app.py

import os
import google.generativeai as genai
import gradio as gr
from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains import ConversationalRetrievalChain, LLMChain
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
import time
import concurrent.futures
import logging
import re
import requests
from bs4 import BeautifulSoup
from difflib import SequenceMatcher
from collections import Counter
import matplotlib.pyplot as plt
from io import BytesIO
import base64

# تنظیم لاگ‌گیری
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

# تنظیم API Key
gemini_api_key = os.environ.get('GEMINI_API_KEY')
if not gemini_api_key:
    raise ValueError("GOOGLE_API_KEY not found. Please set it in the Space settings.")
genai.configure(api_key=gemini_api_key)

def process_single_pdf(pdf_file):
    pdf_path = pdf_file.name if hasattr(pdf_file, 'name') else pdf_file
    logger.info(f"Starting to process file: {pdf_path}")
    if not os.path.isfile(pdf_path):
        logger.error(f"File {pdf_path} does not exist.")
        return None, None
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=150)
    loader = PyPDFLoader(pdf_path)
    try:
        pages = loader.load_and_split()
        docs = text_splitter.split_documents(pages)
        sections = {"Introduction": [], "Methodology": [], "Results": [], "Discussion": [], "References": []}
        for doc in docs:
            text = doc.page_content
            if re.search(r"Introduction|مقدمه", text, re.I):
                sections["Introduction"].append(doc)
            elif re.search(r"Methodology|روش", text, re.I):
                sections["Methodology"].append(doc)
            elif re.search(r"Results|نتایج", text, re.I):
                sections["Results"].append(doc)
            elif re.search(r"Discussion|بحث", text, re.I):
                sections["Discussion"].append(doc)
            elif re.search(r"References|Bibliography|منابع", text, re.I):
                sections["References"].append(doc)
        logger.info(f"Processed file: {pdf_path} - Number of chunks: {len(docs)}")
        return docs, sections
    except Exception as e:
        logger.error(f"Error processing {pdf_path}: {str(e)}")
        return None, None

def upload_and_process_pdf(pdf_files):
    if not pdf_files:
        return None, None, None, "Please upload at least one PDF file."
    logger.info(f"Number of input files: {len(pdf_files)}")
    all_docs = []
    all_sections = {"Introduction": [], "Methodology": [], "Results": [], "Discussion": [], "References": []}
    with concurrent.futures.ThreadPoolExecutor() as executor:
        future_to_file = {executor.submit(process_single_pdf, pdf_file): pdf_file for pdf_file in pdf_files}
        for future in concurrent.futures.as_completed(future_to_file):
            docs, sections = future.result()
            if docs:
                all_docs.extend(docs)
                for key in all_sections:
                    all_sections[key].extend(sections[key])
            else:
                pdf_file = future_to_file[future]
                return None, None, None, f"Error processing file: {pdf_file.name if hasattr(pdf_file, 'name') else pdf_file}"
    logger.info(f"Total number of processed documents: {len(all_docs)}")
    return None, all_docs, all_sections, None

def create_vector_db(docs):
    if not docs:
        return None, "No content was processed."
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=gemini_api_key)
    try:
        logger.info("Starting to build FAISS...")
        vector_store = FAISS.from_documents(docs, embedding=embeddings)
        logger.info(f"Vector database built with {len(docs)} documents.")
        return vector_store, None
    except Exception as e:
        logger.error(f"Error creating vector database: {str(e)}")
        return None, f"Error in vector processing: {str(e)}"

def extract_keywords(text):
    try:
        prompt = f"Extract 5 main keywords from the following text that represent the main topic:\n**Text:**\n{text[:2000]}\n**Keywords:**"
        model = genai.GenerativeModel('gemini-pro')
        response = model.generate_content(prompt)
        keywords = response.text.split("**Keywords:**")[-1].strip().split(", ")
        logger.info(f"Extracted keywords: {keywords}")
        time.sleep(1)
        return keywords[:5]
    except Exception as e:
        logger.error(f"Error extracting keywords: {str(e)}")
        return ["research", "results", "method", "analysis", "topic"]

