sonyps1928
commited on
Commit
Β·
cef31a4
1
Parent(s):
40bbb95
update app
Browse files
app.py
CHANGED
@@ -1,340 +1,233 @@
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import gradio as gr
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import os
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import torch
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import json
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from fastapi import FastAPI, HTTPException, Depends, Header
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from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
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import uvicorn
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from pydantic import BaseModel
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from typing import Optional
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#
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MODEL_CONFIGS = {
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"gpt2": {
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"type": "causal",
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"model_class": GPT2LMHeadModel,
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"tokenizer_class": GPT2Tokenizer,
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"description": "Original GPT-2, good for creative writing",
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"size": "117M"
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},
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"distilgpt2": {
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"type": "causal",
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"model_class": AutoModelForCausalLM,
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"tokenizer_class": AutoTokenizer,
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"description": "Smaller, faster GPT-2",
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"size": "82M"
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},
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"google/flan-t5-small": {
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"type": "seq2seq",
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"model_class": T5ForConditionalGeneration,
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"tokenizer_class": T5Tokenizer,
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"description": "Instruction-following T5 model",
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"size": "80M"
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},
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"microsoft/DialoGPT-small": {
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"type": "causal",
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"model_class": AutoModelForCausalLM,
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"tokenizer_class": AutoTokenizer,
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"description": "Conversational AI model",
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"size": "117M"
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}
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}
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# Environment variables
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HF_TOKEN = os.getenv("HF_TOKEN")
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API_KEY = os.getenv("API_KEY")
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ADMIN_PASSWORD = os.getenv("ADMIN_PASSWORD")
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#
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def
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if
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return
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try:
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except Exception as e:
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if API_KEY:
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model, tokenizer = load_model_and_tokenizer(model_name)
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if config["type"] == "causal":
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inputs = tokenizer.encode(prompt, return_tensors="pt", max_length=512, truncation=True)
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with torch.no_grad():
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outputs = model.generate(
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inputs,
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max_length=min(max_length + inputs.shape[1], 512),
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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do_sample=True,
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pad_token_id=tokenizer.pad_token_id,
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num_return_sequences=1
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)
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return generated_text[len(prompt):].strip()
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elif config["type"] == "seq2seq":
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task_prompt = f"Complete this text: {prompt}" if "flan-t5" in model_name.lower() else prompt
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inputs = tokenizer(task_prompt, return_tensors="pt", max_length=512, truncation=True)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_length=max_length,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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do_sample=True,
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num_return_sequences=1
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)
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return generated_text.strip()
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except Exception as e:
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raise RuntimeError(f"Error generating text: {str(e)}")
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# Gradio interface function
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def generate_text_gradio(prompt, model_name, max_length, temperature, top_p, top_k, api_key=""):
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if API_KEY and api_key != API_KEY:
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return "Error: Invalid API key"
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try:
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return generate_text_core(prompt, model_name, max_length, temperature, top_p, top_k)
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except Exception as e:
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return f"Error: {str(e)}"
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# Create FastAPI app
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app = FastAPI(title="Multi-Model Text Generation API", version="1.0.0")
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# API Routes
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@app.post("/generate", response_model=GenerateResponse)
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async def generate_text_api(
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request: GenerateRequest,
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authenticated: bool = Depends(authenticate_api_key)
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):
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try:
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generated_text = generate_text_core(
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request.prompt,
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request.model_name,
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request.max_length,
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request.temperature,
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request.top_p,
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request.top_k
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)
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return GenerateResponse(
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generated_text=generated_text,
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model_used=request.model_name
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/models")
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async def list_models():
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return {
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"models": [
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{
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"name": name,
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"description": config["description"],
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"size": config["size"],
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"type": config["type"]
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}
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for name, config in MODEL_CONFIGS.items()
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]
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}
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@app.get("/health")
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async def health_check():
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return {"status": "healthy", "loaded_model": loaded_model_name}
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# Create Gradio interface
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with gr.Blocks(title="Multi-Model Text Generation Server") as demo:
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gr.Markdown("# Multi-Model Text Generation Server")
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gr.Markdown("Choose a model from the dropdown, enter a text prompt, and generate text.")
