Update app.py

app.py

@@ -1,185 +1,152 @@
 import gradio as gr
 import pandas as pd
-import matplotlib.pyplot as plt
 import torch
 from transformers import T5Tokenizer, T5ForConditionalGeneration
-from sentence\_transformers import SentenceTransformer, util
-import numpy as np
+from sentence_transformers import SentenceTransformer, util
 
 # ------------------------------
-
 # Offline Quiz Generator
-
 # ------------------------------
+model_qg = T5ForConditionalGeneration.from_pretrained("t5-base")
+tokenizer_qg = T5Tokenizer.from_pretrained("t5-base")
 
-model\_qg = T5ForConditionalGeneration.from\_pretrained("t5-base")
-tokenizer\_qg = T5Tokenizer.from\_pretrained("t5-base")
-
-def generate\_mcqs(text, num\_questions=3):
-input\_text = f"generate questions: {text}"
-input\_ids = tokenizer\_qg.encode(input\_text, return\_tensors="pt", max\_length=512, truncation=True)
-outputs = model\_qg.generate(input\_ids=input\_ids, max\_length=256, num\_return\_sequences=1)
-return tokenizer\_qg.decode(outputs\[0], skip\_special\_tokens=True).strip()
+def generate_mcqs(text, num_questions=3):
+    input_text = f"generate questions: {text}"
+    input_ids = tokenizer_qg.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model_qg.generate(input_ids=input_ids, max_length=256, num_return_sequences=1)
+    return tokenizer_qg.decode(outputs[0], skip_special_tokens=True).strip()
 
 # ------------------------------
-
 # Weakness Analyzer
-
 # ------------------------------
-
-def analyze\_weakness(csv\_file):
-df = pd.read\_csv(csv\_file.name)
-summary = df.groupby("Topic")\["Score"].mean().sort\_values()
-return summary.to\_string()
+def analyze_weakness(csv_file):
+    df = pd.read_csv(csv_file.name)
+    summary = df.groupby("Topic")["Score"].mean().sort_values()
+    return summary.to_string()
 
 # ------------------------------
-
 # Teaching Assistant
-
 # ------------------------------
-
-def chatbot\_response(message, history):
-return "This is a placeholder response for now. (LLM not integrated)"
+def chatbot_response(message, history):
+    return "This is a placeholder response for now. (LLM not integrated)"
 
 # ------------------------------
-
 # Speech Question Solver
-
 # ------------------------------
-
-def speech\_answer(audio):
-return "Audio to text transcription + answer generation is not included in offline version."
+def speech_answer(audio):
+    return "Audio to text transcription + answer generation is not included in offline version."
 
 # ------------------------------
-
 # PDF/YT 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)
-return tokenizer\_qg.decode(summary\_ids\[0], skip\_special\_tokens=True)
+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)
+    return tokenizer_qg.decode(summary_ids[0], skip_special_tokens=True)
 
 # ------------------------------
-
 # Engagement Predictor (Mock)
-
 # ------------------------------
-
-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"
+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"
 
 # ------------------------------
-
 # Badge Generator
-
 # ------------------------------
-
-def generate\_badge(file):
-df = pd.read\_csv(file.name)
-avg\_score = df\['Score'].mean()
-if avg\_score >= 80:
-return "🏅 Gold Badge"
-elif avg\_score >= 50:
-return "🥈 Silver Badge"
-else:
-return "🥉 Bronze Badge"
+def generate_badge(file):
+    df = pd.read_csv(file.name)
+    avg_score = df['Score'].mean()
+    if avg_score >= 80:
+        return "🏅 Gold Badge"
+    elif avg_score >= 50:
+        return "🥈 Silver Badge"
+    else:
+        return "🥉 Bronze Badge"
 
 # ------------------------------
-
-# Translator (Mock - offline)
-
+# Translator (Mock)
 # ------------------------------
-
-def translate\_text(text, target\_lang):
-return f"(Translated to {target\_lang}) - This is a mock translation."
+def translate_text(text, target_lang):
+    return f"(Translated to {target_lang}) - This is a mock translation."
 
