import gradio as gr
from transformers import pipeline
import PyPDF2
import re
import os
import io
import random
import time
from groq import Groq
# ==================== DEBUGGING ====================
print("=" * 50)
print("STARTING APPLICATION - ENVIRONMENT CHECK")
print("=" * 50)
# List ALL environment variables (be careful with this in production!)
print("\nAll Environment Variables:")
for key in os.environ:
if 'KEY' in key or 'TOKEN' in key or 'SECRET' in key:
value = os.environ[key]
masked = value[:4] + "..." + value[-4:] if len(value) > 8 else "SET"
print(f" {key}: {masked}")
# ==================== IMPORT HANDLING ====================
try:
from google import genai
from google.genai import types
print("\n✅ Imported google.genai successfully")
except ImportError as e:
print(f"\n❌ Failed to import google.genai: {e}")
genai = None
types = None
# ==================== CONFIGURATION ====================
# Try multiple ways to get tokens
hf_token = os.environ.get("HF_TOKEN") or os.getenv("HF_TOKEN")
gemini_key = os.environ.get("GEMINI_API_KEY") or os.getenv("GEMINI_API_KEY")
groq_key = os.environ.get("GROQ_API_KEY") or os.getenv("GROQ_API_KEY")
print(f"\nToken Check:")
print(f" HF_TOKEN: {'SET (' + hf_token[:4] + '...)' if hf_token else 'NOT SET'}")
print(f" GEMINI_API_KEY: {'SET (' + gemini_key[:4] + '...)' if gemini_key else 'NOT SET'}")
print(f" GROQ_API_KEY: {'SET (' + groq_key[:4] + '...)' if groq_key else 'NOT SET'}")
# ==================== GEMINI SETUP ====================
gemini_client = None
if gemini_key and genai:
try:
print(f"\nAttempting Gemini setup...")
try:
gemini_client = genai.Client(api_key=gemini_key)
print(" ✅ Gemini client initialized with new SDK")
except Exception as e1:
print(f" ⚠️ New SDK failed: {e1}")
try:
gemini_client = genai.client.Client(api_key=gemini_key)
print(" ✅ Gemini client initialized with alternate method")
except Exception as e2:
print(f" ⚠️ Alternate failed: {e2}")
# Fallback to legacy
try:
import google.generativeai as old_genai
old_genai.configure(api_key=gemini_key)
gemini_client = old_genai
print(" ✅ Using legacy google.generativeai")
except Exception as e3:
print(f" ❌ Legacy also failed: {e3}")
except Exception as e:
print(f" ❌ Gemini setup error: {e}")
else:
if not gemini_key:
print("\n⚠️ No GEMINI_API_KEY found")
if not genai:
print("⚠️ Google GenAI SDK not imported")
# ==================== GROQ SETUP ====================
groq_client = None
if groq_key:
try:
print(f"\nAttempting Groq setup...")
groq_client = Groq(api_key=groq_key)
# Test the client with a simple request
test_response = groq_client.chat.completions.create(
messages=[{"role": "user", "content": "Hi"}],
model="llama-3.3-70b-versatile",
max_completion_tokens=5
)
print(" ✅ Groq client initialized and tested successfully")
except Exception as e:
print(f" ❌ Groq setup error: {e}")
groq_client = None
else:
print("\n⚠️ No GROQ_API_KEY found")
print("\n" + "=" * 50)
print("SETUP COMPLETE")
print("=" * 50)
# Lazy load summarizer
summarizer = None
def load_summarizer():
global summarizer
if summarizer is None:
try:
print("Loading summarizer...")
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", device=-1)
print("✅ Summarizer ready")
except Exception as e:
print(f"❌ Summarizer failed: {e}")
return summarizer
# ==================== STATUS CHECK ====================
def check_status():
statuses = []
# Gemini status
if gemini_client and gemini_key:
statuses.append("✅ Gemini Ready")
elif gemini_key:
statuses.append("⚠️ Gemini key present but client failed")
else:
statuses.append("❌ Gemini: Add GEMINI_API_KEY")
# Groq status
if groq_client and groq_key:
statuses.append("✅ Groq Ready")
elif groq_key:
statuses.append("⚠️ Groq key present but client failed")
else:
statuses.append("❌ Groq: Add GROQ_API_KEY")
return " | ".join(statuses)
# ==================== APP 1: PDF & ESSAY TOOLS ====================
def extract_text_from_pdf(pdf_file):
"""Extract text from PDF - handle both file path and bytes"""
if pdf_file is None:
return None, "Please upload a PDF file."