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with gr.Row():
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with gr.Column():
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model_selector = gr.Dropdown(
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label="Model",
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choices=list(MODEL_CONFIGS.keys()),
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value="gpt2",
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interactive=True
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)
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# Show model info
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model_info = gr.Markdown("**Model Info:** Original GPT-2, good for creative writing (117M)")
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def update_model_info(model_name):
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config = MODEL_CONFIGS[model_name]
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return f"**Model Info:** {config['description']} ({config['size']})"
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model_selector.change(update_model_info, inputs=model_selector, outputs=model_info)
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prompt_input = gr.Textbox(
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label="Text Prompt",
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placeholder="Enter
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lines=
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max_length_slider = gr.Slider(
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10, 200, 100, 10,
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label="Max Generation Length"
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)
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temperature_slider = gr.Slider(
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0.1, 2.0, 0.7, 0.1,
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label="Temperature"
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)
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top_p_slider = gr.Slider(
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0.1, 1.0, 0.9, 0.05,
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label="Top-p (nucleus sampling)"
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)
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top_k_slider = gr.Slider(
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1, 100, 50, 1,
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label="Top-k sampling"
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)
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if API_KEY:
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api_key_input = gr.Textbox(
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label="API Key",
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type="password",
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placeholder="Enter API
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)
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else:
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api_key_input = gr.Textbox(value="", visible=False)
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with gr.Column():
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label="Generated Text",
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lines=
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placeholder="Generated text will appear here..."
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)
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gr.Examples(
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examples=[
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["Once upon a time in a distant galaxy,"],
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["The future of artificial intelligence is"],
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["In the heart of the ancient forest,"],
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["The detective walked into the room and noticed"],
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],
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inputs=prompt_input
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)
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### POST /generate
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Generate text using the specified model.
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**Request Body:**
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```json
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{
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"prompt": "Your text prompt here",
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"model_name": "gpt2",
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"max_length": 100,
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"temperature": 0.7,
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"top_p": 0.9,
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"top_k": 50
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}
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```
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**Response:**
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```json
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{
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"generated_text": "Generated text...",
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"model_used": "gpt2",
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"status": "success"
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}
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```
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### GET /models
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List all available models.
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### GET /health
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Check server health and loaded model status.
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**Example cURL:**
