app.py

import os
import re
import logging
import tempfile
from pathlib import Path
from typing import List,Tuple,Any
import numpy as np
import PyPDF2
from sentence_transformers import SentenceTransformer
import faiss
import gradio as gr
from gtts import gTTS
import requests
import math
import ast
import json

try:
    import sympy as sp
    SYMPY_OK = True
except Exception:
    SYMPY_OK = False

try:
    from groq import Groq
    GROQ_OK = True
except ImportError:
    GROQ_OK = False
    print("Groq library not installed!")

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

GROQ_API_KEY = os.getenv("GROQ_API_KEY","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")
groq_client = None

if GROQ_OK:
    try:
        groq_client = Groq(api_key=GROQ_API_KEY)
        print("Groq client initialized successfully!")
    except Exception as e:
        groq_client = None
        print(f"Groq initialization error: {e}")


class SafeEval(ast.NodeVisitor):
    ALLOWED_NAMES = {n: getattr(math,n) for n in dir(math) if not n.startswith("__")}
    ALLOWED_NAMES.update({"abs": abs,"round": round,"pi": math.pi,"e": math.e})

    def visit(self,node):
        if isinstance(node,ast.Expression):
            return self.visit(node.body)
        if isinstance(node,ast.BinOp):
            left = self.visit(node.left)
            right = self.visit(node.right)
            return self._binop(node.op,left,right)
        if isinstance(node,ast.UnaryOp):
            operand = self.visit(node.operand)
            return self._unaryop(node.op,operand)
        if isinstance(node,ast.Num):
            return node.n
        if isinstance(node,ast.Constant) and isinstance(node.value,(int,float)):
            return node.value
        if isinstance(node,ast.Call):
            func = node.func
            if isinstance(func,ast.Name) and func.id in self.ALLOWED_NAMES:
                args = [self.visit(a) for a in node.args]
                return self.ALLOWED_NAMES[func.id](*args)
        if isinstance(node,ast.Name):
            if node.id in self.ALLOWED_NAMES:
                return self.ALLOWED_NAMES[node.id]
            raise ValueError(f"Use of name '{node.id}' is not allowed")
        raise ValueError(f"Unsupported expression: {ast.dump(node)}")

    def _binop(self,op,a,b):
        if isinstance(op,ast.Add): return a + b
        if isinstance(op,ast.Sub): return a - b
        if isinstance(op,ast.Mult): return a * b
        if isinstance(op,ast.Div): return a / b
        if isinstance(op,ast.Mod): return a % b
        if isinstance(op,ast.Pow): return a ** b
        if isinstance(op,ast.FloorDiv): return a // b
        raise ValueError("Unsupported binary operator")

    def _unaryop(self,op,a):
        if isinstance(op,ast.UAdd): return +a
        if isinstance(op,ast.USub): return -a
        raise ValueError("Unsupported unary operator")


def safe_calc_eval(expr: str):
    expr = expr.strip()
    expr = expr.replace('^','**')
    expr = expr.replace('x','*').replace('X','*')
    expr = expr.replace('×','*').replace('÷','/')
    
    if SYMPY_OK:
        try:
            result = sp.sympify(expr)
            numeric = float(result.evalf())
            return True,str(numeric)
        except:
            pass
    try:
        node = ast.parse(expr,mode='eval')
        se = SafeEval()
        val = se.visit(node)
        return True,str(val)
    except Exception as e:
        return False,f"Calc error: {e}"


def get_stock_price(symbol: str) -> dict:
    symbol = symbol.upper().strip()
    try:
        url = f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}"
        headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"}
        resp = requests.get(url,headers=headers,timeout=10)
        resp.raise_for_status()
        data = resp.json()
        
        if "chart" in data and "result" in data["chart"] and data["chart"]["result"]:
            result = data["chart"]["result"][0]
            meta = result.get("meta",{})
            