try:
if isinstance(pdf_file, str):
with open(pdf_file, 'rb') as f:
pdf_reader = PyPDF2.PdfReader(f)
text = ""
for page in pdf_reader.pages:
page_text = page.extract_text()
if page_text:
text += page_text + "\n"
else:
if hasattr(pdf_file, 'read'):
pdf_bytes = pdf_file.read()
if hasattr(pdf_file, 'seek'):
pdf_file.seek(0)
else:
pdf_bytes = pdf_file
if isinstance(pdf_bytes, bytes):
pdf_stream = io.BytesIO(pdf_bytes)
else:
pdf_stream = io.BytesIO(pdf_bytes.encode() if isinstance(pdf_bytes, str) else pdf_bytes)
pdf_reader = PyPDF2.PdfReader(pdf_stream)
text = ""
for page in pdf_reader.pages:
page_text = page.extract_text()
if page_text:
text += page_text + "\n"
text = re.sub(r'\s+', ' ', text).strip()
if len(text) < 50:
return None, "Could not extract text. PDF may be image-based or scanned."
return text, None
except Exception as e:
return None, f"Error reading PDF: {str(e)}"
def summarize_with_gemini(text, max_length, min_length):
"""Try to use Gemini for summarization"""
if not gemini_client or not gemini_key:
return None
try:
if hasattr(gemini_client, 'models'):
prompt = f"Summarize the following text in {min_length}-{max_length} words. Be concise and clear:\n\n{text[:15000]}"
try:
response = gemini_client.models.generate_content(
model="gemini-2.5-flash",
contents=prompt
)
return response.text
except:
try:
response = gemini_client.models.generate_content(
model="gemini-2.5-flash",
contents=prompt,
config=types.GenerateContentConfig(
max_output_tokens=500
) if types else None
)
return response.text
except:
pass
if hasattr(gemini_client, 'GenerativeModel'):
model = gemini_client.GenerativeModel('gemini-2.5-flash')
prompt = f"Summarize the following text in {min_length}-{max_length} words:\n\n{text[:15000]}"
response = model.generate_content(prompt)
return response.text
except Exception as e:
print(f"Gemini summary error: {e}")
return None
def summarize_pdf(pdf_file, max_length, min_length):
text, error = extract_text_from_pdf(pdf_file)
if error:
return error
gemini_result = summarize_with_gemini(text, max_length, min_length)
if gemini_result:
return gemini_result
summ = load_summarizer()
if summ:
try:
result = summ(text[:3500], max_length=max_length, min_length=min_length, do_sample=False)
return result[0]['summary_text']
except Exception as e:
return f"Summarization error: {str(e)}"
return "Error: No summarization available"
def generate_essay_with_gemini(prompt, essay_type, word_count, tone):
"""Generate essay using Gemini"""
if not gemini_client or not gemini_key:
return None
try:
full_prompt = f"""You are an expert academic writer. Write a {essay_type} essay in {tone} tone.
Target length: approximately {word_count} words.
Topic: {prompt}
Requirements:
- Engaging introduction with clear thesis statement
- Well-structured body paragraphs with supporting arguments and evidence
- Strong conclusion that summarizes main points
- Use academic vocabulary and formal writing style
Write the essay now:"""
if hasattr(gemini_client, 'models'):
try:
response = gemini_client.models.generate_content(
model="gemini-2.5-flash",
contents=full_prompt
)
essay = response.text.strip()
except:
if hasattr(gemini_client, 'GenerativeModel'):
model = gemini_client.GenerativeModel('gemini-2.5-flash')
response = model.generate_content(full_prompt)
essay = response.text.strip()
else:
return None
else:
model = gemini_client.GenerativeModel('gemini-2.5-flash')
response = model.generate_content(full_prompt)
essay = response.text.strip()
essay = re.sub(r'^#+\s*', '', essay)
word_count_actual = len(essay.split())
return f"""# {essay_type} Essay: {prompt[:50]}{'...' if len(prompt) > 50 else ''}
{essay}
---
*~{word_count_actual} words | {tone} | Gemini*"""
except Exception as e:
print(f"Essay generation error: {e}")
return None
def generate_essay(prompt, essay_type, word_count, tone):
if not prompt or len(prompt.strip()) < 10:
return "Please provide a detailed prompt (at least 10 characters)."
if gemini_client and gemini_key:
gemini_result = generate_essay_with_gemini(prompt, essay_type, word_count, tone)
if gemini_result:
return gemini_result
return f"""❌ AI generation not available.
Template Essay: {prompt}
Introduction:
{prompt} is an important topic that requires careful consideration. This essay explores its key aspects.
Body:
The significance of {prompt} cannot be overstated. Various perspectives exist on this matter, with experts debating the best approaches. Research continues to reveal new insights.