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```bash
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curl -X POST "http://localhost:7860/generate" \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer YOUR_API_KEY" \
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-d '{"prompt": "Once upon a time", "model_name": "gpt2"}'
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```
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""")
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# Mount Gradio app to FastAPI
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app = gr.mount_gradio_app(app, demo, path="/")
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if __name__ == "__main__":
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auth_config = ("admin", ADMIN_PASSWORD) if ADMIN_PASSWORD else None
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# Launch with both FastAPI and Gradio
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demo.launch(
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auth=
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)
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import gradio as gr
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import os
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import hashlib
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import time
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from collections import defaultdict
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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import torch
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# Load secrets from environment
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HF_TOKEN = os.getenv("HF_TOKEN")
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API_KEY = os.getenv("API_KEY")
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ADMIN_PASSWORD = os.getenv("ADMIN_PASSWORD")
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print(f"π Security Status:")
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print(f" HF_TOKEN: {'β
Set' if HF_TOKEN else 'β Not set'}")
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print(f" API_KEY: {'β
Set' if API_KEY else 'β Not set'}")
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print(f" ADMIN_PASSWORD: {'β
Set' if ADMIN_PASSWORD else 'β Not set'}")
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# Rate limiting storage
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request_counts = defaultdict(list)
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# Load model with optional HF token
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model_name = "gpt2"
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try:
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if HF_TOKEN:
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tokenizer = GPT2Tokenizer.from_pretrained(model_name, use_auth_token=HF_TOKEN)
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model = GPT2LMHeadModel.from_pretrained(model_name, use_auth_token=HF_TOKEN)
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print("β
Model loaded with HF token")
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else:
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tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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model = GPT2LMHeadModel.from_pretrained(model_name)
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print("β
Model loaded without token")
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tokenizer.pad_token = tokenizer.eos_token
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print("β
Model initialization complete")
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except Exception as e:
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print(f"β Model loading failed: {e}")
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raise e
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def validate_api_key(provided_key):
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"""Validate API key with rate limiting"""
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if not API_KEY:
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return True, "No API key required"
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if not provided_key:
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return False, "API key required but not provided"
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if provided_key != API_KEY:
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return False, "Invalid API key"
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# Rate limiting per API key
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now = time.time()
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key_hash = hashlib.sha256(provided_key.encode()).hexdigest()[:8]
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# Clean old requests (last hour)
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request_counts[key_hash] = [
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req_time for req_time in request_counts[key_hash]
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if now - req_time < 3600
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]
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# Check rate limit (100 requests per hour)
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if len(request_counts[key_hash]) >= 100:
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return False, "Rate limit exceeded (100 requests/hour)"
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# Log successful request
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request_counts[key_hash].append(now)
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return True, f"Authenticated (Requests: {len(request_counts[key_hash])}/100)"
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def generate_text(prompt, max_length=100, temperature=0.7, top_p=0.9, top_k=50, api_key=""):
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"""Generate text with security validation"""
