File size: 2,012 Bytes
ae5ad81 |
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 |
from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
import os
from huggingface_hub import InferenceClient
app = FastAPI()
# Statische Dateien (CSS, JS) einbinden
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")
# HF-Token aus Umgebungsvariable
HF_TOKEN = os.environ.get("HF_TOKEN", "")
@app.get("/", response_class=HTMLResponse)
async def root(request: Request):
return templates.TemplateResponse("index.html", {"request": request})
@app.post("/analyze")
async def analyze_text(request: Request):
data = await request.json()
user_text = data.get("text", "")
# Sentiment-Analyse durchführen
client = InferenceClient(token=HF_TOKEN)
sentiment = client.text_classification(
text=user_text,
model="cardiffnlp/twitter-roberta-base-sentiment"
)
# Chat-Antwort generieren
messages = data.get("history", [])
messages.append({"role": "user", "content": user_text})
# System-Prompt basierend auf Stimmung
best_sentiment = sorted(sentiment, key=lambda x: x["score"], reverse=True)[0]
if best_sentiment["label"] == "NEGATIVE" and best_sentiment["score"] > 0.6:
messages.append({"role": "system", "content": "Der Patient zeigt starke negative Emotionen – schlage Schuldprojektion oder Verdrängung vor."})
elif best_sentiment["label"] == "POSITIVE" and best_sentiment["score"] > 0.6:
messages.append({"role": "system", "content": "Der Patient wirkt übertrieben positiv – möglicherweise Abwehrmechanismus durch Kompensation."})
# Chat-Antwort generieren
response = client.chat_completion(
model="gpt-3.5-turbo",
messages=messages
)
return {
"reply": response.generated_text,
"toneLabel": best_sentiment["label"],
"toneScore": best_sentiment["score"]
} |