def translate_to_english(text):
    try:
        prompt = f"Translate the following text to English:\n**Text:**\n{text[:1000]}\n**Translation:**"
        model = genai.GenerativeModel('gemini-pro')
        response = model.generate_content(prompt)
        translated_text = response.text.split("**Translation:**")[-1].strip()
        logger.info(f"Translated text: {translated_text[:50]}...")
        time.sleep(1)
        return translated_text
    except Exception as e:
        logger.error(f"Error in translation: {str(e)}")
        return text

def check_plagiarism(text, language):
    try:
        keywords = extract_keywords(text)
        translated_keywords = translate_to_english(" ".join(keywords))
        query = translated_keywords
        
        # Search in Google Scholar (free but limited)
        url_scholar = f"https://scholar.google.com/scholar?q={query}"
        response_scholar = requests.get(url_scholar, headers={"User-Agent": "Mozilla/5.0"})
        soup_scholar = BeautifulSoup(response_scholar.text, 'html.parser')
        results_scholar = []
        for item in soup_scholar.find_all('h3', class_='gs_rt', limit=5):
            title = item.get_text().strip()
            link = item.find('a')['href'] if item.find('a') else "No link available"
            author_info = item.find_next('div', class_='gs_a')
            if author_info:
                author_year = author_info.get_text().strip()
                author_match = re.search(r"(.+?)(?: - (\d{4}))?", author_year)
                author = author_match.group(1) if author_match.group(1) else "Unknown Author"
                year = author_match.group(2) if author_match.group(2) else "Unknown"
            else:
                author, year = "Unknown Author", "Unknown"
            results_scholar.append((title, link, author, year))
        logger.info(f"Google Scholar results: {results_scholar}")

        # Search in arXiv (free)
        url_arxiv = f"https://arxiv.org/search/?query={query}&searchtype=all&source=header&start=0&max_results=10"
        response_arxiv = requests.get(url_arxiv, headers={"User-Agent": "Mozilla/5.0"})
        soup_arxiv = BeautifulSoup(response_arxiv.text, 'html.parser')
        results_arxiv = []
        for item in soup_arxiv.find_all('p', class_='title', limit=5):
            title = item.get_text().strip()
            link = item.find_previous('a', class_='arxiv-url')['href'] if item.find_previous('a', class_='arxiv-url') else "No link available"
            author_info = item.find_next('p', class_='authors')
            year_info = item.find_next('p', class_='is-size-7')
            author = author_info.get_text().replace("Authors:", "").strip() if author_info else "Unknown Author"
            year = re.search(r"\d{4}", year_info.get_text() if year_info else "").group(0) if re.search(r"\d{4}", year_info.get_text() if year_info else "") else "Unknown"
            results_arxiv.append((title, link, author, year))
        logger.info(f"arXiv results: {results_arxiv}")

        all_results = results_scholar + results_arxiv
        if not all_results:
            return "No significant similarity found.\n**Explanation:** Your text was compared with scientific resources in Google Scholar and arXiv, and no meaningful matches were found.\n**Status:** Plagiarism likelihood is very low." if language == "English" else "هیچ تشابه قابل توجهی یافت نشد.\n**توضیح:** متن شما با منابع علمی موجود در Google Scholar و arXiv مقایسه شد و هیچ تطابقی معناداری پیدا نشد.\n**وضعیت:** احتمال سرقت ادبی بسیار پایین است."

