Update app.py

app.py

@@ -177,76 +177,90 @@ def main():
     apply_theme(selected_theme)
 
     # --- Title and Welcome ---
-    slider_value = st.slider("AI Plagiarism Detection Tool", min_value=0, max_value=100, value=50)
     st.markdown("<h1 class='welcome-text'>Welcome to AI & Plagiarism Detection</h1>", unsafe_allow_html=True)
 
-    # --- Load Models ---
-    ai_detection_model = load_ai_detection_model()
-    tokenizer, plagiarism_model = load_plagiarism_model()
+    # --- Tabs for File Upload and Text Input ---
+    tab1, tab2 = st.tabs(["Upload File", "Enter Text"])
+
+    with tab1:
+        uploaded_files = st.file_uploader("Upload files (PDF or DOCX)", type=["pdf", "docx"], accept_multiple_files=True)
+        if uploaded_files:
+            for uploaded_file in uploaded_files:
+                file_size = len(uploaded_file.getvalue())
+                if file_size > 1000000000:
+                    st.error(f"{uploaded_file.name}: File size exceeds the 1GB limit.")
+                    continue
+
+                try:
+                    if uploaded_file.type == "application/pdf":
+                        raw_text = extract_text_from_pdf(uploaded_file)
+                    elif uploaded_file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
+                        raw_text = extract_text_from_docx(uploaded_file)
+                    else:
+                        raw_text = None
+                        st.error(f"{uploaded_file.name}: Unsupported file type")
+                        continue
 
-    # --- File Upload ---
-    uploaded_files = st.file_uploader("Upload files (PDF or DOCX)", type=["pdf", "docx"], accept_multiple_files=True)
-
-    if uploaded_files:
-        for uploaded_file in uploaded_files:
-            file_size = len(uploaded_file.getvalue())
-            if file_size > 1000000000:
-                st.error(f"{uploaded_file.name}: File size exceeds the 1GB limit.")
-                continue
-
-            try:
-                if uploaded_file.type == "application/pdf":
-                    raw_text = extract_text_from_pdf(uploaded_file)
-                elif uploaded_file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
-                    raw_text = extract_text_from_docx(uploaded_file)
-                else:
-                    raw_text = None
-                    st.error(f"{uploaded_file.name}: Unsupported file type")
+                except Exception as e:
+                    st.error(f"Error processing {uploaded_file.name}: {e}")
                     continue
 
-            except Exception as e:
-                st.error(f"Error processing {uploaded_file.name}: {e}")
-                continue
-
-            if raw_text:
-                # --- Split text into manageable chunks ---
-                text_chunks = split_text_into_chunks(raw_text, tokenizer)
-
-                # --- AI Detection ---
-                ai_percentage_avg = None
-                human_percentage = None
-                if ai_detection_model:
-                    ai_percentages = detect_ai_content(text_chunks, ai_detection_model)
-                    if ai_percentages:
-                        ai_percentage_avg = sum(ai_percentages) / len(ai_percentages) * 100
-                        human_percentage = 100 - ai_percentage_avg
-
-                # --- Plagiarism Check ---
-                plagiarism_percentage = None
-                if tokenizer and plagiarism_model:
-                    plagiarism_percentage = plagiarism_check(text_chunks, tokenizer, plagiarism_model)
-
-                # --- Tiled Output ---
-                with st.container():
-                    st.markdown(f"<div class='output-box'><h3>{uploaded_file.name}</h3></div>", unsafe_allow_html=True)
-
-                    col1, col2 = st.columns(2)
-
-                    with col1:
-                        st.markdown("<div class='output-box'><h4>AI Detection:</h4></div>", unsafe_allow_html=True)
-                        if ai_percentage_avg is not None:
-                            st.metric(label="AI Content", value=f"{ai_percentage_avg:.2f}%", delta="AI Generated")
-                            st.metric(label="Human Written", value=f"{human_percentage:.2f}%", delta="Humanized Text")
-                        else:
-                            st.write("AI Detection not available")
-
-                    with col2:
-                        st.markdown("<div class='output-box'><h4>Plagiarism Detection:</h4></div>", unsafe_allow_html=True)
-                        if plagiarism_percentage is not None:
-                            st.metric(label="Plagiarism", value=f"{plagiarism_percentage:.2f}%", delta="Plagiarized" if plagiarism_percentage > 0 else "Original")
-                        else:
-                            st.write("Plagiarism Detection not available")
+                if raw_text:
+                    # Split text into manageable chunks
+                    text_chunks = split_text_into_chunks(raw_text, tokenizer)
+                    process_and_display(text_chunks, uploaded_file.name, ai_detection_model, tokenizer, plagiarism_model)
+
+    with tab2:
+        manual_text = st.text_area("Enter text here", "")
+        if manual_text:
+            text_chunks = split_text_into_chunks(manual_text, tokenizer)
+            process_and_display(text_chunks, "Manual Input", ai_detection_model, tokenizer, plagiarism_model)
+
+# --- Helper function to process text and display results ---
+def process_and_display(text_chunks, source_name, ai_detection_model, tokenizer, plagiarism_model):
+    # AI Detection
+    ai_percentage_avg = None
+    human_percentage = None
+    if ai_detection_model:
+        ai_percentages = detect_ai_content(text_chunks, ai_detection_model)
+        if ai_percentages:
+            ai_percentage_avg = sum(ai_percentages) / len(ai_percentages) * 100
+            human_percentage = 100 - ai_percentage_avg
+
+    # Plagiarism Check
+    plagiarism_percentage = None
+    if tokenizer and plagiarism_model:
+        plagiarism_percentage = plagiarism_check(text_chunks, tokenizer, plagiarism_model)
+
+    # --- Tiled Output ---
+    with st.container():
+        st.markdown(f"<div class='output-box'><h3>{source_name}</h3></div>", unsafe_allow_html=True)
+
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.markdown("<div class='output-box'><h4>AI Detection:</h4></div>", unsafe_allow_html=True)
+            if ai_percentage_avg is not None:
+                st.metric(label="AI Content", value=f"{ai_percentage_avg:.2f}%", delta="AI Generated")
+                st.metric(label="Human Written", value=f"{human_percentage:.2f}%", delta="Humanized Text")
+            else:
+                st.write("AI Detection not available")
+
+        with col2:
+            st.markdown("<div class='output-box'><h4>Plagiarism Detection:</h4></div>", unsafe_allow_html=True)
+            if plagiarism_percentage is not None:
+                st.metric(label="Plagiarism", value=f"{plagiarism_percentage:.2f}%", delta="Plagiarized" if plagiarism_percentage > 0 else "Original")
+            else:
+                st.write("Plagiarism Detection not available")
+
+# --- Load models globally ---
+@st.cache_resource
+def load_models():
+    ai_detection_model = load_ai_detection_model()
+    tokenizer, plagiarism_model = load_plagiarism_model()
+    return ai_detection_model, tokenizer, plagiarism_model
 
 # --- Call Main ---
 if __name__ == "__main__":
+    ai_detection_model, tokenizer, plagiarism_model = load_models()  # Load models
     main()