Update app.py

app.py

@@ -1,103 +1,155 @@
 import gradio as gr
-from utils.quiz_generator import generate_quiz
-from utils.chatbot import ask_ai
-from utils.summarizer import summarize_text, get_youtube_transcript, extract_text_from_pdf
-from utils.translator import translate_text
-from utils.plagiarism_checker import check_plagiarism
-from utils.weakness_analyzer import analyze_weakness
-from utils.engagement_predictor import predict_engagement
-from utils.badge import assign_badges
-
-# Chat history for chatbot
-chat_history = []
-
-# ------------------- Gradio UI -------------------
-
-with gr.Blocks(title="📚 Smart LMS AI Suite") as demo:
-
-    gr.Markdown("# 🎓 Smart LMS AI Suite\nYour AI-powered Learning Assistant 🚀")
-
-    with gr.Tabs():
-        
-        # 1. Quiz Generator
-        with gr.TabItem("🧠 Quiz Generator"):
-            with gr.Row():
-                topic_input = gr.Textbox(label="Paste Topic Content")
-                num_qs = gr.Slider(1, 10, value=5, label="Number of Questions")
-            quiz_output = gr.Textbox(label="Generated Quiz")
-            quiz_button = gr.Button("Generate Quiz")
-            quiz_button.click(fn=generate_quiz, inputs=[topic_input, num_qs], outputs=quiz_output)
-
-        # 2. AI Teaching Assistant
-        with gr.TabItem("🤖 AI Teaching Assistant"):
-            with gr.Row():
-                question_input = gr.Textbox(label="Ask your question")
-                chatbot_output = gr.Textbox(label="AI Answer")
-            chat_button = gr.Button("Ask")
-            def chat_interface(q):
-                global chat_history
-                response, chat_history = ask_ai(q, chat_history)
-                return response
-            chat_button.click(fn=chat_interface, inputs=question_input, outputs=chatbot_output)
-
-        # 3. Summarizer
-        with gr.TabItem("📄 Summarizer"):
-            with gr.Row():
-                summarizer_input = gr.Textbox(lines=4, label="Paste Text to Summarize")
-                yt_url = gr.Textbox(label="Or Enter YouTube Video URL")
-                pdf_input = gr.File(label="Or Upload PDF", file_types=[".pdf"])
-            summary_output = gr.Textbox(label="Summary")
-            summary_button = gr.Button("Summarize")
-
-            def summarize_all(text, url, pdf):
-                if pdf:
-                    content = extract_text_from_pdf(pdf.name)
-                elif url:
-                    content = get_youtube_transcript(url)
-                else:
-                    content = text
-                return summarize_text(content)
-                
-            summary_button.click(fn=summarize_all, inputs=[summarizer_input, yt_url, pdf_input], outputs=summary_output)
-
-        # 4. Translator
-        with gr.TabItem("🌍 Translator"):
-            text_input = gr.Textbox(label="Enter English Text")
-            lang = gr.Dropdown(choices=["te", "hi", "ta", "bn", "kn", "gu", "ur"], label="Target Language")
-            translated = gr.Textbox(label="Translated Text")
-            translate_button = gr.Button("Translate")
-            translate_button.click(fn=translate_text, inputs=[text_input, lang], outputs=translated)
-
-        # 5. Plagiarism Checker
-        with gr.TabItem("🕵️ Plagiarism Checker"):
-            with gr.Row():
-                plag_input1 = gr.Textbox(label="Text 1")
-                plag_input2 = gr.Textbox(label="Text 2")
-            plag_output = gr.Textbox(label="Plagiarism Result")
-            plag_btn = gr.Button("Check")
-            plag_btn.click(fn=check_plagiarism, inputs=[plag_input1, plag_input2], outputs=plag_output)
-
-        # 6. Weakness Analyzer
-        with gr.TabItem("📉 Weakness Analyzer"):
-            weakness_file = gr.File(label="Upload Quiz Scores CSV")
-            weakness_result = gr.Textbox(label="Analysis Result")
-            weakness_btn = gr.Button("Analyze")
-            weakness_btn.click(fn=analyze_weakness, inputs=weakness_file, outputs=weakness_result)
-
-        # 7. Engagement Predictor
-        with gr.TabItem("📊 Engagement Predictor"):
-            engagement_file = gr.File(label="Upload Student Log CSV")
-            engagement_output = gr.Textbox(label="Prediction Report")
-            engagement_btn = gr.Button("Predict Engagement")
-            engagement_btn.click(fn=predict_engagement, inputs=engagement_file, outputs=engagement_output)
-
-        # 8. Badge Generator
-        with gr.TabItem("🏅 Badge Generator"):
-            badge_file = gr.File(label="Upload Performance CSV")
-            badge_output = gr.Textbox(label="Badge Report")
-            badge_btn = gr.Button("Generate Badges")
-            badge_btn.click(fn=assign_badges, inputs=badge_file, outputs=badge_output)
-
-
-# 🔁 Launch
-demo.launch()
+import requests
+import os
+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_dotenv()
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+# ----------------- QUIZ GENERATOR -----------------
+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}}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    try:
+        result = response.json()[0]["generated_text"]
+        return f"{num_questions} Sample Questions:\n\n{result}"
+    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}}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    try:
+        return response.json()[0]["generated_text"].split("AI:")[-1].strip()
+    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}}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    try:
+        return response.json()[0]["summary_text"]
+    except:
+        return "⚠️ Unable to summarize content."
+
+# ----------------- TRANSLATOR -----------------
+def translate_text(text, target_lang="te"):
+    API_URL = "https://libretranslate.de/translate"
+    payload = {
+        "q": text,
+        "source": "en",
+        "target": target_lang,
+        "format": "text"
+    }
+    response = requests.post(API_URL, data=payload)
+    try:
+        return response.json()["translatedText"]
+    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)
+        topic_scores = df.groupby('Topic')['Score'].mean().sort_values()
+        weakest = topic_scores.head(3)
+        return f"Weak Areas:\n{weakest.to_string()}"
+    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
+    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"
+    elif score >= 75:
+        return "🥈 Silver Badge"
+    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()