        max_similarity = 0
        matched_texts = []
        for title, link, author, year in all_results:
            similarity = SequenceMatcher(None, text[:1000], title).ratio()
            if similarity > 0.1:  # Minimum 10% similarity for display
                matched_texts.append(f"**Title:** {title}\n**Author:** {author}\n**Year:** {year}\n**Link:** {link}\n**Note:** This resource may have some similarity with your text." if language == "English" else f"**عنوان:** {title}\n**نویسنده:** {author}\n**سال:** {year}\n**لینک:** {link}\n**توضیح:** این منبع ممکن است بخشی از متن شما را مشابه داشته باشد.")
            if similarity > max_similarity:
                max_similarity = similarity
        
        time.sleep(1)
        similarity_percent = max_similarity * 100
        if not matched_texts:
            return "No significant similarity found.\n**Explanation:** Your text was compared with scientific resources and no matches were found.\n**Status:** Plagiarism likelihood is very low." if language == "English" else "هیچ تشابه قابل توجهی یافت نشد.\n**توضیح:** متن شما با منابع علمی مقایسه شد و تطابقی پیدا نشد.\n**وضعیت:** احتمال سرقت ادبی بسیار پایین است."

        if similarity_percent > 20:
            status = "Plagiarism is likely. Please review similar resources and add appropriate citations." if language == "English" else "احتمال سرقت ادبی وجود دارد. لطفاً منابع مشابه را بررسی کنید و ارجاع مناسب اضافه کنید."
        elif similarity_percent > 10:
            status = "Low similarity. Possibly coincidental, but reviewing resources is recommended." if language == "English" else "تشابه کم. احتمالاً تصادفی است، اما مرور منابع توصیه می‌شود."
        else:
            status = "Very low similarity. Plagiarism likelihood is negligible." if language == "English" else "تشابه بسیار کم. احتمال سرقت ادبی ناچیز است."
        
        output = (f"**Similarity Percentage:** {similarity_percent:.2f}%\n"
                  f"**Status:** {status}\n"
                  f"**Similar Resources Found:**\n" + "\n--------------------\n".join(matched_texts[:3]))
        return output
    except Exception as e:
        logger.error(f"Error in plagiarism check: {str(e)}")
        return f"Error in plagiarism check: {str(e)}\nPlease try again or contact support." if language == "English" else f"خطا در بررسی سرقت ادبی: {str(e)}\nلطفاً دوباره امتحان کنید یا با پشتیبانی تماس بگیرید."

def suggest_resources(text, language):
    try:
        keywords = extract_keywords(text)
        translated_text = translate_to_english(" ".join(keywords))
        query = translated_text
        # Search in arXiv (free)
        url_arxiv = f"https://arxiv.org/search/?query={query}&searchtype=all&source=header&start=0&max_results=10"
        response_arxiv = requests.get(url_arxiv, headers={"User-Agent": "Mozilla/5.0"})
        soup_arxiv = BeautifulSoup(response_arxiv.text, 'html.parser')
        papers_arxiv = []
        for paper in soup_arxiv.find_all('p', class_='title', limit=5):
            title = paper.get_text().strip()
            link = paper.find_previous('a', class_='arxiv-url')['href'] if paper.find_previous('a', class_='arxiv-url') else "No link available"
            papers_arxiv.append(f"{title} (Link: {link})")
        
        time.sleep(1)
        return papers_arxiv if papers_arxiv else ["No resources found."] if language == "English" else ["منبعی یافت نشد."]
    except Exception as e:
        logger.error(f"Error in suggesting resources: {str(e)}")
        return ["Error in resource search"] if language == "English" else ["خطا در جستجوی منابع"]

def evaluate_quality(docs, sections, language):
    text = " ".join([doc.page_content for doc in docs])
    score = 0
    explanation = []
    suggestions = []
    auto_fix = ""

    # Criterion 1: References (Quality and Quantity)
    ref_count = len(re.findall(r"\[\d+\]|[A-Za-z]+\s+\d{4}", text))
    if ref_count > 15:
        score += 30
        explanation.append("Very strong and credible references (more than 15 citations from reputable journals).")
    elif ref_count > 10:
        score += 25
        explanation.append("Sufficient and credible references (10-15 citations).")
    elif ref_count > 0:
        score += 15
        explanation.append("Existing but limited references (fewer than 10 citations).")
        suggestions.append("Add at least 5 sources from reputable journals (like IEEE, Springer, or Elsevier) with precise author and year citations.")
    else:
        explanation.append("No sufficient references found.")
        suggestions.append("Complete the references section with at least 10 citations from peer-reviewed articles.")
        auto_fix += "\n**Auto-fix - Sample Citation:**\n[1] Smith, J. (2020). 'Advanced Research Methods', Journal of Science, 15(3), 123-145."

