初始化项目，由ModelHub XC社区提供模型
Model: DineshKasi/ai-assistant Source: Original Platform
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ModelHub XC
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app.py
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208
app.py
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"""
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STEP 3 — Gradio app that uses YOUR OWN model
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=============================================
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Upload this as app.py to your Hugging Face Space.
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It loads YOUR model (dineshkasi/my-ai-assistant) — not anyone else's!
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Your friends visit:
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https://huggingface.co/spaces/YOUR_USERNAME/YOUR_MODEL_NAME
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"""
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import torch
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# ══════════════════════════════════════════════════════
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# ✏️ CHANGE THESE TO YOUR OWN DETAILS
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# ══════════════════════════════════════════════════════
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HF_USERNAME = "DineshKasi"
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MODEL_NAME = "ai-assistant"
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# ══════════════════════════════════════════════════════
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REPO_ID = f"{HF_USERNAME}/{MODEL_NAME}"
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print(f"Loading YOUR model: {REPO_ID} ...")
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tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
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model = AutoModelForCausalLM.from_pretrained(REPO_ID)
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model.eval()
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print("Model loaded!")
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def ask(question, history, temperature, max_tokens):
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"""Generate a response from YOUR model."""
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# Build context from history (last 3 turns)
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context = ""
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for user_msg, bot_msg in history[-3:]:
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context += f"<|user|>{user_msg}<|endoftext|>"
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context += f"<|assistant|>{bot_msg}<|endoftext|>"
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prompt = context + f"<|user|>{question}<|endoftext|><|assistant|>"
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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with torch.no_grad():
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output = model.generate(
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inputs,
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max_new_tokens=int(max_tokens),
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temperature=float(temperature),
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do_sample=True,
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top_p=0.92,
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repetition_penalty=1.1,
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pad_token_id=tokenizer.eos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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)
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full_text = tokenizer.decode(output[0], skip_special_tokens=True)
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# Extract only the last assistant reply
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if "<|assistant|>" in full_text:
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reply = full_text.split("<|assistant|>")[-1].strip()
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else:
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reply = full_text[len(prompt):].strip()
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return reply
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# ── Gradio UI ─────────────────────────────────────────
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with gr.Blocks(
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theme=gr.themes.Soft(primary_hue="violet"),
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title=f"{HF_USERNAME}'s AI Assistant",
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css="""
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#header { text-align:center; padding: 24px 0 8px; }
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#header h1 { font-size:2rem; font-weight:700; color:#7c3aed; margin:0; }
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#header p { color:#6b7280; margin:6px 0 0; }
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.badge {
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display:inline-block; background:#f3e8ff; color:#7c3aed;
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border-radius:999px; padding:3px 14px; font-size:0.8rem;
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margin:3px; font-weight:500;
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}
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#badges { text-align:center; margin:8px 0 18px; }
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#model-credit {
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text-align:center; margin-top:14px;
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font-size:0.8rem; color:#9ca3af;
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}
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#model-credit a { color:#7c3aed; text-decoration:none; }
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footer { display:none !important; }
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""",
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) as demo:
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gr.HTML(f"""
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<div id="header">
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<h1>🤖 {HF_USERNAME}'s AI Assistant</h1>
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<p>Powered by <a href="https://huggingface.co/{REPO_ID}" target="_blank"
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style="color:#7c3aed;">{REPO_ID}</a> — your very own model!</p>
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</div>
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<div id="badges">
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<span class="badge">💻 Coding</span>
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<span class="badge">🔬 Science</span>
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<span class="badge">📐 Math</span>
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<span class="badge">✍️ Writing</span>
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<span class="badge">📊 Business</span>
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<span class="badge">🌍 General</span>
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</div>
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""")
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chatbot = gr.Chatbot(
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label="Chat with my model",
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bubble_full_width=False,
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height=460,
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)
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with gr.Row():
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txt = gr.Textbox(
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placeholder="Ask me anything...",
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show_label=False,
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scale=8,
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container=False,
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)
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btn = gr.Button("Send ➤", variant="primary", scale=1)
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gr.Examples(
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examples=[
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"Write a Python function to find prime numbers",
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"Explain how neural networks learn",
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"What is the difference between RAM and ROM?",
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"Help me write a professional email",
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"What is quantum entanglement?",
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"How does a binary search tree work?",
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],
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inputs=txt,
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label="💡 Try these",
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)
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with gr.Accordion("⚙️ Settings", open=False):
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temperature = gr.Slider(0.1, 1.2, value=0.7, step=0.1,
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label="Temperature (creativity)")
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max_tokens = gr.Slider(64, 512, value=200, step=32,
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label="Max response length")
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with gr.Accordion("🔌 Use this model in your project", open=False):
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gr.Markdown(f"""
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### Load my model directly in Python
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```python
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pip install transformers torch
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```
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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tokenizer = AutoTokenizer.from_pretrained("{REPO_ID}")
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model = AutoModelForCausalLM.from_pretrained("{REPO_ID}")
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def ask(question):
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prompt = f"<|user|>{{question}}<|endoftext|><|assistant|>"
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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with torch.no_grad():
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output = model.generate(inputs, max_new_tokens=200,
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temperature=0.7, do_sample=True,
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pad_token_id=tokenizer.eos_token_id)
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reply = tokenizer.decode(output[0], skip_special_tokens=True)
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return reply.split("<|assistant|>")[-1].strip()
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print(ask("Explain machine learning in simple terms"))
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```
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### Via Hugging Face Inference API (no install)
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```python
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from huggingface_hub import InferenceClient
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client = InferenceClient("{REPO_ID}")
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result = client.text_generation(
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"<|user|>What is a neural network?<|endoftext|><|assistant|>",
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max_new_tokens=200,
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)
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print(result)
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```
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> Model page: [huggingface.co/{REPO_ID}](https://huggingface.co/{REPO_ID})
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""")
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gr.HTML(f"""
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<div id="model-credit">
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Model: <a href="https://huggingface.co/{REPO_ID}">{REPO_ID}</a>
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· Built by {HF_USERNAME}
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· Hosted on Hugging Face Spaces
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</div>
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""")
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# Wiring
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def submit(msg, history):
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return "", history + [[msg, None]]
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def respond(history, temp, max_tok):
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question = history[-1][0]
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history[-1][1] = ask(question, history[:-1], temp, max_tok)
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return history
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txt.submit(submit, [txt, chatbot], [txt, chatbot], queue=False).then(
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respond, [chatbot, temperature, max_tokens], chatbot
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)
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btn.click(submit, [txt, chatbot], [txt, chatbot], queue=False).then(
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respond, [chatbot, temperature, max_tokens], chatbot
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)
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demo.queue()
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demo.launch()
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