Update app.py

app.py

@@ -1,21 +1,33 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, T5Tokenizer, T5ForConditionalGeneration
+from sentence_transformers import SentenceTransformer, util
 
 # ------------------------------
-# Load Fine-Tuned Question Generator
+# Load Models
 # ------------------------------
-model_name = "iarfmoose/t5-base-question-generator"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+# Fine-tuned quiz generator model
+quiz_model_name = "iarfmoose/t5-base-question-generator"
+quiz_tokenizer = AutoTokenizer.from_pretrained(quiz_model_name)
+quiz_model = AutoModelForSeq2SeqLM.from_pretrained(quiz_model_name)
+
+# For summarizer and fallback tasks
+default_model_name = "t5-base"
+tokenizer_qg = T5Tokenizer.from_pretrained(default_model_name)
+model_qg = T5ForConditionalGeneration.from_pretrained(default_model_name)
+
+# For plagiarism detection
+model_plag = SentenceTransformer('all-MiniLM-L6-v2')
 
 # ------------------------------
-# Better Quiz Generator
+# Quiz Generator (Fine-Tuned)
 # ------------------------------
 def generate_mcqs(text, num_questions=3):
     input_text = f"generate questions: {text.strip()}"
-    input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+    input_ids = quiz_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
 
-    outputs = model.generate(
+    outputs = quiz_model.generate(
         input_ids=input_ids,
         max_length=256,
         num_return_sequences=num_questions,
@@ -24,24 +36,135 @@ def generate_mcqs(text, num_questions=3):
         top_p=0.95
     )
 
-    questions = [tokenizer.decode(out, skip_special_tokens=True).strip() for out in outputs]
+    questions = [quiz_tokenizer.decode(out, skip_special_tokens=True).strip() for out in outputs]
     return "\n".join([f"{i+1}. {q}" for i, q in enumerate(questions)])
 
+# ------------------------------
+# 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()
+
+# ------------------------------
+# Teaching Assistant (Mock)
+# ------------------------------
+def chatbot_response(message, history):
+    return "This is a placeholder response for now. (LLM not integrated)"
+
+# ------------------------------
+# Speech Question Solver (Mock)
+# ------------------------------
+def speech_answer(audio):
+    return "Audio transcription and answer generation not supported offline."
+
+# ------------------------------
+# 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=5.0, 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()
+    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()
+    if avg_score >= 80:
+        return "🏅 Gold Badge"
+    elif avg_score >= 50:
+        return "🥈 Silver Badge"
+    else:
+        return "🥉 Bronze Badge"
+
+# ------------------------------
+# Translator (Mock)
+# ------------------------------
+def translate_text(text, target_lang):
+    return f"(Translated to {target_lang}) - This is a mock translation."
+
+# ------------------------------
+# Plagiarism Checker
+# ------------------------------
+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 Interface
 # ------------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("# 🧠 Quiz Generator (Fine-Tuned T5)")
-    
-    with gr.Row():
+    gr.Markdown("# 📚 Smart LMS Suite (AI Powered Offline Tools)")
+
+    with gr.Tab("🧠 Quiz Generator"):
         quiz_text = gr.Textbox(label="📄 Input Content", lines=6, placeholder="Paste a paragraph here...")
-    
-    with gr.Row():
         quiz_slider = gr.Slider(1, 10, value=3, label="🧾 Number of Questions")
         quiz_btn = gr.Button("🚀 Generate Quiz")
+        quiz_output = gr.Textbox(label="📋 Generated Questions", lines=10)
+        quiz_btn.click(fn=generate_mcqs, inputs=[quiz_text, quiz_slider], outputs=quiz_output)
 
-    quiz_output = gr.Textbox(label="📋 Generated Questions", lines=10)
+    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)
 
-    quiz_btn.click(fn=generate_mcqs, inputs=[quiz_text, quiz_slider], outputs=quiz_output)
+    with gr.Tab("🤖 Teaching Assistant"):
+        gr.ChatInterface(fn=chatbot_response)
 
+    with gr.Tab("🎤 Speech Q Solver"):
+        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"):
+        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 the App
+# ------------------------------
 demo.launch()