    # Criterion 2: Coherence, Writing, and Scientific Weight
    words = text.split()
    word_freq = Counter(words).most_common(10)
    keywords = [word[0] for word in word_freq[:3]] if word_freq else ["research", "results", "method"]
    scientific_terms = sum(1 for word in words if word.lower() in ["analysis", "data", "method", "result", "hypothesis", "theory"])
    if word_freq and word_freq[0][1] > len(words) * 0.02 and scientific_terms > len(words) * 0.05:
        score += 25
        explanation.append("Excellent textual coherence and high scientific weight (focus on topic and use of scientific terms).")
    else:
        explanation.append("Poor textual coherence or low scientific weight (topic dispersion or lack of scientific terms).")
        suggestions.append(f"Use keywords like {', '.join(keywords)} and scientific terms (like 'statistical analysis' or 'hypothesis') more frequently and make sentences smoother.")
        try:
            prompt = f"Rewrite the following paragraph to be more scientific, smoother, and with higher scientific weight:\n**Text:**\n{text[:500]}\n**Rewritten:**"
            model = genai.GenerativeModel('gemini-pro')
            response = model.generate_content(prompt)
            auto_fix += f"\n**Auto-fix - Rewritten Paragraph:**\n{response.text.split('**Rewritten:**')[-1].strip()}"
            time.sleep(1)
        except Exception as e:
            logger.error(f"Error in rewriting: {str(e)}")
            auto_fix += "\n**Auto-fix - Rewritten:**\nError in rewriting, please manually revise the text."

    # Criterion 3: Tables/Figures
    if re.search(r"Table|Figure|جدول|شکل", text, re.I):
        score += 20
        explanation.append("Effective use of tables or figures to support findings.")
    else:
        explanation.append("No use of tables or figures.")
        suggestions.append("Add a table for data and a figure (like a bar chart or line graph) for trends to make findings more comprehensible.")

    # Criterion 4: Depth of Analysis and Scientific Weight
    analysis_text = " ".join([doc.page_content for doc in sections.get("Results", []) + sections.get("Discussion", [])])
    stats_found = bool(re.search(r"Statistic|Regression|ANOVA|T-test|Correlation|آمار", analysis_text, re.I))
    if len(analysis_text.split()) > 1500 and stats_found:
        score += 25
        explanation.append("Very high depth of analysis (long and statistical with strong scientific weight).")
    elif len(analysis_text.split()) > 1000:
        score += 15
        explanation.append("Acceptable depth of analysis (long but lacking sufficient statistical analysis).")
        suggestions.append("Add advanced statistical analysis (like regression, ANOVA, or T-test) to strengthen findings.")
    else:
        explanation.append("Poor depth of analysis (short and without statistical analysis).")
        suggestions.append("Expand the Results/Discussion section to at least 1500 words with comprehensive statistical analysis.")

    # Criterion 5: IMRAD Structure (Advanced Academic Quality)
    imrad_structure = {"Introduction": 0, "Methodology": 0, "Results": 0, "Discussion": 0}
    for section_name in imrad_structure.keys():
        if sections.get(section_name) and len(sections[section_name]) > 0:
            imrad_structure[section_name] = 1
    imrad_score = sum(imrad_structure.values()) * 5  # هر بخش 5 امتیاز
    score += imrad_score
    if imrad_score == 20:
        explanation.append("Complete IMRAD structure present (Introduction, Methodology, Results, Discussion).")
    elif imrad_score > 0:
        explanation.append(f"Partial IMRAD structure present (missing {4 - sum(imrad_structure.values())} sections).")
        suggestions.append("Ensure all sections of IMRAD (Introduction, Methodology, Results, Discussion) are included for a complete academic structure.")
    else:
        explanation.append("No IMRAD structure detected.")
        suggestions.append("Structure your document following the IMRAD format (Introduction, Methodology, Results, Discussion) for better academic quality.")

