Update app.py

app.py

@@ -5,16 +5,15 @@ from dotenv import load_dotenv
 import pandas as pd
 import numpy as np
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import LabelEncoder
 from sentence_transformers import SentenceTransformer, util
 
-# Load environment variables
+# Load Hugging Face token
 load_dotenv()
 HF_TOKEN = os.getenv("HF_TOKEN")
-
 headers = {"Authorization": f"Bearer {HF_TOKEN}"}
 
-# ----------------- QUIZ GENERATOR -----------------
+# ----------------- FEATURE FUNCTIONS -----------------
+
 def generate_quiz(text, num_questions):
     API_URL = "https://api-inference.huggingface.co/models/mrm8488/t5-base-finetuned-question-generation-ap"
     payload = {"inputs": f"generate questions: {text}", "parameters": {"max_length": 256}}
@@ -25,7 +24,6 @@ def generate_quiz(text, num_questions):
     except:
         return "⚠️ Failed to generate quiz. Try again later."
 
-# ----------------- AI TEACHING ASSISTANT -----------------
 def get_bot_response(query):
     API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
     payload = {"inputs": f"Student: {query}\nAI:", "parameters": {"max_new_tokens": 150}}
@@ -35,7 +33,6 @@ def get_bot_response(query):
     except:
         return "⚠️ AI Assistant unavailable right now."
 
-# ----------------- SUMMARIZER -----------------
 def summarize_text(text):
     API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
     payload = {"inputs": text, "parameters": {"max_length": 120}}
@@ -45,7 +42,6 @@ def summarize_text(text):
     except:
         return "⚠️ Unable to summarize content."
 
-# ----------------- TRANSLATOR -----------------
 def translate_text(text, target_lang="te"):
     API_URL = "https://libretranslate.de/translate"
     payload = {
@@ -60,14 +56,12 @@ def translate_text(text, target_lang="te"):
     except:
         return "⚠️ Translation failed."
 
-# ----------------- PLAGIARISM CHECKER -----------------
 def check_plagiarism(text1, text2):
     model = SentenceTransformer('all-MiniLM-L6-v2')
     embeddings = model.encode([text1, text2], convert_to_tensor=True)
     similarity = util.pytorch_cos_sim(embeddings[0], embeddings[1]).item()
     return f"Similarity Score: {similarity:.2f}\n{('⚠️ Possible Plagiarism' if similarity > 0.75 else '✅ No significant overlap')}"
 
-# ----------------- WEAKNESS ANALYZER -----------------
 def analyze_weakness(file):
     try:
         df = pd.read_csv(file.name)
@@ -77,16 +71,14 @@ def analyze_weakness(file):
     except:
         return "⚠️ Failed to analyze file. Ensure it contains 'Topic' and 'Score' columns."
 
-# ----------------- ENGAGEMENT PREDICTOR -----------------
 def predict_engagement(attendance, login_freq, video_watch):
     X = np.array([[attendance, login_freq, video_watch]])
-    y = [0, 1, 1, 0, 1]  # Mock labels: 1 = Engaged, 0 = Disengaged
+    y = [0, 1, 1, 0, 1]
     X_train = np.array([[90, 5, 80], [85, 4, 90], [95, 6, 85], [60, 2, 40], [88, 3, 75]])
     clf = RandomForestClassifier().fit(X_train, y)
     prediction = clf.predict(X)[0]
     return "✅ Likely to be Engaged" if prediction else "⚠️ At Risk of Disengagement"
 
-# ----------------- BADGE GENERATOR -----------------
 def generate_badge(score, speed):
     if score >= 90 and speed <= 30:
         return "🏅 Gold Badge"
@@ -95,61 +87,63 @@ def generate_badge(score, speed):
     else:
         return "🥉 Bronze Badge"
 