            current_price = meta.get("regularMarketPrice",0)
            previous_close = meta.get("previousClose",0)
            currency = meta.get("currency","USD")
            exchange = meta.get("exchangeName","Unknown")
            name = meta.get("shortName",symbol)
            
            change = current_price - previous_close if previous_close else 0
            change_percent = (change / previous_close * 100) if previous_close else 0
            
            return {
                "success": True,
                "symbol": symbol,
                "name": name,
                "price": round(current_price,2),
                "change": round(change,2),
                "change_percent": round(change_percent,2),
                "previous_close": round(previous_close,2),
                "currency": currency,
                "exchange": exchange
            }
        return {"success": False,"error": f"No data for {symbol}"}
    except Exception as e:
        logger.error(f"Stock API error: {e}")
        return {"success": False,"error": str(e)}


def extract_stock_symbol(question: str) -> str:
    question_upper = question.upper()
    
    known_stocks = {
        "CARECLOUD": "MTBC","CARE CLOUD": "MTBC","MTBC": "MTBC",
        "APPLE": "AAPL","GOOGLE": "GOOGL","ALPHABET": "GOOGL",
        "MICROSOFT": "MSFT","AMAZON": "AMZN","TESLA": "TSLA",
        "META": "META","FACEBOOK": "META","NVIDIA": "NVDA",
        "NETFLIX": "NFLX","INTEL": "INTC","AMD": "AMD",
        "PAYPAL": "PYPL","DISNEY": "DIS","WALMART": "WMT",
        "NIKE": "NKE","BOEING": "BA","UBER": "UBER",
        "ZOOM": "ZM","SPOTIFY": "SPOT"
    }
    
    for name,symbol in known_stocks.items():
        if name in question_upper:
            logger.info(f"Found stock: {name} -> {symbol}")
            return symbol
    
    common_words = {'THE','AND','FOR','ARE','BUT','NOT','YOU','ALL',
                    'STOCK','PRICE','CURRENT','TELL','ABOUT','WHAT','HOW'}
    
    words = re.findall(r'\b[A-Z]{2,5}\b',question_upper)
    for word in words:
        if word not in common_words:
            return word
    return ""


def web_search(query: str,max_results: int = 5) -> List[dict]:
    try:
        resp = requests.get(
            "https://html.duckduckgo.com/html/",
            params={"q": query},
            timeout=10,
            headers={"User-Agent": "Mozilla/5.0"}
        )
        resp.raise_for_status()
        text = resp.text
        results = []
        
        parts = text.split('result__a')
        for part in parts[1:max_results+1]:
            title = ""
            snippet = ""
            try:
                title_match = re.search(r'>([^<]+)<',part)
                title = title_match.group(1) if title_match else ""
            except:
                pass
            try:
                if 'result__snippet' in part:
                    snippet_part = part.split('result__snippet')[1]
                    snippet_match = re.search(r'>([^<]+)<',snippet_part)
                    snippet = snippet_match.group(1) if snippet_match else ""
            except:
                pass
            if title or snippet:
                results.append({"title": title.strip(),"snippet": snippet.strip()})
        return results
    except Exception as e:
        logger.error(f"Web search error: {e}")
        return []


class AgenticRAGAgent:
    def __init__(self):
        self.chunks = []
        self.index = None
        self.embedder = SentenceTransformer('all-MiniLM-L6-v2')
        self.temperature = 0.3
        self.max_tokens = 1000
        self.chunk_size = 512
        self.chunk_overlap = 50
        self.retrieval_k = 10
        self.enable_web_search = True
        self.enable_calculations = True
        self.enable_fact_checking = True
        self.enable_analysis = True
        self.enable_stock_lookup = True
        self.relevance_threshold = 0.35
        self.pdf_loaded = False
        print("AgenticRAGAgent initialized")