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# Validate API key
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is_valid, message = validate_api_key(api_key)
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if not is_valid:
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return f"π Authentication Error: {message}"
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# Input validation
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if not prompt or len(prompt.strip()) == 0:
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return "β Error: Prompt cannot be empty"
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if len(prompt) > 1000:
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return "β Error: Prompt too long (max 1000 characters)"
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try:
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print(f"π {message}")
|
87 |
+
print(f"π Generating text for prompt: {prompt[:50]}...")
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88 |
|
89 |
+
inputs = tokenizer.encode(prompt, return_tensors="pt", max_length=512, truncation=True)
|
90 |
+
|
91 |
+
with torch.no_grad():
|
92 |
+
outputs = model.generate(
|
93 |
+
inputs,
|
94 |
+
max_length=min(max_length + len(inputs[0]), 512),
|
95 |
+
temperature=max(0.1, min(2.0, temperature)),
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96 |
+
top_p=max(0.1, min(1.0, top_p)),
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97 |
+
top_k=max(1, min(100, top_k)),
|
98 |
+
do_sample=True,
|
99 |
+
pad_token_id=tokenizer.eos_token_id,
|
100 |
+
num_return_sequences=1
|
101 |
+
)
|
102 |
+
|
103 |
+
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
104 |
+
result = generated_text[len(prompt):].strip()
|
105 |
+
|
106 |
+
print(f"β
Generation successful, length: {len(result)} chars")
|
107 |
+
return result
|
108 |
|
109 |
except Exception as e:
|
110 |
+
error_msg = f"β Generation error: {str(e)}"
|
111 |
+
print(error_msg)
|
112 |
+
return error_msg
|
113 |
+
|
114 |
+
# Create Gradio interface with conditional elements
|
115 |
+
with gr.Blocks(title="π Secure GPT-2 Generator") as demo:
|
116 |
+
gr.Markdown("# π Secure GPT-2 Text Generator")
|
117 |
+
gr.Markdown("**Security Features**: API Authentication β’ Rate Limiting β’ Admin Protection")
|
118 |
+
|
119 |
+
# Security status display
|
120 |
+
security_status = []
|
121 |
+
if HF_TOKEN:
|
122 |
+
security_status.append("π HF Token Active")
|
123 |
if API_KEY:
|
124 |
+
security_status.append("π API Authentication Enabled")
|
125 |
+
if ADMIN_PASSWORD:
|
126 |
+
security_status.append("π€ Admin Protection Active")
|
127 |
+
|
128 |
+
if security_status:
|
129 |
+
gr.Markdown(f"**Active Security**: {' β’ '.join(security_status)}")
|
130 |
+
else:
|
131 |
+
gr.Markdown("β οΈ **No security features enabled** - running in public mode")
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|
133 |
with gr.Row():
|
134 |
with gr.Column():
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|
135 |
prompt_input = gr.Textbox(
|
136 |
+
label="βοΈ Text Prompt",
|
137 |
+
placeholder="Enter your prompt here... (max 1000 chars)",
|
138 |
+
lines=3,
|
139 |
+
max_lines=5
|
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|
140 |
)
|
141 |
+
|
142 |
+
# Show API key input only if API_KEY is configured
|
143 |
if API_KEY:
|
144 |
api_key_input = gr.Textbox(
|
145 |
+
label="π API Key (Required)",
|
146 |
type="password",
|
147 |
+
placeholder="Enter your API key...",
|
148 |
+
info="API authentication is enabled for this Space"
|
149 |
)
|
150 |
else:
|
151 |
api_key_input = gr.Textbox(value="", visible=False)
|
152 |
+
gr.Markdown("π **Public Access**: No API key required")
|
153 |
+
|
154 |
+
with gr.Accordion("βοΈ Generation Parameters", open=False):
|
155 |
+
with gr.Row():
|
156 |
+
max_length = gr.Slider(
|
157 |
+
minimum=10,
|
158 |
+
maximum=200,
|
159 |
+
value=100,
|
160 |
+
step=10,
|
161 |
+
label="π Max Length"
|
162 |
+
)
|
163 |
+
temperature = gr.Slider(
|
164 |
+
minimum=0.1,
|
165 |
+
maximum=2.0,
|
166 |
+
value=0.7,
|
167 |
+
step=0.1,
|
168 |
+
label="π‘οΈ Temperature"
|
169 |
+
)
|
170 |
+
|
171 |
+
with gr.Row():
|
172 |
+
top_p = gr.Slider(
|
173 |
+
minimum=0.1,
|
174 |
+
maximum=1.0,
|
175 |
+
value=0.9,
|
176 |
+
step=0.1,
|
177 |
+
label="π― Top-p"
|
178 |
+
)
|
179 |
+
top_k = gr.Slider(
|
180 |
+
minimum=1,
|
181 |
+
maximum=100,
|
182 |
+
value=50,
|
183 |
+
step=1,
|
184 |
+
label="π’ Top-k"
|
185 |
+
)
|
186 |
+
|
187 |
+
generate_btn = gr.Button("π Generate Text", variant="primary", size="lg")
|
188 |
+
|
189 |
with gr.Column():
|
190 |
+
output_text = gr.Textbox(
|
191 |
+
label="π Generated Text",
|
192 |
+
lines=12,
|
193 |
+
placeholder="Generated text will appear here...",
|
194 |
+
show_copy_button=True
|
195 |
)
|
196 |
+
|
197 |
+
# Rate limit info
|
198 |
+
if API_KEY:
|
199 |
+
gr.Markdown("**Rate Limits**: 100 requests per hour per API key")
|
200 |
+
|
201 |
+
# Examples
|
|
|
202 |
gr.Examples(
|
203 |
examples=[
|
204 |
["Once upon a time in a distant galaxy,"],
|
205 |
["The future of artificial intelligence is"],
|
206 |
["In the heart of the ancient forest,"],
|
207 |
["The detective walked into the room and noticed"],
|
208 |
+
["Write a short story about a robot who dreams of"],
|
209 |
],
|
210 |
+
inputs=prompt_input,
|
211 |
+
label="π‘ Example Prompts"
|
212 |
+
)
|
213 |
+
|
214 |
+
# Connect the generation function
|
215 |
+
generate_btn.click(
|
216 |
+
fn=generate_text,
|
217 |
+
inputs=[prompt_input, max_length, temperature, top_p, top_k, api_key_input],
|
218 |
+
outputs=output_text
|
219 |
)
|
220 |
|
221 |
+
# Launch with authentication
|
222 |
+
auth_tuple = None
|
223 |
+
if ADMIN_PASSWORD:
|
224 |
+
auth_tuple = ("admin", ADMIN_PASSWORD)
|
225 |
+
print("π Admin authentication enabled")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
226 |
|
227 |
if __name__ == "__main__":
|
|
|
|
|
|
|
228 |
demo.launch(
|
229 |
+
auth=auth_tuple,
|
230 |
+
show_api=True, # Enable API documentation
|
231 |
+
show_error=True
|
232 |
+
)
|
233 |
+
print("π Secure GPT-2 Generator is running!")
|
|