Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import PyPDF2
+import re
+import os
+import io
+import random
+import time
+from groq import Groq
+
+# ==================== IMPORT HANDLING ====================
+# Try different import methods for google genai
+try:
+    from google import genai
+    from google.genai import types
+    print("✅ Imported google.genai successfully")
+except ImportError:
+    try:
+        import google.genai as genai
+        from google.genai import types
+        print("✅ Imported google.genai as module")
+    except ImportError:
+        try:
+            from google_genai import genai
+            from google_genai import types
+            print("✅ Imported google_genai")
+        except ImportError as e:
+            print(f"❌ Failed to import google genai: {e}")
+            genai = None
+            types = None
+
+# ==================== CONFIGURATION ====================
+# Setup tokens
+hf_token = os.getenv("HF_TOKEN")
+gemini_key = os.getenv("GEMINI_API_KEY")
+groq_key = os.getenv("GROQ_API_KEY")
+
+print(f"HF_TOKEN: {hf_token is not None}")
+print(f"GEMINI_API_KEY: {gemini_key is not None}")
+print(f"GROQ_API_KEY: {groq_key is not None}")
+
+# Configure Gemini client
+gemini_client = None
+if gemini_key and genai:
+    try:
+        try:
+            gemini_client = genai.Client(api_key=gemini_key)
+        except:
+            try:
+                gemini_client = genai.client.Client(api_key=gemini_key)
+            except:
+                import google.generativeai as old_genai
+                old_genai.configure(api_key=gemini_key)
+                gemini_client = old_genai
+                print("✅ Using legacy google.generativeai")
+        
+        if gemini_client:
+            print("✅ Gemini client configured")
+    except Exception as e:
+        print(f"❌ Gemini config error: {e}")
+        gemini_client = None
+
+# Configure Groq client
+groq_client = None
+if groq_key:
+    try:
+        groq_client = Groq(api_key=groq_key)
+        print("✅ Groq client configured")
+    except Exception as e:
+        print(f"❌ Groq config error: {e}")
+
+# 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 = []
+    if gemini_client:
+        statuses.append("✅ Gemini Ready")
+    elif gemini_key:
+        statuses.append("⚠️ Gemini key present but SDK not loaded")
+    else:
+        statuses.append("❌ Gemini: Add GEMINI_API_KEY")
+    
+    if groq_client:
+        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", css=custom_css) as demo:
+    
+    # Status Display
+    status = check_status()
+    if "✅" in status and "❌" not in status:
+        gr.HTML(f'<div class="status-ok">{status}</div>')
+    elif "❌" in status:
+        gr.HTML(f'<div class="status-error">{status}</div>')
+    else:
+        gr.HTML(f'<div class="status-warn">{status}</div>')
+    
+    gr.HTML('<div class="header"><h1>🎓 Student AI Suite</h1><p>Essay Generator • PDF Summarizer • Quiz Generator • Urdu Translator</p></div>')
+    
+    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)