    def remove_emojis(self,text: str) -> str:
        emoji_pattern = re.compile("["
            u"\U0001F600-\U0001F64F"
            u"\U0001F300-\U0001F5FF"
            u"\U0001F680-\U0001F6FF"
            u"\U0001F1E0-\U0001F1FF"
            u"\U00002702-\U000027B0"
            u"\U000024C2-\U0001F251"
            "]+",flags=re.UNICODE)
        return emoji_pattern.sub(r'',text)

    def clean_for_voice(self,text: str) -> str:
        text = self.remove_emojis(text)
        text = re.sub(r'[\*_`#\[\]\|]','',text)
        text = re.sub(r'\s+',' ',text).strip()
        return text

    def generate_voice(self,text: str):
        if not text or not text.strip():
            return None
        clean = self.clean_for_voice(text)
        if len(clean) < 5:
            return None
        try:
            tts = gTTS(text=clean[:500],lang='en',slow=False)
            tmp = tempfile.NamedTemporaryFile(delete=False,suffix=".mp3")
            tts.save(tmp.name)
            return tmp.name
        except Exception as e:
            logger.error(f"Voice error: {e}")
            return None

    def upload_pdfs(self,files):
        if not files:
            return "No files selected."
        folder = Path("sample_data")
        folder.mkdir(exist_ok=True)
        all_chunks = []
        count = 0
        for file in files:
            filename = str(file.name) if hasattr(file,'name') else str(file)
            if not filename.lower().endswith('.pdf'):
                continue
            dest = folder / Path(filename).name
            try:
                content = file.read() if hasattr(file,'read') else open(filename,'rb').read()
                with open(dest,"wb") as f:
                    f.write(content)
            except Exception as e:
                continue
            text = ""
            try:
                with open(dest,'rb') as f:
                    reader = PyPDF2.PdfReader(f)
                    for page in reader.pages:
                        t = page.extract_text()
                        if t:
                            text += t + " "
            except Exception as e:
                continue
            if text.strip():
                chunks = [text[i:i+self.chunk_size] for i in range(0,len(text),self.chunk_size - self.chunk_overlap)]
                all_chunks.extend([{"content": str(c.strip())} for c in chunks if c.strip()])
                count += 1
        if not all_chunks:
            return "No readable text in PDFs."
        vecs = self.embedder.encode([c["content"] for c in all_chunks],show_progress_bar=True)
        vecs = vecs / np.linalg.norm(vecs,axis=1,keepdims=True)
        dim = vecs.shape[1]
        self.index = faiss.IndexFlatIP(dim)
        self.index.add(vecs.astype('float32'))
        self.chunks = all_chunks
        self.pdf_loaded = True
        return f"Loaded {count} PDF(s) with {len(all_chunks)} chunks!"

    def is_stock_question(self,question: str) -> Tuple[bool,str]:
        question_lower = question.lower()
        stock_keywords = ['stock','share','price','trading','ticker','nasdaq','nyse','market']
        known_companies = ['carecloud','mtbc','apple','google','microsoft','amazon',
                          'tesla','meta','nvidia','netflix','intel','amd']
        
        has_keyword = any(kw in question_lower for kw in stock_keywords)
        has_company = any(co in question_lower for co in known_companies)
        
        if has_keyword or has_company:
            symbol = extract_stock_symbol(question)
            if symbol:
                logger.info(f"Stock question detected: {symbol}")
                return True,symbol
        return False,""

    def is_calculation_question(self,question: str) -> Tuple[bool,str]:
        question_lower = question.lower()
        
        calc_keywords = ['calculate','compute','solve','calcuate','calc']
        has_calc_word = any(kw in question_lower for kw in calc_keywords)
        
        math_match = re.search(r'(\d+)\s*[\*xX×\+\-\/÷\^]\s*(\d+)',question)
        if math_match:
            expr = math_match.group(0)
            expr = expr.replace('x','*').replace('X','*').replace('×','*').replace('÷','/')
            logger.info(f"Math expression found: {expr}")
            return True,expr
        