    # Criterion 6: Word Count (Basic Length Check)
    word_count = len(words)
    if word_count > 5000:
        score += 10
        explanation.append("Document length is excellent (over 5000 words).")
    elif word_count > 3000:
        score += 7
        explanation.append("Document length is good (3000-5000 words).")
    else:
        explanation.append("Document length is short (less than 3000 words).")
        suggestions.append("Expand the document to at least 3000 words for better academic depth.")

    # Measure scientific weight by global comparison
    try:
        keywords = extract_keywords(text)
        translated_text = translate_to_english(" ".join(keywords))
        query = translated_text
        url_arxiv = f"https://arxiv.org/search/?query={query}&searchtype=all&source=header"
        response_arxiv = requests.get(url_arxiv, headers={"User-Agent": "Mozilla/5.0"})
        soup_arxiv = BeautifulSoup(response_arxiv.text, 'html.parser')
        arxiv_titles = [paper.get_text().strip() for paper in soup_arxiv.find_all('p', class_='title')[:3]]
        if arxiv_titles:
            suggestions.append(f"To increase scientific weight, refer to similar arXiv papers like '{arxiv_titles[0]}' and compare your findings with them.")
        time.sleep(1)
    except Exception as e:
        logger.error(f"Error in scientific weight assessment: {str(e)}")
        suggestions.append("Global comparison with scientific resources failed due to an error.")

