Create app.py

app.py

@@ -0,0 +1,98 @@
+# Create app.py file
+app_script = """
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+model_path = "Ozaii/TinyWali1.1B"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path)
+
+# Ensure the model is in evaluation mode and on the correct device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# Define Generation Parameters and Function with Enhanced Context Management
+def generate_response(user_input, chat_history):
+    max_context_length = 750  # Specify the maximum context length
+    max_response_length = 150  # Specify the maximum response length
+
+    # Prepare the prompt with chat history
+    prompt = ""
+    for message in chat_history:
+        if message[0] is not None:
+            prompt += f"User: {message[0]}\n"
+        if message[1] is not None:
+            prompt += f"Assistant: {message[1]}\n"
+    prompt += f"User: {user_input}\nAssistant:"
+
+    # Ensure the context does not exceed the maximum context length
+    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)
+    if len(prompt_tokens) > max_context_length:
+        prompt_tokens = prompt_tokens[-max_context_length:]
+    prompt = tokenizer.decode(prompt_tokens, clean_up_tokenization_spaces=True)
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+    # Generate response
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs.input_ids,
+            max_length=len(inputs.input_ids[0]) + max_response_length,  # Limit the maximum length for context and response
+            min_length=45,
+            temperature=0.7,  # Slightly higher temperature for more diverse responses
+            top_k=30,
+            top_p=0.9,  # Allow a bit more randomness
+            repetition_penalty=1.1,  # Mild repetition penalty
+            no_repeat_ngram_size=3,  # Ensure no repeated phrases
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Post-process the assistant's response
+    assistant_response = response.split("Assistant:")[-1].strip()
+    # Ensure the response ends properly by stripping incomplete sentences
+    assistant_response = assistant_response.split('\\n')[0].strip()
+
+    # Append the interaction to the chat history
+    chat_history.append((user_input, assistant_response))
+
+    # Return the updated chat history
+    return chat_history, chat_history
+
+def restart_chat():
+    return [], []
+
+# Create Gradio Interface
+with gr.Blocks() as chat_interface:
+    gr.Markdown("<h1><center>W.AI Chat Nikker xD</center></h1>")
+    chat_history = gr.State([])
+    with gr.Column():
+        chatbox = gr.Chatbot()
+        with gr.Row():
+            user_input = gr.Textbox(show_label=False, placeholder="Summon Wali Here...")
+            submit_button = gr.Button("Send")
+            restart_button = gr.Button("Restart")
+
+    submit_button.click(
+        generate_response,
+        inputs=[user_input, chat_history],
+        outputs=[chatbox, chat_history]
+    )
+
+    restart_button.click(
+        restart_chat,
+        inputs=[],
+        outputs=[chatbox, chat_history]
+    )
+
+# Launch the Gradio interface
+chat_interface.launch(share=True)
+"""
+# Save the script to app.py
+with open("app.py", "w") as f:
+    f.write(app_script)