Rename database.csv to backend.py

backend.py

@@ -0,0 +1,41 @@
+from fastapi import FastAPI, UploadFile, File
+import requests
+from transformers import pipeline
+from sentence_transformers import CrossEncoder
+import pandas as pd
+import os
+
+app = FastAPI()
+
+ai_detector = pipeline("text-classification", model="roberta-base-openai-detector")
+text_model = CrossEncoder("cross-encoder/stsb-roberta-large")
+
+TEXT_DB = "text_plagiarism.csv"
+
+def load_database():
+    if not os.path.exists(TEXT_DB):
+        pd.DataFrame(columns=["content", "plagiarism_score"]).to_csv(TEXT_DB, index=False)
+    return pd.read_csv(TEXT_DB)
+
+def save_to_database(content, plagiarism_score):
+    df = load_database()
+    new_entry = pd.DataFrame({"content": [content], "plagiarism_score": [plagiarism_score]})
+    df = pd.concat([df, new_entry], ignore_index=True)
+    df.to_csv(TEXT_DB, index=False)
+
+@app.post("/check_text")
+def check_text(text: str):
+    stored_texts = load_database()["content"].tolist()
+    if stored_texts:
+        similarity_scores = text_model.predict([[text, stored] for stored in stored_texts])
+        highest_similarity = max(similarity_scores)
+    else:
+        highest_similarity = 0
+    save_to_database(text, highest_similarity * 100)
+    return {"plagiarism_score": highest_similarity * 100}
+
+@app.post("/detect_ai")
+def detect_ai(text: str):
+    result = ai_detector(text)
+    return result[0]
+

database.csv

@@ -1 +0,0 @@
-text