        pure_math = re.match(r'^[\d\s\+\-\*\/\^\(\)\.xX×÷]+$',question.strip())
        if pure_math:
            expr = question.strip()
            expr = expr.replace('x','*').replace('X','*').replace('×','*').replace('÷','/')
            return True,expr
        
        if has_calc_word:
            nums = re.findall(r'\d+',question)
            if len(nums) >= 2:
                expr = f"{nums[0]}*{nums[1]}"
                return True,expr
        
        return False,""

    def is_pdf_related_question(self,question: str) -> bool:
        pdf_keywords = ['pdf','document','file','attached','uploaded','summarize',
                       'summary','in the document','from the document','the paper']
        question_lower = question.lower()
        return any(kw in question_lower for kw in pdf_keywords)

    def is_general_knowledge_question(self,question: str) -> bool:
        question_lower = question.lower()
        if 'stock' in question_lower or 'price' in question_lower:
            return False
        if re.search(r'\d+\s*[\*\+\-\/]\s*\d+',question):
            return False
        general_triggers = ['what is ai','how does','explain','tell me about',
                           'history of','future of','definition']
        return any(t in question_lower for t in general_triggers)

    def check_context_relevance(self,question: str,context: str,scores: np.ndarray) -> Tuple[bool,float]:
        if not context:
            return False,0.0
        max_score = float(np.max(scores)) if len(scores) > 0 else 0.0
        stop_words = {'what','is','the','a','how','tell','me','about','stock','price'}
        q_terms = [w.lower() for w in re.findall(r'\b\w+\b',question) if w.lower() not in stop_words and len(w) > 2]
        matches = sum(1 for t in q_terms if t in context.lower())
        coverage = matches / len(q_terms) if q_terms else 0
        is_relevant = max_score >= self.relevance_threshold and coverage >= 0.3
        return is_relevant,max_score

    def determine_tool(self,question: str) -> Tuple[str,str]:
        logger.info(f"Determining tool for: {question}")
        
        is_stock,symbol = self.is_stock_question(question)
        if is_stock and symbol:
            logger.info(f"Tool: STOCK,Symbol: {symbol}")
            return 'stock',symbol
        
        is_calc,expr = self.is_calculation_question(question)
        if is_calc and expr:
            logger.info(f"Tool: CALCULATOR,Expression: {expr}")
            return 'calculator',expr
        
        if self.is_pdf_related_question(question):
            if self.pdf_loaded:
                logger.info("Tool: PDF")
                return 'pdf',''
        
        if self.is_general_knowledge_question(question):
            logger.info("Tool: WEB")
            return 'web',''
        
        if self.pdf_loaded:
            return 'check_pdf',''
        
        logger.info("Tool: WEB (default)")
        return 'web',''

    def perform_analysis(self,answer: str,tools_used: List[str]) -> str:
        if not self.enable_analysis or not answer:
            return ""
        analysis = []
        for tool in tools_used:
            if tool == "PDF":
                analysis.append("📄 Source: PDF Documents")
            elif tool == "Web":
                analysis.append("🌐 Source: Web Search")
            elif tool == "Calculator":
                analysis.append("🧮 Source: Calculator")
            elif tool == "Stock":
                analysis.append("📈 Source: Yahoo Finance (Real-time)")
        word_count = len(answer.split())
        analysis.append(f"📊 Response: {word_count} words")
        if analysis:
            return "\n\n[📊 Analysis]\n• " + "\n• ".join(analysis)
        return ""

    def ask(self,question: str,history: List) -> Tuple[List,Any]:
        global groq_client
        
        if not isinstance(question,str):
            question = str(question) if question else ""
        if not isinstance(history,list):
            history = []
        
        question = question.strip()
        if not question:
            return history,None

        if question.lower() in ["hi","hello","hey"]:
            reply = "👋 Hi!  I can help with:\n• 📈 Stock prices (try: 'stock price of MTBC')\n• 🧮 Calculations (try: '2*4')\n• 📄 PDF questions\n• 🌐 Web search"
            history.append([question,reply])
            return history,self.generate_voice(reply)

        tools_used = []
        reply = ""

        tool,extra = self.determine_tool(question)
        logger.info(f"Selected tool: {tool},extra: {extra}")