    score = max(min(score, 100), 0)
    if language == "Farsi":
        # تبدیل توضیحات و پیشنهادات به فارسی
        explanation = "; ".join([
            "منابع بسیار قوی و قابل استناد (بیش از 15 ارجاع از مجلات معتبر)" if x == "Very strong and credible references (more than 15 citations from reputable journals)." else
            "منابع کافی و قابل استناد (10-15 ارجاع)" if x == "Sufficient and credible references (10-15 citations)." else
            "منابع موجود اما محدود (کمتر از 10 ارجاع)" if x == "Existing but limited references (fewer than 10 citations)." else
            "منابع کافی یافت نشد" if x == "No sufficient references found." else
            "انسجام متنی عالی و بار علمی بالا (تمرکز روی موضوع و استفاده از اصطلاحات علمی)" if x == "Excellent textual coherence and high scientific weight (focus on topic and use of scientific terms)." else
            "انسجام متنی ضعیف یا بار علمی پایین (پراکندگی موضوعی یا کمبود اصطلاحات علمی)" if x == "Poor textual coherence or low scientific weight (topic dispersion or lack of scientific terms)." else
            "استفاده مؤثر از جداول یا شکل‌ها برای پشتیبانی یافته‌ها" if x == "Effective use of tables or figures to support findings." else
            "عدم استفاده از جداول یا شکل‌ها" if x == "No use of tables or figures." else
            "عمق تحلیل بسیار بالا (تحلیل طولانی و آماری با بار علمی قوی)" if x == "Very high depth of analysis (long and statistical with strong scientific weight)." else
            "عمق تحلیل قابل قبول (طولانی اما بدون تحلیل آماری کافی)" if x == "Acceptable depth of analysis (long but lacking sufficient statistical analysis)." else
            "عمق تحلیل ضعیف (کوتاه و بدون تحلیل آماری)" if x == "Poor depth of analysis (short and without statistical analysis)." else
            "ساختار کامل IMRAD موجود است (مقدمه، روش‌شناسی، نتایج، بحث)" if x == "Complete IMRAD structure present (Introduction, Methodology, Results, Discussion)." else
            f"ساختار ناقص IMRAD موجود است (بخش‌های گمشده {4 - sum(imrad_structure.values())} بخش)" if x.startswith("Partial IMRAD structure present") else
            "هیچ ساختاری از IMRAD تشخیص داده نشد" if x == "No IMRAD structure detected." else
            "طول سند عالی است (بیش از 5000 کلمه)" if x == "Document length is excellent (over 5000 words)." else
            "طول سند خوب است (3000-5000 کلمه)" if x == "Document length is good (3000-5000 words)." else
            "طول سند کوتاه است (کمتر از 3000 کلمه)" if x == "Document length is short (less than 3000 words)." else
            x for x in explanation
        ])
        suggestions = "; ".join([
            "حداقل 5 منبع از مجلات معتبر (مثل IEEE، Springer، یا Elsevier) با ذکر دقیق نویسنده و سال اضافه کنید" if x == "Add at least 5 sources from reputable journals (like IEEE, Springer, or Elsevier) with precise author and year citations." else
            "بخش منابع را با حداقل 10 ارجاع از مقالات Peer-Reviewed تکمیل کنید" if x == "Complete the references section with at least 10 citations from peer-reviewed articles." else
            f"از کلمات کلیدی مثل {', '.join(keywords)} و اصطلاحات علمی (مثل 'تحلیل آماری' یا 'فرضیه') بیشتر استفاده کنید و جملات را روان‌تر کنید" if x.startswith("Use keywords like") else
            "یک جدول برای داده‌ها و یک شکل (مثل نمودار میله‌ای یا خطی) برای روندها اضافه کنید تا یافته‌ها قابل‌فهم‌تر شوند" if x == "Add a table for data and a figure (like a bar chart or line graph) for trends to make findings more comprehensible." else
            "تحلیل آماری پیشرفته (مثل رگرسیون، ANOVA، یا T-test) برای تقویت یافته‌ها اضافه کنید" if x == "Add advanced statistical analysis (like regression, ANOVA, or T-test) to strengthen findings." else
            "بخش نتایج/بحث را با حداقل 1500 کلمه و تحلیل آماری جامع گسترش دهید" if x == "Expand the Results/Discussion section to at least 1500 words with comprehensive statistical analysis." else
            "برای افزایش بار علمی، به مقالات مشابه در arXiv مثل '...' رجوع کنید و یافته‌های خود را با آن‌ها مقایسه کنید" if x.startswith("To increase scientific weight") else
            "مقایسه با منابع علمی جهانی به دلیل خطا انجام نشد" if x == "Global comparison with scientific resources failed due to an error." else
            "تمام بخش‌های IMRAD (مقدمه، روش‌شناسی، نتایج، بحث) را برای ساختار آکادمیک کامل شامل کنید" if x == "Ensure all sections of IMRAD (Introduction, Methodology, Results, Discussion) are included for a complete academic structure." else
            "سند را به حداقل 3000 کلمه گسترش دهید تا عمق آکادمیک بیشتری داشته باشد" if x == "Expand the document to at least 3000 words for better academic depth." else
            x for x in suggestions
        ])
    return score, explanation, suggestions, auto_fix

def generate_visualization(docs, language):
    if not docs:
        return "No data available for visualization.", None
    
    # استخراج کلمات کلیدی از تمام اسناد
    all_text = " ".join([doc.page_content for doc in docs])
    keywords = extract_keywords(all_text)
    
    # ایجاد نمودار میله‌ای برای کلمات کلیدی
    plt.figure(figsize=(10, 6))
    plt.bar(keywords, [1] * len(keywords))  # ساده‌سازی با ارتفاع ثابت برای نمایش
    plt.title("Top Keywords from Document" if language == "English" else "کلمات کلیدی اصلی از سند")
    plt.xlabel("Keywords" if language == "English" else "کلمات کلیدی")
    plt.ylabel("Frequency" if language == "English" else "تعداد")
    
    # ذخیره نمودار به‌عنوان تصویر باینری
    buffer = BytesIO()
    plt.savefig(buffer, format='png', bbox_inches='tight')
    buffer.seek(0)
    image_png = buffer.getvalue()
    buffer.close()
    