 # ------------------------------
-
 # Plagiarism Checker
-
 # ------------------------------
+model_plag = SentenceTransformer('all-MiniLM-L6-v2')
 
-model\_plag = SentenceTransformer('all-MiniLM-L6-v2')
-
-def check\_plagiarism(text1, text2):
-emb1 = model\_plag.encode(text1, convert\_to\_tensor=True)
-emb2 = model\_plag.encode(text2, convert\_to\_tensor=True)
-score = util.cos\_sim(emb1, emb2).item()
-return f"Similarity Score: {score:.2f} - {'⚠️ Possible Plagiarism' if score > 0.8 else '✅ Looks Original'}"
+def check_plagiarism(text1, text2):
+    emb1 = model_plag.encode(text1, convert_to_tensor=True)
+    emb2 = model_plag.encode(text2, convert_to_tensor=True)
+    score = util.cos_sim(emb1, emb2).item()
+    return f"Similarity Score: {score:.2f} - {'⚠️ Possible Plagiarism' if score > 0.8 else '✅ Looks Original'}"
 
 # ------------------------------
-
 # Gradio UI
-
 # ------------------------------
-
 with gr.Blocks() as demo:
-gr.Markdown("# 📚 AI-Powered LMS Suite (Offline Mode)")
-
-```
-with gr.Tab("🧠 Quiz Generator"):
-    quiz_text = gr.Textbox(label="Content", lines=5)
-    quiz_slider = gr.Slider(1, 10, value=3, label="Number of Questions")
-    quiz_btn = gr.Button("Generate Quiz")
-    quiz_out = gr.Textbox(label="Generated Quiz")
-    quiz_btn.click(fn=generate_mcqs, inputs=[quiz_text, quiz_slider], outputs=quiz_out)
-
-with gr.Tab("📉 Weakness Analyzer"):
-    weak_file = gr.File(label="Upload CSV with Topic & Score columns")
-    weak_btn = gr.Button("Analyze")
-    weak_out = gr.Textbox(label="Analysis")
-    weak_btn.click(fn=analyze_weakness, inputs=weak_file, outputs=weak_out)
-
-with gr.Tab("🤖 Teaching Assistant"):
-    chat = 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_btn.click(fn=speech_answer, inputs=audio_in, outputs=audio_out)
-
-with gr.Tab("📄 Summarizer"):
-    sum_text = gr.Textbox(lines=5, label="Paste Text")
-    sum_btn = gr.Button("Summarize")
-    sum_out = gr.Textbox(label="Summary")
-    sum_btn.click(fn=summarize_text, inputs=sum_text, outputs=sum_out)
-
-with gr.Tab("📊 Engagement Predictor"):
-    eng_file = gr.File(label="Upload CSV with TimeSpent column")
-    eng_btn = gr.Button("Predict")
-    eng_out = gr.Textbox()
-    eng_btn.click(fn=predict_engagement, inputs=eng_file, outputs=eng_out)
-
-with gr.Tab("🏅 Badge Generator"):
-    badge_file = gr.File(label="Upload CSV with Score column")
-    badge_btn = gr.Button("Get Badge")
-    badge_out = gr.Textbox()
-    badge_btn.click(fn=generate_badge, inputs=badge_file, outputs=badge_out)
-
-with gr.Tab("🌍 Translator"):
-    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
-
+    gr.Markdown("# 📚 AI-Powered LMS Suite (Offline Mode)")
+
+    with gr.Tab("🧠 Quiz Generator"):
+        quiz_text = gr.Textbox(label="Content", lines=5)
+        quiz_slider = gr.Slider(1, 10, value=3, label="Number of Questions")
+        quiz_btn = gr.Button("Generate Quiz")
+        quiz_out = gr.Textbox(label="Generated Quiz")
+        quiz_btn.click(fn=generate_mcqs, inputs=[quiz_text, quiz_slider], outputs=quiz_out)
+
+    with gr.Tab("📉 Weakness Analyzer"):
+        weak_file = gr.File(label="Upload CSV with Topic & Score columns")
+        weak_btn = gr.Button("Analyze")
+        weak_out = gr.Textbox(label="Analysis")
+        weak_btn.click(fn=analyze_weakness, inputs=weak_file, outputs=weak_out)
+
+    with gr.Tab("🤖 Teaching Assistant"):
+        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_btn.click(fn=speech_answer, inputs=audio_in, outputs=audio_out)
+
+    with gr.Tab("📄 Summarizer"):
+        sum_text = gr.Textbox(lines=5, label="Paste Text")
+        sum_btn = gr.Button("Summarize")
+        sum_out = gr.Textbox(label="Summary")
+        sum_btn.click(fn=summarize_text, inputs=sum_text, outputs=sum_out)
+
+    with gr.Tab("📊 Engagement Predictor"):
+        eng_file = gr.File(label="Upload CSV with TimeSpent column")
+        eng_btn = gr.Button("Predict")
+        eng_out = gr.Textbox()
+        eng_btn.click(fn=predict_engagement, inputs=eng_file, outputs=eng_out)
+
+    with gr.Tab("🏅 Badge Generator"):
+        badge_file = gr.File(label="Upload CSV with Score column")
+        badge_btn = gr.Button("Get Badge")
+        badge_out = gr.Textbox()
+        badge_btn.click(fn=generate_badge, inputs=badge_file, outputs=badge_out)
+
+    with gr.Tab("🌍 Translator"):
+        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()