Update app.py

app.py

@@ -25,24 +25,24 @@ def analyze_weakness(csv_file):
     return summary.to_string()
 
 # ------------------------------
-# Teaching Assistant
+# Teaching Assistant (Placeholder)
 # ------------------------------
 def chatbot_response(message, history):
     return "This is a placeholder response for now. (LLM not integrated)"
 
 # ------------------------------
-# Speech Question Solver
+# Speech Question Solver (Mock)
 # ------------------------------
 def speech_answer(audio):
-    return "Audio to text transcription + answer generation is not included in offline version."
+    return "Audio transcription and answer generation not supported offline."
 
 # ------------------------------
-# PDF/YT Summarizer
+# Summarizer
 # ------------------------------
 def summarize_text(text):
     input_text = f"summarize: {text.strip()}"
     input_ids = tokenizer_qg.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
-    summary_ids = model_qg.generate(input_ids, max_length=150, min_length=30, length_penalty=5., num_beams=2)
+    summary_ids = model_qg.generate(input_ids, max_length=150, min_length=30, length_penalty=5.0, num_beams=2)
     return tokenizer_qg.decode(summary_ids[0], skip_special_tokens=True)
 
 # ------------------------------
@@ -50,18 +50,15 @@ def summarize_text(text):
 # ------------------------------
 def predict_engagement(file):
     df = pd.read_csv(file.name)
-    avg_time = df['TimeSpent'].mean()
-    if avg_time < 10:
-        return "⚠️ Risk of disengagement"
-    else:
-        return "✅ Engaged student"
+    avg_time = df["TimeSpent"].mean()
+    return "✅ Engaged student" if avg_time >= 10 else "⚠️ Risk of disengagement"
 
 # ------------------------------
 # Badge Generator
 # ------------------------------
 def generate_badge(file):
     df = pd.read_csv(file.name)
-    avg_score = df['Score'].mean()
+    avg_score = df["Score"].mean()
     if avg_score >= 80:
         return "🏅 Gold Badge"
     elif avg_score >= 50:
@@ -87,7 +84,7 @@ def check_plagiarism(text1, text2):
     return f"Similarity Score: {score:.2f} - {'⚠️ Possible Plagiarism' if score > 0.8 else '✅ Looks Original'}"
 
 # ------------------------------
-# Gradio UI
+# Gradio Interface
 # ------------------------------
 with gr.Blocks() as demo:
     gr.Markdown("# 📚 AI-Powered LMS Suite (Offline Mode)")
@@ -109,9 +106,9 @@ with gr.Blocks() as demo:
         gr.ChatInterface(fn=chatbot_response)
 
     with gr.Tab("🎤 Speech Q Solver"):
-        audio_in = gr.Audio(source="microphone", type="filepath")
-        audio_btn = gr.Button("Answer")
-        audio_out = gr.Textbox()
+        audio_in = gr.Audio(label="Upload Audio", type="filepath")
+        audio_btn = gr.Button("Get Answer")
+        audio_out = gr.Textbox(label="Answer")
         audio_btn.click(fn=speech_answer, inputs=audio_in, outputs=audio_out)
 
     with gr.Tab("📄 Summarizer"):
@@ -136,19 +133,4 @@ with gr.Blocks() as demo:
         trans_in = gr.Textbox(label="Enter Text")
         trans_lang = gr.Textbox(label="Target Language")
         trans_btn = gr.Button("Translate")
-        trans_out = gr.Textbox()
-        trans_btn.click(fn=translate_text, inputs=[trans_in, trans_lang], outputs=trans_out)
-
-    with gr.Tab("📋 Plagiarism Checker"):
-        text1 = gr.Textbox(label="Text 1", lines=3)
-        text2 = gr.Textbox(label="Text 2", lines=3)
-        plag_btn = gr.Button("Check Similarity")
-        plag_out = gr.Textbox()
-        plag_btn.click(fn=check_plagiarism, inputs=[text1, text2], outputs=plag_out)
-
-# ------------------------------
-# Launch App
-# ------------------------------
-demo.launch()
-
-
+        tr