    # تبدیل تصویر به Base64 و نمایش در Gradio
    image_base64 = base64.b64encode(image_png).decode('utf-8')
    return "Here is a visualization of the top keywords from your document." if language == "English" else "این یک نمایش بصری از کلمات کلیدی اصلی سند شماست.", f'<img src="data:image/png;base64,{image_base64}" style="max-width: 100%; height: auto; display: block;">'

llm_gemini = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=gemini_api_key, convert_system_message_to_human=True, temperature=0.5)

def academic_chatbot(pdf_file, mode, query, language, detail_level, section_dropdown, visualize=False):
    start_time = time.time()
    logger.info(f"Starting processing - Mode: {mode}, Question: {query}, Language: {language}, Detail: {detail_level}, Section: {section_dropdown}, Visualize: {visualize}")
    
    if mode != "Standard Response" and not pdf_file:
        return "Please upload at least one PDF file."
    
    if mode == "Standard Response":
        chain = create_conversation_chain(None, None, mode, language, detail_level)
        try:
            result = chain.invoke({"question": query})["text"]
            return result + f"\n\n⏱ Processing time: {time.time() - start_time:.2f} seconds"
        except Exception as e:
            logger.error(f"Error in standard processing: {str(e)}")
            return f"Error: {str(e)}"
    
    pdf_files = pdf_file if isinstance(pdf_file, list) else [pdf_file]
    _, docs, sections, error = upload_and_process_pdf(pdf_files)
    if error:
        return error
    
    target_docs = docs if section_dropdown == "Entire Document" else sections.get(section_dropdown, docs)
    context = " ".join([doc.page_content for doc in target_docs])
    
    vector_store = None
    if mode in ["Academic Analysis (RAG)", "Plagiarism Check", "Quality Evaluation"]:
        vector_store, vectordb_error = create_vector_db(target_docs)
        if vectordb_error:
            return vectordb_error
    
    chain = create_conversation_chain(vector_store, target_docs, mode, language, detail_level, section_dropdown)
    try:
        if mode == "Auto Summary":
            time.sleep(2)
            result = chain.invoke({"context": context[:5000]})["text"]
        elif mode == "Plagiarism Check":
            plagiarism_result = check_plagiarism(context, language)
            result = plagiarism_result
        elif mode == "Quality Evaluation":
            score, explanation, suggestions, auto_fix = evaluate_quality(target_docs, sections, language)
            time.sleep(2)
            result = chain.invoke({"context": context[:5000], "score": score, "explanation": explanation, "suggestions": suggestions})["text"] + auto_fix
        else:
            result = chain.invoke({"question": query, "chat_history": []})["answer"]
        
        if mode not in ["Plagiarism Check", "Quality Evaluation"]:
            resources = suggest_resources(context, language)
            result += "\n\n**Suggested Resources:**\n" + "\n".join(resources) if language == "English" else "\n\n**منابع پیشنهادی:**\n" + "\n".join(resources)
        
        # اگر گزینه بصری‌سازی فعال باشه، نمودار رو اضافه کن
        if visualize and mode in ["Quality Evaluation", "Auto Summary", "Academic Analysis (RAG)"]:
            viz_text, viz_image = generate_visualization(target_docs, language)
            result += f"\n\n{viz_text}\n{viz_image}"
        
        return result + f"\n\n⏱ Processing time: {time.time() - start_time:.2f} seconds"
    except Exception as e:
        logger.error(f"Error in processing: {str(e)}")
        if "429" in str(e):
            return "Error: Rate limit exceeded for Gemini API. Please wait a few minutes and try again." if language == "English" else "خطا: محدودیت درخواست به API Gemini. لطفاً چند دقیقه صبر کنید و دوباره امتحان کنید."
        return f"Error: {str(e)}" if language == "English" else f"خطا: {str(e)}"