Conclusion:
In conclusion, {prompt} represents a complex issue that demands attention.
---
*Template fallback - Gemini SDK issue*
Check: 1) GEMINI_API_KEY is set 2) google-genai package is installed"""
def summarize_text(text, max_length, min_length):
if len(text.strip()) < 100:
return "Please provide at least 100 characters to summarize."
gemini_result = summarize_with_gemini(text, max_length, min_length)
if gemini_result:
return gemini_result
summ = load_summarizer()
if summ:
try:
result = summ(text[:3500], max_length=max_length, min_length=min_length, do_sample=False)
return result[0]['summary_text']
except Exception as e:
return f"Error: {str(e)}"
return "Error: No summarization available"
# ==================== APP 2: QUIZ GENERATOR ====================
def extract_sentences(text):
sentences = re.split(r'[.!?]', text)
return [s.strip() for s in sentences if len(s.split()) > 6]
def create_quiz(text, num_questions):
sentences = extract_sentences(text)
if len(sentences) < num_questions:
num_questions = len(sentences)
selected = random.sample(sentences, num_questions)
quiz_data = []
for sentence in selected:
words = sentence.split()
keyword = random.choice(words[2:-2])
question = sentence.replace(keyword, "_____")
all_words = list(set(text.split()))
wrong = random.sample(all_words, 3)
options = wrong + [keyword]
random.shuffle(options)
quiz_data.append({
"question": question,
"options": options,
"answer": keyword
})
return quiz_data
def start_quiz(text, num_questions, timer_minutes):
if not text.strip():
return "⚠️ Please enter study material.", None, None, None, None, None, None, ""
quiz = create_quiz(text, num_questions)
end_time = time.time() + (timer_minutes * 60)
return show_question(quiz, 0, 0, end_time)
def show_question(quiz, index, score, end_time):
if time.time() > end_time:
return finish_quiz(score, len(quiz))
if index >= len(quiz):
return finish_quiz(score, len(quiz))
q = quiz[index]
remaining = int(end_time - time.time())
timer_display = f"⏳ Time Left: {remaining} seconds"
return (
f"### Question {index+1}:\n\n{q['question']}",
gr.update(choices=q["options"], value=None),
f"Score: {score}",
quiz,
index,
score,
end_time,
timer_display
)
def submit_answer(selected, quiz, index, score, end_time):
if selected == quiz[index]["answer"]:
score += 1
index += 1
return show_question(quiz, index, score, end_time)
def finish_quiz(score, total):
return (
f"## ✅ Quiz Finished!\n\nFinal Score: **{score}/{total}**",
gr.update(choices=[]),
"",
None,
None,
None,
None,
""
)
# ==================== APP 3: URDU TRANSLATOR ====================
def translate_to_urdu(text):
"""Translate English text to Urdu using Groq's Llama 3.3 70B"""
if not text or not text.strip():
return "Please enter some text to translate."
if not groq_client:
return "❌ Groq API not configured. Please add GROQ_API_KEY."
try:
chat_completion = groq_client.chat.completions.create(
messages=[
{
"role": "system",
"content": "You are a professional English to Urdu translator. Translate the given text accurately to Urdu (اردو) using natural, conversational language. Respond ONLY with the translation, no explanations."
