Create app.py

app.py

@@ -0,0 +1,40 @@
+import os
+from fastapi import FastAPI
+from pydantic import BaseModel
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+MODEL_PATH = "LSTM__0.9170.pt.pt"
+MODEL_URL = "https://drive.google.com/uc?id=133F-sRp_mCGOo73t1ieSnbk5fSxPFENT"
+
+if not os.path.exists(MODEL_PATH):
+    import gdown
+    print("Scaricamento dei pesi dal Google Drive...")
+    gdown.download(MODEL_URL, MODEL_PATH, quiet=False)
+
+tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-base-cased-v1.1", use_fast=False)
+model = AutoModelForSequenceClassification.from_pretrained(
+    "dmis-lab/biobert-base-cased-v1.1",
+    num_labels=2
+)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
+model.to(device)
+model.eval()
+
+app = FastAPI()
+
+class Query(BaseModel):
+    question: str
+    context: str
+    long_answer: str
+
+@app.post("/chat")
+def get_response(query: Query):
+    text = query.question + " " + query.context + " " + query.long_answer
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    outputs = model(**inputs)
+    answer = torch.argmax(outputs.logits, dim=-1).item()
+    result = "Yes" if answer == 1 else "No"
+    return {"answer": result}