Add initial implementation of TacoGPT chatbot

.gitignore

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+.idea

app.py

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+from typing import List, Tuple
+
+import torch
+import gradio as gr
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+)
+
+# Causal LMs only
+DEFAULT_MODELS = [
+    "microsoft/DialoGPT-medium",
+    "gpt2",
+    "EleutherAI/gpt-neo-125M",
+    "EleutherAI/pythia-350m",
+    "facebook/opt-350m",
+    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    "Qwen/Qwen1.5-1.8B-Chat",
+]
+
+# cache model load to optimize model selection
+_MODEL_CACHE = {}
+
+
+def load_model(model_name: str):
+    key = model_name
+
+    if key in _MODEL_CACHE:
+        return _MODEL_CACHE[key]
+
+    tok = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+    model = AutoModelForCausalLM.from_pretrained(model_name, use_safetensors=True)
+    model.eval()
+
+    _MODEL_CACHE[key] = (tok, model)
+    return tok, model
+
+
+def build_inputs(tokenizer, history: List[Tuple[str, str]], user_message: str):
+    # If a chat template exists, use it (best for Qwen/TinyLlama).
+    use_chat_template = bool(getattr(tokenizer, "chat_template", None))
+    if use_chat_template:
+        conv = []
+        for u, b in (history or [])[-6:]:
+            conv.append({"role": "user", "content": u})
+            conv.append({"role": "assistant", "content": b})
+        conv.append({"role": "user", "content": user_message})
+
+        prompt_ids = tokenizer.apply_chat_template(
+            conv,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_tensors=None,
+        )
+        input_ids = torch.tensor([prompt_ids], dtype=torch.long)
+        attention_mask = torch.ones_like(input_ids)
+        return input_ids, attention_mask
+
+    eos = tokenizer.eos_token or ""
+
+    ids = []
+    for u, b in (history or [])[-6:]:
+        ids.extend(tokenizer.encode(u + eos))
+        ids.extend(tokenizer.encode(b + eos))
+
+    # Current user message; add EOS to mark turn boundary for all non-templated LMs
+    ids.extend(tokenizer.encode(user_message + eos))
+
+    input_ids = torch.tensor([ids], dtype=torch.long)
+    attention_mask = torch.ones_like(input_ids)
+    return input_ids, attention_mask
+
+
+def chat_fn(user_message: str, history: List[Tuple[str, str]], model_name: str):
+    if not user_message or not user_message.strip():
+        return "", history, history
+
+    tokenizer, model = load_model(model_name)
+    input_ids, attention_mask = build_inputs(
+        tokenizer, history or [], user_message.strip()
+    )
+
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            max_new_tokens=48,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            repetition_penalty=1.1,
+            no_repeat_ngram_size=3,
+            pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
+        )
+
+    # Decode only the newly generated part
+    new_tokens = output_ids[0, input_ids.shape[1] :]
+    reply = tokenizer.decode(new_tokens, skip_special_tokens=True).strip()
+
+    if not reply:
+        reply = tokenizer.decode(output_ids[0], skip_special_tokens=True)[-200:].strip()
+
+    new_hist = (history or []) + [(user_message, reply)]
+    return "", new_hist, new_hist
+
+
+with gr.Blocks(title="🌮 TacoGPT") as demo:
+    gr.Markdown(
+        """
+        # 🌮 TacoGPT - Your Spicy AI Assistant 
+        """
+    )
+
+    with gr.Row():
+        model_name = gr.Dropdown(
+            choices=DEFAULT_MODELS,
+            value=DEFAULT_MODELS[0],
+            label="Model",
+            scale=2,
+        )
+
+    chatbot = gr.Chatbot(height=460, label="Chat")
+    msg = gr.Textbox(
+        placeholder="Type your message and press Enter...",
+        label="Message",
+        submit_btn="Send",
+    )
+
+    state = gr.State([])
+
+    gr.Examples(
+        examples=[
+            "Who created Python?",
+            "Write a taco recipe.",
+            "Give me a fun fact about tacos.",
+            "What's the history of tacos?",
+        ],
+        inputs=msg,
+    )
+
+    msg.submit(
+        chat_fn,
+        inputs=[msg, state, model_name],
+        outputs=[msg, chatbot, state],
+        queue=True,
+        api_name=False,
+    )
+
+    gr.Markdown(
+        """
+        **Tips**
+        - Tiny causal models can be quirky; try TinyLlama or Qwen 1.5 1.8B for better chat quality.
+        - Keep the questions short and objective. Like, "Who was Python creator?"
+        - The first run may be slower while downloading the LM weights.
+        """.strip()
+    )
+
+
+if __name__ == "__main__":
+    demo.launch(server_name="127.0.0.1", server_port=7860, show_error=True)

requirements.txt

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+--index-url https://download.pytorch.org/whl/cpu
+torch
+transformers
+gradio
+safetensors