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import gradio as gr |
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import requests |
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import os |
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from dotenv import load_dotenv |
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import pandas as pd |
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import numpy as np |
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from sklearn.ensemble import RandomForestClassifier |
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from sentence_transformers import SentenceTransformer, util |
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load_dotenv() |
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HF_TOKEN = os.getenv("HF_TOKEN") |
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headers = {"Authorization": f"Bearer {HF_TOKEN}"} |
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def generate_quiz(text, num_questions): |
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prompt = ( |
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f"Generate {num_questions} simple quiz questions from the following text:\n\n" |
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f"{text}\n\n" |
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f"Each question should be followed by options A, B, C, D and a correct answer." |
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) |
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API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-small" |
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payload = { |
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"inputs": prompt, |
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"parameters": {"max_new_tokens": 300} |
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} |
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response = requests.post(API_URL, headers=headers, json=payload) |
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try: |
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result = response.json() |
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if isinstance(result, list) and "generated_text" in result[0]: |
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output = result[0]["generated_text"].strip() |
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return output if output else "⚠️ Model returned empty text. Try reducing question count or using a smaller paragraph." |
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else: |
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return f"⚠️ Model didn't return expected output. Try fewer questions or simpler content." |
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except Exception as e: |
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return f"⚠️ API Error: {e}" |
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def get_bot_response(query): |
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API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta" |
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payload = {"inputs": f"Student: {query}\nAI:", "parameters": {"max_new_tokens": 150}} |
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response = requests.post(API_URL, headers=headers, json=payload) |
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try: |
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return response.json()[0]["generated_text"].split("AI:")[-1].strip() |
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except: |
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return "⚠️ AI Assistant unavailable right now." |
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def summarize_text(text): |
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API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn" |
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payload = {"inputs": text, "parameters": {"max_length": 120}} |
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response = requests.post(API_URL, headers=headers, json=payload) |
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try: |
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return response.json()[0]["summary_text"] |
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except: |
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return "⚠️ Unable to summarize content." |
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def translate_text(text, target_lang="te"): |
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API_URL = "https://libretranslate.de/translate" |
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payload = { |
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"q": text, |
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"source": "en", |
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"target": target_lang, |
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"format": "text" |
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} |
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response = requests.post(API_URL, data=payload) |
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try: |
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return response.json()["translatedText"] |
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except: |
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return "⚠️ Translation failed." |
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def check_plagiarism(text1, text2): |
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model = SentenceTransformer('all-MiniLM-L6-v2') |
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embeddings = model.encode([text1, text2], convert_to_tensor=True) |
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similarity = util.pytorch_cos_sim(embeddings[0], embeddings[1]).item() |
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return f"Similarity Score: {similarity:.2f}\n{('⚠️ Possible Plagiarism' if similarity > 0.75 else '✅ No significant overlap')}" |
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def analyze_weakness(file): |
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try: |
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df = pd.read_csv(file.name) |
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topic_scores = df.groupby('Topic')['Score'].mean().sort_values() |
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weakest = topic_scores.head(3) |
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return f"Weak Areas:\n{weakest.to_string()}" |
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except: |
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return "⚠️ Failed to analyze file. Ensure it contains 'Topic' and 'Score' columns." |
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def predict_engagement(attendance, login_freq, video_watch): |
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X = np.array([[attendance, login_freq, video_watch]]) |
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y = [0, 1, 1, 0, 1] |
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X_train = np.array([[90, 5, 80], [85, 4, 90], [95, 6, 85], [60, 2, 40], [88, 3, 75]]) |
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clf = RandomForestClassifier().fit(X_train, y) |
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prediction = clf.predict(X)[0] |
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return "✅ Likely to be Engaged" if prediction else "⚠️ At Risk of Disengagement" |
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def generate_badge(score, speed): |
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if score >= 90 and speed <= 30: |
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return "🏅 Gold Badge" |
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elif score >= 75: |
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return "🥈 Silver Badge" |
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else: |
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return "🥉 Bronze Badge" |
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with gr.Blocks(title="Smart LMS AI Suite") as app: |
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with gr.Tab("🧠 Quiz Generator"): |
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quiz_text = gr.Textbox(label="Paste Topic Content") |
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quiz_slider = gr.Slider(1, 10, label="Number of Questions", value=3) |
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quiz_output = gr.Textbox(label="Generated Quiz") |
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quiz_button = gr.Button("Generate Quiz") |
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quiz_button.click(fn=generate_quiz, inputs=[quiz_text, quiz_slider], outputs=quiz_output) |
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with gr.Tab("🤖 AI Teaching Assistant"): |
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bot_input = gr.Textbox(label="Ask a Question") |
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bot_output = gr.Textbox(label="AI Answer") |
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bot_button = gr.Button("Get Answer") |
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bot_button.click(fn=get_bot_response, inputs=bot_input, outputs=bot_output) |
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with gr.Tab("📄 Summarizer"): |
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sum_input = gr.Textbox(label="Paste Content") |
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sum_output = gr.Textbox(label="Summary") |
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sum_button = gr.Button("Summarize") |
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sum_button.click(fn=summarize_text, inputs=sum_input, outputs=sum_output) |
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with gr.Tab("🌍 Translator"): |
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trans_input = gr.Textbox(label="Text in English") |
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lang_dropdown = gr.Dropdown(["te", "hi", "ta", "fr"], value="te", label="Target Language Code") |
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trans_output = gr.Textbox(label="Translated Text") |
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trans_button = gr.Button("Translate") |
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trans_button.click(fn=translate_text, inputs=[trans_input, lang_dropdown], outputs=trans_output) |
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with gr.Tab("🧾 Plagiarism Checker"): |
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plag_1 = gr.Textbox(label="Document 1") |
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plag_2 = gr.Textbox(label="Document 2") |
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plag_out = gr.Textbox(label="Result") |
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plag_btn = gr.Button("Check Plagiarism") |
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plag_btn.click(fn=check_plagiarism, inputs=[plag_1, plag_2], outputs=plag_out) |
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with gr.Tab("📉 Weakness Analyzer"): |
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csv_input = gr.File(label="Upload CSV with 'Topic' and 'Score' Columns") |
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weak_out = gr.Textbox(label="Weak Topics") |
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weak_btn = gr.Button("Analyze") |
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weak_btn.click(fn=analyze_weakness, inputs=csv_input, outputs=weak_out) |
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with gr.Tab("📊 Engagement Predictor"): |
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att = gr.Slider(0, 100, value=85, label="Attendance %") |
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login = gr.Slider(0, 10, value=5, label="Login Frequency") |
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video = gr.Slider(0, 100, value=80, label="Video Watch %") |
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engage_out = gr.Textbox(label="Prediction") |
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engage_btn = gr.Button("Predict") |
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engage_btn.click(fn=predict_engagement, inputs=[att, login, video], outputs=engage_out) |
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with gr.Tab("🏅 Badge Generator"): |
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score = gr.Slider(0, 100, label="Score") |
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speed = gr.Slider(0, 60, label="Time Taken (mins)") |
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badge_out = gr.Textbox(label="Badge Awarded") |
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badge_btn = gr.Button("Generate Badge") |
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badge_btn.click(fn=generate_badge, inputs=[score, speed], outputs=badge_out) |
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gr.Markdown("🚀 Built using Hugging Face, Gradio, and Free APIs") |
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app.launch() |
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