-# ----------------- INTERFACES -----------------
-
-with gr.Tab("🧠 Quiz Generator"):
-    quiz_text = gr.Textbox(label="Paste Topic Content")
-    quiz_slider = gr.Slider(1, 10, label="Number of Questions", value=3)
-    quiz_output = gr.Textbox(label="Generated Quiz")
-    quiz_button = gr.Button("Generate Quiz")
-    quiz_button.click(fn=generate_quiz, inputs=[quiz_text, quiz_slider], outputs=quiz_output)
-
-with gr.Tab("🤖 AI Teaching Assistant"):
-    bot_input = gr.Textbox(label="Ask a Question")
-    bot_output = gr.Textbox(label="AI Answer")
-    bot_button = gr.Button("Get Answer")
-    bot_button.click(fn=get_bot_response, inputs=bot_input, outputs=bot_output)
-
-with gr.Tab("📄 Summarizer"):
-    sum_input = gr.Textbox(label="Paste Content")
-    sum_output = gr.Textbox(label="Summary")
-    sum_button = gr.Button("Summarize")
-    sum_button.click(fn=summarize_text, inputs=sum_input, outputs=sum_output)
-
-with gr.Tab("🌍 Translator"):
-    trans_input = gr.Textbox(label="Text in English")
-    lang_dropdown = gr.Dropdown(["te", "hi", "ta", "fr"], value="te", label="Target Language Code")
-    trans_output = gr.Textbox(label="Translated Text")
-    trans_button = gr.Button("Translate")
-    trans_button.click(fn=translate_text, inputs=[trans_input, lang_dropdown], outputs=trans_output)
-
-with gr.Tab("🧾 Plagiarism Checker"):
-    plag_1 = gr.Textbox(label="Document 1")
-    plag_2 = gr.Textbox(label="Document 2")
-    plag_out = gr.Textbox(label="Result")
-    plag_btn = gr.Button("Check Plagiarism")
-    plag_btn.click(fn=check_plagiarism, inputs=[plag_1, plag_2], outputs=plag_out)
-
-with gr.Tab("📉 Weakness Analyzer"):
-    csv_input = gr.File(label="Upload CSV with 'Topic' and 'Score' Columns")
-    weak_out = gr.Textbox(label="Weak Topics")
-    weak_btn = gr.Button("Analyze")
-    weak_btn.click(fn=analyze_weakness, inputs=csv_input, outputs=weak_out)
-
-with gr.Tab("📊 Engagement Predictor"):
-    att = gr.Slider(0, 100, value=85, label="Attendance %")
-    login = gr.Slider(0, 10, value=5, label="Login Frequency")
-    video = gr.Slider(0, 100, value=80, label="Video Watch %")
-    engage_out = gr.Textbox(label="Prediction")
-    engage_btn = gr.Button("Predict")
-    engage_btn.click(fn=predict_engagement, inputs=[att, login, video], outputs=engage_out)
-
-with gr.Tab("🏅 Badge Generator"):
-    score = gr.Slider(0, 100, label="Score")
-    speed = gr.Slider(0, 60, label="Time Taken (mins)")
-    badge_out = gr.Textbox(label="Badge Awarded")
-    badge_btn = gr.Button("Generate Badge")
-    badge_btn.click(fn=generate_badge, inputs=[score, speed], outputs=badge_out)
-
-gr.Markdown("🚀 Built with Hugging Face, LibreTranslate, Gradio")
-gr.launch()
+# ----------------- UI -----------------
+with gr.Blocks(title="Smart LMS AI Suite") as app:
+
+    with gr.Tab("🧠 Quiz Generator"):
+        quiz_text = gr.Textbox(label="Paste Topic Content")
+        quiz_slider = gr.Slider(1, 10, label="Number of Questions", value=3)
+        quiz_output = gr.Textbox(label="Generated Quiz")
+        quiz_button = gr.Button("Generate Quiz")
+        quiz_button.click(fn=generate_quiz, inputs=[quiz_text, quiz_slider], outputs=quiz_output)
+
+    with gr.Tab("🤖 AI Teaching Assistant"):
+        bot_input = gr.Textbox(label="Ask a Question")
+        bot_output = gr.Textbox(label="AI Answer")
+        bot_button = gr.Button("Get Answer")
+        bot_button.click(fn=get_bot_response, inputs=bot_input, outputs=bot_output)
+
+    with gr.Tab("📄 Summarizer"):
+        sum_input = gr.Textbox(label="Paste Content")
+        sum_output = gr.Textbox(label="Summary")
+        sum_button = gr.Button("Summarize")
+        sum_button.click(fn=summarize_text, inputs=sum_input, outputs=sum_output)
+
+    with gr.Tab("🌍 Translator"):
+        trans_input = gr.Textbox(label="Text in English")
+        lang_dropdown = gr.Dropdown(["te", "hi", "ta", "fr"], value="te", label="Target Language Code")
+        trans_output = gr.Textbox(label="Translated Text")
+        trans_button = gr.Button("Translate")
+        trans_button.click(fn=translate_text, inputs=[trans_input, lang_dropdown], outputs=trans_output)
+
+    with gr.Tab("🧾 Plagiarism Checker"):
+        plag_1 = gr.Textbox(label="Document 1")
+        plag_2 = gr.Textbox(label="Document 2")
+        plag_out = gr.Textbox(label="Result")
+        plag_btn = gr.Button("Check Plagiarism")
+        plag_btn.click(fn=check_plagiarism, inputs=[plag_1, plag_2], outputs=plag_out)
+
+    with gr.Tab("📉 Weakness Analyzer"):
+        csv_input = gr.File(label="Upload CSV with 'Topic' and 'Score' Columns")
+        weak_out = gr.Textbox(label="Weak Topics")
+        weak_btn = gr.Button("Analyze")
+        weak_btn.click(fn=analyze_weakness, inputs=csv_input, outputs=weak_out)
+
+    with gr.Tab("📊 Engagement Predictor"):
+        att = gr.Slider(0, 100, value=85, label="Attendance %")
+        login = gr.Slider(0, 10, value=5, label="Login Frequency")
+        video = gr.Slider(0, 100, value=80, label="Video Watch %")
+        engage_out = gr.Textbox(label="Prediction")
+        engage_btn = gr.Button("Predict")
+        engage_btn.click(fn=predict_engagement, inputs=[att, login, video], outputs=engage_out)
+
+    with gr.Tab("🏅 Badge Generator"):
+        score = gr.Slider(0, 100, label="Score")
+        speed = gr.Slider(0, 60, label="Time Taken (mins)")
+        badge_out = gr.Textbox(label="Badge Awarded")
+        badge_btn = gr.Button("Generate Badge")
+        badge_btn.click(fn=generate_badge, inputs=[score, speed], outputs=badge_out)
+
+    gr.Markdown("🚀 Built using Hugging Face, Gradio, and Free APIs")
+
+app.launch()