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App.py

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+# app.py
+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)
+
+# ===============================
+# Input Schema
+# ===============================
+class InputData(BaseModel):
+    title: str | None = None
+    content: str | None = None
+
+
+# ===============================
+# Helper Functions
+# ===============================
+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}
+
+
+# ===============================
+# Main Endpoint
+# ===============================
+@app.post("/analyze")
+def analyze(data: InputData):
+    result = {}
+
+    if data.title:
+        finbert_out = finbert(data.title)
+        result["title"] = extract_scores(finbert_out)
+
+    if data.content:
+        longformer_out = longformer(data.content)
+        result["content"] = extract_scores(longformer_out)
+
+    # Gabungkan menjadi mood_score sederhana
+    mood_score = (
+        result.get("title", {}).get("scores", {}).get("positive", 0)
+        + result.get("content", {}).get("scores", {}).get("positive", 0)
+        - result.get("title", {}).get("scores", {}).get("negative", 0)
+        - result.get("content", {}).get("scores", {}).get("negative", 0)
+    )
+
+    return {
+        "mood_score": mood_score,
+        "details": result,
+        "status": "ok"
+    }
+
+
+@app.get("/")
+def root():
+    return {"message": "Forex Sentiment API active!"}

requirments.txt

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+fastapi
+uvicorn
+transformers==4.45.0
+torch
+pydantic