},
{
"role": "user",
"content": f"Translate to Urdu:\n\n{text}"
}
],
model="llama-3.3-70b-versatile",
temperature=0.3,
max_completion_tokens=2048,
)
return chat_completion.choices[0].message.content
except Exception as e:
return f"Error: {str(e)}"
# ==================== CSS ====================
custom_css = """
.header {
text-align: center;
margin-bottom: 2rem;
padding: 2rem;
background: linear-gradient(135deg, #059669, #6b7280);
border-radius: 12px;
color: white;
}
.header h1 {
font-size: 2.5rem;
margin-bottom: 0.5rem;
}
.status-ok {
background: #d1fae5;
border: 2px solid #059669;
padding: 1rem;
border-radius: 8px;
text-align: center;
color: #065f46;
font-weight: bold;
margin-bottom: 1rem;
}
.status-warn {
background: #fef3c7;
border: 2px solid #f59e0b;
padding: 1rem;
border-radius: 8px;
text-align: center;
color: #92400e;
margin-bottom: 1rem;
}
.status-error {
background: #fee2e2;
border: 2px solid #ef4444;
padding: 1rem;
border-radius: 8px;
text-align: center;
color: #991b1b;
margin-bottom: 1rem;
}
@import url('https://fonts.googleapis.com/css2?family=Noto+Nastaliq+Urdu&display=swap');
.urdu-text {
font-family: 'Noto Nastaliq Urdu', serif;
font-size: 1.5em;
line-height: 2;
direction: rtl;
text-align: right;
}
"""
# ==================== MAIN UI ====================
with gr.Blocks(title="Student AI Suite") as demo:
# Status Display
status = check_status()
if "✅" in status and "❌" not in status:
gr.HTML(f'{status}
')
elif "❌" in status:
gr.HTML(f'{status}
')
else:
gr.HTML(f'{status}
')
gr.HTML('')
with gr.Tabs():
# ==================== TAB 1: PDF & ESSAY ====================
with gr.TabItem("📄 Essay & PDF Tools"):
with gr.Tabs():
with gr.TabItem("📑 PDF Summarizer"):
pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"], type="binary")
with gr.Row():
max_len = gr.Slider(50, 500, 200, step=10, label="Max Length")
min_len = gr.Slider(20, 200, 50, step=10, label="Min Length")
summarize_btn = gr.Button("📝 Summarize PDF", variant="primary")
pdf_output = gr.Textbox(label="Summary", lines=12)
gr.Markdown("---")
text_input = gr.Textbox(label="Or paste text", lines=5)
text_btn = gr.Button("Summarize Text")
text_output = gr.Textbox(label="Summary", lines=8)
with gr.TabItem("✍️ Essay Generator"):
prompt_input = gr.Textbox(label="Essay Topic", placeholder="Example: 'The impact of AI on education'", lines=3)
essay_type = gr.Dropdown(["Argumentative", "Expository", "Descriptive", "Persuasive"], value="Argumentative")
tone = gr.Dropdown(["Academic", "Formal", "Neutral"], value="Academic")
words = gr.Slider(200, 1000, 500, step=50, label="Word Count")
gen_btn = gr.Button("✨ Generate Essay", variant="primary")
essay_output = gr.Textbox(label="Generated Essay", lines=25)
# ==================== TAB 2: QUIZ GENERATOR ====================
with gr.TabItem("🎯 Smart Quiz Generator"):
gr.Markdown("### Paste your study text → generate quiz → test yourself!")
quiz_text_input = gr.Textbox(lines=6, label="Study Material")
with gr.Row():
num_questions = gr.Slider(1, 10, value=5, step=1, label="Number of Questions")
timer_minutes = gr.Slider(1, 10, value=2, step=1, label="Timer (Minutes)")
start_btn = gr.Button("Start Quiz", variant="primary")
question_box = gr.Markdown()
options_radio = gr.Radio(label="Select Answer")
submit_btn = gr.Button("Submit Answer", variant="secondary")
score_display = gr.Markdown()
timer_display = gr.Markdown()
# Hidden states for quiz
quiz_state = gr.State()
index_state = gr.State()
score_state = gr.State()
endtime_state = gr.State()
# ==================== TAB 3: URDU TRANSLATOR ====================
with gr.TabItem("🌍 English to Urdu"):
gr.Markdown("### Powered by Groq AI")
with gr.Row():
with gr.Column():
input_text = gr.Textbox(
label="English Text",
placeholder="Enter text to translate...",
lines=4
)
translate_btn = gr.Button("Translate", variant="primary")
with gr.Column():
output_text = gr.Textbox(
label="Urdu Translation (اردو)",
lines=4,
elem_classes=["urdu-text"],
interactive=False
)
examples = [
"Hello, how are you?",
"I love Pakistan.",
"The weather is beautiful today."
]
gr.Examples(examples=examples, inputs=input_text)
# ==================== EVENT HANDLERS ====================
# PDF & Essay handlers
summarize_btn.click(summarize_pdf, [pdf_input, max_len, min_len], pdf_output)
text_btn.click(summarize_text, [text_input, max_len, min_len], text_output)
gen_btn.click(generate_essay, [prompt_input, essay_type, words, tone], essay_output)
# Quiz handlers
start_btn.click(
start_quiz,
inputs=[quiz_text_input, num_questions, timer_minutes],
outputs=[
question_box,
options_radio,
score_display,
quiz_state,
index_state,
score_state,
endtime_state,
timer_display
]
)
submit_btn.click(
submit_answer,
inputs=[options_radio, quiz_state, index_state, score_state, endtime_state],
outputs=[
question_box,
options_radio,
score_display,
quiz_state,
index_state,
score_state,
endtime_state,
timer_display
]
)
# Urdu translator handlers
translate_btn.click(fn=translate_to_urdu, inputs=input_text, outputs=output_text)
input_text.submit(fn=translate_to_urdu, inputs=input_text, outputs=output_text)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860, css=custom_css)