs.input_ids[0]) + max_response_length, # Limit the maximum length for context and response |
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min_length=45, |
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temperature=0.7, # Slightly higher temperature for more diverse responses |
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top_k=30, |
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top_p=0.9, # Allow a bit more randomness |
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repetition_penalty=1.1, # Mild repetition penalty |
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no_repeat_ngram_size=3, # Ensure no repeated phrases |
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eos_token_id=tokenizer.eos_token_id, |
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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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# Post-process the assistant's response |
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assistant_response = response.split("Assistant:")[-1].strip() |
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# Ensure the response ends properly by stripping incomplete sentences |
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assistant_response = assistant_response.split('\\n')[0].strip() |
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# Append the interaction to the chat history |
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chat_history.append((user_input, assistant_response)) |
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# Return the updated chat history |
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return chat_history, chat_history |
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def restart_chat(): |
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return [], [] |
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# Create Gradio Interface |
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with gr.Blocks() as chat_interface: |
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gr.Markdown("<h1><center>W.AI Chat Nikker xD</center></h1>") |
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chat_history = gr.State([]) |
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with gr.Column(): |
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chatbox = gr.Chatbot() |
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with gr.Row(): |
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user_input = gr.Textbox(show_label=False, placeholder="Summon Wali Here...") |
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submit_button = gr.Button("Send") |
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restart_button = gr.Button("Restart") |
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submit_button.click( |
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generate_response, |
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inputs=[user_input, chat_history], |
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outputs=[chatbox, chat_history] |
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) |
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restart_button.click( |
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restart_chat, |
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inputs=[], |
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outputs=[chatbox, chat_history] |
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) |
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# Launch the Gradio interface |
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chat_interface.launch(share=True) |
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""" |
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with open("app.py", "w") as f: |
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f.write(app_script) |