        # STOCK TOOL
        if tool == 'stock' and extra:
            stock_data = get_stock_price(extra)
            if stock_data.get("success"):
                change_emoji = "📈" if stock_data["change"] >= 0 else "📉"
                sign = "+" if stock_data["change"] >= 0 else ""
                reply = f"""## 📈 {stock_data['name']} ({stock_data['symbol']})
**Current Price:** ${stock_data['price']} {stock_data['currency']}
**Change:** {change_emoji} {sign}${stock_data['change']} ({sign}{stock_data['change_percent']}%)
**Previous Close:** ${stock_data['previous_close']}
**Exchange:** {stock_data['exchange']}
*Real-time data from Yahoo Finance*"""
                tools_used.append("Stock")
            else:
                tool = 'web'

        # CALCULATOR TOOL
        if tool == 'calculator' and extra:
            ok,result = safe_calc_eval(extra)
            if ok:
                reply = f"""## 🧮 Calculator
**Expression:** `{extra}`
**Result:** **{result}**"""
                tools_used.append("Calculator")
            else:
                reply = f"Calculation error: {result}"
                tools_used.append("Calculator")

        # PDF TOOL
        if tool in ['pdf','check_pdf'] and self.index:
            try:
                q_vec = self.embedder.encode([question])
                q_vec = q_vec / np.linalg.norm(q_vec)
                scores,indices = self.index.search(q_vec.astype('float32'),k=self.retrieval_k)
                context_list = [self.chunks[i]["content"] for i in indices[0] if i < len(self.chunks)]
                context = "\n\n".join(context_list)
                
                if tool == 'pdf' or self.check_context_relevance(question,context,scores[0])[0]:
                    tools_used.append("PDF")
                    prompt = f"Document:\n{context}\n\nQuestion: {question}\n\nAnswer based on the document:"
                    if groq_client:
                        resp = groq_client.chat.completions.create(
                            model="llama-3.3-70b-versatile",
                            messages=[{"role": "user","content": prompt}],
                            temperature=self.temperature,
                            max_tokens=self.max_tokens
                        )
                        reply = resp.choices[0].message.content.strip()
                else:
                    tool = 'web'
            except Exception as e:
                logger.error(f"PDF error: {e}")
                tool = 'web'

        # WEB SEARCH TOOL
        if tool == 'web' and not reply:
            results = web_search(question)
            if results:
                tools_used.append("Web")
                web_text = "\n".join([f"- {r['title']}: {r['snippet']}" for r in results[:3]])
                prompt = f"Web results:\n{web_text}\n\nQuestion: {question}\n\nProvide a helpful answer:"
                if groq_client:
                    try:
                        resp = groq_client.chat.completions.create(
                            model="llama-3.3-70b-versatile",
                            messages=[{"role": "user","content": prompt}],
                            temperature=self.temperature,
                            max_tokens=self.max_tokens
                        )
                        reply = resp.choices[0].message.content.strip()
                        reply += "\n\n🌐 **Web Sources:**\n" + "\n".join([f"• {r['title']}" for r in results[:3]])
                    except Exception as e:
                        reply = f"Error: {e}"
                else:
                    reply = "Web results:\n" + web_text

        # FALLBACK
        if not reply:
            if groq_client:
                try:
                    resp = groq_client.chat.completions.create(
                        model="llama-3.3-70b-versatile",
                        messages=[{"role": "user","content": question}],
                        temperature=self.temperature,
                        max_tokens=self.max_tokens
                    )
                    reply = resp.choices[0].message.content.strip()
                    tools_used.append("LLM")
                except Exception as e:
                    reply = f"Error: {e}"
            else:
                reply = "Unable to process request."

