Spaces:
Sleeping
Sleeping
File size: 2,479 Bytes
6e06e81 174bc58 582d6a0 6e06e81 582d6a0 6e06e81 582d6a0 6e06e81 174bc58 6e06e81 582d6a0 174bc58 582d6a0 174bc58 582d6a0 174bc58 582d6a0 174bc58 582d6a0 174bc58 582d6a0 174bc58 6e06e81 582d6a0 6e06e81 174bc58 582d6a0 6e06e81 582d6a0 6e06e81 582d6a0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch
app = FastAPI(title="Forex Sentiment API", version="1.0")
# ===============================
# Load Models
# ===============================
finbert_name = "ProsusAI/finbert"
longformer_name = "Miruzen/LongFormer_Skripsi"
device = 0 if torch.cuda.is_available() else -1
print("📥 Loading FinBERT model...")
finbert = pipeline(
"text-classification",
model=finbert_name,
tokenizer=finbert_name,
return_all_scores=True,
device=device,
)
print("📥 Loading LongFormer model...")
longformer = pipeline(
"text-classification",
model=longformer_name,
tokenizer=longformer_name,
return_all_scores=True,
device=device,
)
class InputData(BaseModel):
title: str | None = None
content: str | None = None
def extract_scores(predictions):
"""Convert HF model output into {positive, neutral, negative} dict."""
scores = {"positive": 0.0, "neutral": 0.0, "negative": 0.0}
for item in predictions[0]:
label = item["label"].lower()
if "pos" in label:
scores["positive"] = item["score"]
elif "neg" in label:
scores["negative"] = item["score"]
elif "neu" in label:
scores["neutral"] = item["score"]
dominant = max(scores, key=scores.get)
return {"label": dominant, "scores": scores}
@app.post("/analyze")
def analyze(data: InputData):
title_result, content_result = None, None
if data.title:
finbert_out = finbert(data.title)
title_result = extract_scores(finbert_out)
if data.content:
longformer_out = longformer(data.content)
content_result = extract_scores(longformer_out)
mood_score = (
(title_result["scores"].get("positive", 0) if title_result else 0)
+ (content_result["scores"].get("positive", 0) if content_result else 0)
- (title_result["scores"].get("negative", 0) if title_result else 0)
- (content_result["scores"].get("negative", 0) if content_result else 0)
)
return {
"status": "ok",
"mood_score": mood_score,
"details": { # 🔹 <── inilah kunci penting yang ditunggu Supabase!
"title": title_result,
"content": content_result,
},
}
@app.get("/")
def root():
return {"message": "Forex Sentiment API active!"}
|