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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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import pandas as pd
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from pathlib import Path
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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BASE_DIR = Path(__file__).resolve().parent
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MODEL_DIR = BASE_DIR / "bert_chatbot_model"
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DATASET_PATH = BASE_DIR / "dataset_chatbot_template.xlsx"
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try:
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tokenizer = AutoTokenizer.from_pretrained(str(MODEL_DIR))
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model = AutoModelForSequenceClassification.from_pretrained(str(MODEL_DIR))
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df_jawaban = pd.read_excel(DATASET_PATH)
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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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except Exception as e:
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print(f"❌ FATAL ERROR: {e}")
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responses = {
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"about_me": "I am a passionate developer specializing in AI and web development.",
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"skills": "My main skills are HTML5, CSS3, JavaScript, Laravel, Node.js, Database, TensorFlow, PyTorch, Firebase, and Jupyter Notebook.",
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"projects": "Some of my projects are Mobile Apps Bald Detection and Jupyter Notebook Bald Detection.",
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"experience": "I have worked as IT Support, AI Engineer, and Freelancer on multiple projects.",
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"career_goal": "My career goal is to become a Full Stack Developer and Machine Learning Engineer.",
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"greeting": "Hello! How can I help you regarding this portfolio?",
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"fallback": "I'm sorry, I don't understand. Please ask another question."
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}
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class ChatRequest(BaseModel):
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text: str
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@app.get("/")
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async def root():
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return {"message": "🚀 Chatbot API running on Hugging Face"}
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@app.post("/chatbot")
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async def chat(req: ChatRequest):
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if 'model' not in globals():
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return {"response": responses["fallback"], "intent": "error_loading"}
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try:
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inputs = tokenizer(req.text, return_tensors="pt", padding=True, truncation=True, max_length=128).to(device)
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with torch.no_grad():
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outputs = model(**inputs)
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pred_id = torch.argmax(outputs.logits, dim=1).item()
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intent = model.config.id2label.get(pred_id, "fallback")
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try:
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jawaban = df_jawaban.loc[df_jawaban['Intent'] == intent, 'Jawaban_ID'].iloc[0]
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except IndexError:
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jawaban = responses.get(intent, responses["fallback"])
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return {"intent": intent, "response": jawaban}
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except Exception as e:
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print(f"❌ Runtime Error: {e}")
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return {"response": "Internal server error"}
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