academic_analysis_prompt = PromptTemplate(
    template="""You are a professional academic analyst. Provide a deep and structured analysis of {section}:
1. Based solely on the provided text.
2. Including a review of the topic, methods, findings, and critique (if applicable).
3. In {language} with {detail_level} detail.
**Related Text:**
{context}
**User Question:** {question}
**Academic Analysis:**""",
    input_variables=["section", "context", "question", "language", "detail_level"]
)

summary_prompt = PromptTemplate(
    template="""You are an expert in academic writing. Produce a structured scientific summary (200-300 words) of the following text in {language} that includes:
1. Research objective
2. Methodology
3. Main findings
4. Conclusion
**Text:**
{context}
**Summary:**""",
    input_variables=["context", "language"]
)

general_qa_prompt = PromptTemplate(
    template="""You are an intelligent assistant. Answer the user's question in {language}:
**User Question:** {question}
Answer:""",
    input_variables=["question", "language"]
)

plagiarism_prompt = PromptTemplate(
    template="""Report the percentage of similarity of the following text with English resources:
**Text:**
{context}
**Result:** {similarity}""",
    input_variables=["context", "similarity"]
)

quality_prompt = PromptTemplate(
    template="""You are a professional academic evaluator. Evaluate the scientific quality of the following text:
**Text:**
{context}
**Score:** {score}/100
**Explanations:** {explanation}
**Improvement Suggestions:** {suggestions}""",
    input_variables=["context", "score", "explanation", "suggestions"]
)

def create_conversation_chain(vector_store, docs, mode, language, detail_level, section=None):
    if mode == "Academic Analysis (RAG)":
        retriever = vector_store.as_retriever(search_kwargs={"k": 3})
        chain = ConversationalRetrievalChain.from_llm(
            llm=llm_gemini,
            retriever=retriever,
            return_source_documents=True,
            combine_docs_chain_kwargs={"prompt": academic_analysis_prompt.partial(language=language, detail_level=detail_level, section=section or "Entire Document")},
            verbose=True
        )
    elif mode == "Auto Summary":
        chain = LLMChain(llm=llm_gemini, prompt=summary_prompt.partial(language=language))
    elif mode == "Plagiarism Check":
        chain = LLMChain(llm=llm_gemini, prompt=plagiarism_prompt.partial(language=language))
    elif mode == "Quality Evaluation":
        chain = LLMChain(llm=llm_gemini, prompt=quality_prompt.partial(language=language))
    else:
        chain = LLMChain(llm=llm_gemini, prompt=general_qa_prompt.partial(language=language))
    return chain

if __name__ == "__main__":
    with gr.Blocks(title="Professional Thesis Analyzer with Gemini") as iface:
        with gr.Row():
            with gr.Column():
                gr.Markdown("# Professional Thesis Analyzer with Gemini")
                gr.Markdown("Upload your PDF file and use the analysis, summary, plagiarism check, or quality evaluation features.")
                pdf_input = gr.File(file_types=['.pdf'], label="Upload PDF File", file_count="multiple")
                mode = gr.Radio(
                    ["Academic Analysis (RAG)", "Auto Summary", "Plagiarism Check", "Quality Evaluation", "Standard Response"],
                    label="Processing Mode",
                    value="Academic Analysis (RAG)"
                )
                query = gr.Textbox(lines=3, placeholder="Enter your question or request here...", label="Question or Request")
                section = gr.Dropdown(["Entire Document", "Introduction", "Methodology", "Results", "Discussion", "References"], label="Target Section", value="Entire Document")
                language_dropdown = gr.Dropdown(["English", "Farsi"], label="Response Language", value="English", interactive=True)
                detail = gr.Dropdown(["Brief", "Detailed"], label="Detail Level", value="Detailed")
                visualize = gr.Checkbox(label="Generate Visualization", value=False)
                submit = gr.Button("Submit")
            with gr.Column():
                output = gr.Textbox(label="Processing Result", lines=15, placeholder="Results will be displayed here...")

        submit.click(
            fn=academic_chatbot,
            inputs=[pdf_input, mode, query, language_dropdown, detail, section, visualize],
            outputs=output
        )

    iface.launch()