        # Add analysis
        analysis = self.perform_analysis(reply,tools_used)
        if analysis:
            reply += analysis

        logger.info(f"Tools used: {tools_used}")
        history.append([question,reply])
        return history,self.generate_voice(reply)

    def update_settings(self,temp,tokens,chunk_size,overlap,k,web,calc,fact,analysis):
        self.temperature = float(temp)
        self.max_tokens = int(tokens)
        self.chunk_size = int(chunk_size)
        self.chunk_overlap = int(overlap)
        self.retrieval_k = int(k)
        self.enable_web_search = bool(web)
        self.enable_calculations = bool(calc)
        self.enable_fact_checking = bool(fact)
        self.enable_analysis = bool(analysis)
        return f"Settings updated!  Temp={temp},Tokens={tokens}"


def create_interface():
    agent = AgenticRAGAgent()

    with gr.Blocks(title="AI Research Agent") as interface:
        chat_memory = gr.State([])

        gr.HTML("""
        <div style="text-align:center;padding:20px;background:linear-gradient(135deg,#667eea 0%,#764ba2 100%);border-radius:15px;">
        <h1 style="color:white;">🤖 AI Research Agent</h1>
        <p style="color:white;">📈 Stocks | 🧮 Calculator | 📄 PDF | 🌐 Web Search</p>
        </div>
        """)

        with gr.Row():
            with gr.Column(scale=2):
                chatbot = gr.Chatbot(label="Chat",height=500)
                with gr.Row():
                    msg = gr.Textbox(placeholder="Try: 'stock price of MTBC' or '2*4' or 'summarize the PDF'",scale=4)
                    submit_btn = gr.Button("Send",variant="primary")
                clear_btn = gr.Button("Clear")
                audio_output = gr.Audio(label="Voice",autoplay=True)

            with gr.Column(scale=1):
                pdf_upload = gr.Files(file_types=[".pdf"],label="Upload PDFs")
                upload_status = gr.Textbox(label="Status",interactive=False)
                
                with gr.Accordion("Settings",open=False):
                    temp = gr.Slider(0,1,value=0.3,label="Temperature")
                    tokens = gr.Slider(100,2000,value=1000,label="Max Tokens")
                    chunk = gr.Slider(256,1024,value=512,label="Chunk Size")
                    overlap = gr.Slider(0,200,value=50,label="Overlap")
                    k = gr.Slider(3,15,value=10,label="Retrieval K")
                    web = gr.Checkbox(value=True,label="Web Search")
                    calc = gr.Checkbox(value=True,label="Calculator")
                    fact = gr.Checkbox(value=True,label="Fact Check")
                    analysis = gr.Checkbox(value=True,label="Analysis")
                    apply_btn = gr.Button("Apply")
                    status = gr.Textbox(label="Settings Status")

        def respond(message,history):
            new_history,audio = agent.ask(message,history)
            display = []
            for item in new_history:
                if isinstance(item,list) and len(item) == 2:
                    display.append({"role": "user","content": str(item[0])})
                    display.append({"role": "assistant","content": str(item[1])})
            return "",new_history,display,audio

        submit_btn.click(respond,[msg,chat_memory],[msg,chat_memory,chatbot,audio_output])
        msg.submit(respond,[msg,chat_memory],[msg,chat_memory,chatbot,audio_output])
        clear_btn.click(lambda: ([],[]),outputs=[chat_memory,chatbot])
        pdf_upload.change(agent.upload_pdfs,[pdf_upload],[upload_status])
        apply_btn.click(agent.update_settings,[temp,tokens,chunk,overlap,k,web,calc,fact,analysis],[status])

    return interface


if __name__ == "__main__":
    print("Starting AI Research Agent...")
    app = create_interface()
    app.launch(server_name="0.0.0.0",server_port=7860,show_error=True)