Update app.py

app.py

@@ -1,44 +1,35 @@
 import streamlit as st
-import os
-import numpy as np
-import pandas as pd
-from sentence_transformers import SentenceTransformer, util
+import requests
 
-# Load AI model (BERT)
-model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
+HF_BACKEND_URL = "https://your-backend-url.hf.space"  # Update this with your backend link
 
-# Function to check plagiarism
-def check_plagiarism(text, stored_texts):
-    documents = stored_texts + [text]
-    embeddings = model.encode(documents, convert_to_tensor=True)
-    similarity_matrix = util.pytorch_cos_sim(embeddings[-1], embeddings[:-1])
-    
-    highest_similarity = np.max(similarity_matrix.numpy())
-    return highest_similarity * 100
+st.title("🚀 Plagiarism & AI Detector")
 
-# Store past documents
-if not os.path.exists("database.csv"):
-    pd.DataFrame(columns=["text"]).to_csv("database.csv", index=False)
-
-def load_database():
-    df = pd.read_csv("database.csv")
-    return df["text"].tolist()
+# Text Plagiarism
+st.subheader("🔍 Check Text Plagiarism")
+text = st.text_area("Enter text to check for plagiarism")
+if st.button("Check Plagiarism"):
+    response = requests.post(f"{HF_BACKEND_URL}/check_text", params={"text": text})
+    st.json(response.json())
 
-def save_to_database(text):
-    df = pd.read_csv("database.csv")
-    new_df = pd.DataFrame({"text": [text]})
-    df = pd.concat([df, new_df], ignore_index=True)
-    df.to_csv("database.csv", index=False)
+# Code Plagiarism
+st.subheader("💻 Check Code Plagiarism")
+code = st.text_area("Paste code to check plagiarism")
+if st.button("Check Code Plagiarism"):
+    response = requests.post(f"{HF_BACKEND_URL}/check_code", params={"code": code})
+    st.json(response.json())
 
-# Streamlit UI
-st.title("Plagiarism Detection System using AI")
-input_text = st.text_area("Enter text to check for plagiarism")
+# AI Detection
+st.subheader("🤖 Detect AI-Generated Content")
+ai_text = st.text_area("Enter text to check AI detection")
+if st.button("Detect AI"):
+    response = requests.post(f"{HF_BACKEND_URL}/detect_ai", params={"text": ai_text})
+    st.json(response.json())
 
-if st.button("Check Plagiarism"):
-    stored_texts = load_database()
-    similarity_score = check_plagiarism(input_text, stored_texts)
-    st.write(f"Plagiarism Score: {similarity_score:.2f}%")
-    
-    if similarity_score < 50:
-        save_to_database(input_text)
-        st.success("Text added to database for future reference.")
+# PDF Upload
+st.subheader("📂 Upload PDF for Plagiarism Check")
+pdf = st.file_uploader("Upload a PDF file", type=["pdf"])
+if pdf:
+    files = {"file": pdf.getvalue()}
+    response = requests.post(f"{HF_BACKEND_URL}/upload_pdf", files=files)
+    st.json(response.json())