Update app.py

app.py

@@ -1,167 +1,185 @@
-import gradio as gr
-import requests
-import os
-from dotenv import load_dotenv
+""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 sklearn.ensemble import RandomForestClassifier
-from sentence_transformers import SentenceTransformer, util
-
-# Load Hugging Face token
-load_dotenv()
-HF_TOKEN = os.getenv("HF_TOKEN")
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-
-# ----------------- FEATURE FUNCTIONS -----------------
-
-def generate_quiz(text, num_questions):
-    prompt = (
-        f"Generate {num_questions} simple quiz questions from the following text:\n\n"
-        f"{text}\n\n"
-        f"Each question should be followed by options A, B, C, D and a correct answer."
-    )
-
-    API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-small"
-    payload = {
-        "inputs": prompt,
-        "parameters": {"max_new_tokens": 300}
-    }
-
-    response = requests.post(API_URL, headers=headers, json=payload)
-    
-    try:
-        result = response.json()
-        if isinstance(result, list) and "generated_text" in result[0]:
-            output = result[0]["generated_text"].strip()
-            return output if output else "⚠️ Model returned empty text. Try reducing question count or using a smaller paragraph."
-        else:
-            return f"⚠️ Model didn't return expected output. Try fewer questions or simpler content."
-    except Exception as e:
-        return f"⚠️ API Error: {e}"
-
-
-
-
-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."
-
-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."
-
-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."
-
-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')}"
-
-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."
-
-def predict_engagement(attendance, login_freq, video_watch):
-    X = np.array([[attendance, login_freq, video_watch]])
-    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"
-
-def generate_badge(score, speed):
-    if score >= 90 and speed <= 30:
-        return "🏅 Gold Badge"
-    elif score >= 75:
-        return "🥈 Silver Badge"
-    else:
-        return "🥉 Bronze Badge"
-
-# ----------------- 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()
+
+# ------------------------------
+
+# Offline Quiz Generator
+
+# ------------------------------
+
+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()
+
+# ------------------------------
+
+# 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
+
+# ------------------------------
+
+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."
+
+# ------------------------------
+
+# 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)
+
+# ------------------------------
+
+# 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"
+
+# ------------------------------
+
+# 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 - offline)
+
+# ------------------------------
+
+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')
+
+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
+
+demo.launch()