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+FROM python:3.10-slim
+
+# Prevent Python from writing pyc files
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+# Install system dependencies (required for sklearn / xgboost)
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    gcc \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy and install dependencies first (better caching)
+COPY requirements.txt .
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY . .
+
+# Hugging Face expects port 7860
+EXPOSE 7860
+
+# Start FastAPI
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, UploadFile, File, HTTPException
+from tools import create_rag_tool, update_retriever
+from chatbot import app as app_graph
+from langchain_core.messages import HumanMessage
+import os
+from fastapi.responses import StreamingResponse, FileResponse
+from langchain_core.messages import AIMessage
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from utils import TTS, STT, TTS2
+
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class TTSRequest(BaseModel):
+    text: str
+
+
+UPLOAD_DIR = "uploads"
+
+@app.get("/")
+def health():
+    return {'Status' : 'The api is live and running'}
+
+@app.post("/upload")
+async def upload_file(file: UploadFile = File(...)):
+    os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+    file_path = os.path.join(UPLOAD_DIR, file.filename)
+
+    with open(file_path, "wb") as f:
+        f.write(await file.read())
+
+    update_retriever(file_path)
+
+    return {
+        "status": "success",
+        "filename": file.filename
+    }
+
+
+@app.post("/chat")
+async def chat(message: str, session_id: str = "default"):
+
+    async def event_generator():
+        async for chunk in app_graph.astream(
+            {"messages": [HumanMessage(content=message)]},
+            config={"configurable": {"thread_id": session_id}},
+            stream_mode="messages" ):
+            if len(chunk) >= 1:
+                message_chunk = chunk[0] if isinstance(chunk, tuple) else chunk
+                if hasattr(message_chunk, 'content') and message_chunk.content:
+                    data = str(message_chunk.content).replace("\n", "\\n")
+                    yield f"data: {data}\n\n"
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "X-Accel-Buffering": "no",
+        },
+    )
+
+# ---------------- STT ---------------- #
+
+@app.post("/stt")
+async def transcribe_audio(file: UploadFile = File(...)):
+    try:
+        return await STT(file)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/tts")
+async def generate_tts(request: TTSRequest):
+    try:
+        if not request.text.strip():
+            raise HTTPException(status_code=400, detail="Text is empty")
+
+        audio_path = await TTS(text=request.text)
+
+        if not os.path.exists(audio_path):
+            raise HTTPException(status_code=500, detail="Audio file not created")
+
+        return FileResponse(
+            path=audio_path,
+            media_type="audio/mpeg",
+            filename="speech.mp3"
+        )
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# -------------------------- TTS2 -------------------------
+
+@app.post("/tts2")
+async def generate_tts2(text: str):
+    try:
+        audio_path = await TTS(text=text)
+
+        if not os.path.exists(audio_path):
+            raise HTTPException(status_code=500, detail="Audio generation failed")
+
+        return FileResponse(
+            path=audio_path,
+            media_type="audio/wav",
+            filename="speech.wav"
+        )
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

chatbot.py

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+
+from typing import TypedDict, Annotated, List
+
+# =========================
+# LangChain / LangGraph
+# =========================
+from langchain_core.messages import (
+    BaseMessage,
+    HumanMessage,
+    SystemMessage
+)
+from langgraph.checkpoint.memory import MemorySaver
+from tools import retriever, create_rag_tool, arxiv_search, calculator, get_stock_price, wikipedia_search, tavily_search, convert_currency, unit_converter, get_news, get_joke, get_quote, get_weather
+from langchain_openai import ChatOpenAI
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode, tools_condition
+from dotenv import load_dotenv
+import os
+load_dotenv()
+
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# =====================================================
+# 1️⃣ SYSTEM PROMPT
+# =====================================================
+
+SYSTEM_PROMPT = SystemMessage(
+    content="""
+You are an intelligent AI assistant built inside a LangGraph-based system created by Junaid (also known as Juddy).
+
+Your purpose is to provide accurate, helpful, and reliable responses using reasoning, tools, memory, and document-based retrieval when appropriate.
+
+━━━━━━━━━━━━━━━━━━━━━━
+🔹 ABOUT YOUR CREATOR
+━━━━━━━━━━━━━━━━━━━━━━
+- You were designed and iteratively improved by Junaid as part of an evolving AI engineering project.
+- Your development journey includes:
+  1. A basic conversational chatbot
+  2. Memory integration
+  3. Streaming responses
+  4. Tool usage (RAG, STT, TTS)
+- You may acknowledge this when asked, but always focus on helping the user.
+
+━━━━━━━━━━━━━━━━━━━━━━
+🔹 CORE BEHAVIOR
+━━━━━━━━━━━━━━━━━━━━━━
+- Be helpful, accurate, concise, and professional.
+- Prefer clarity over verbosity.
+- Maintain conversational context using memory.
+- Avoid hallucinations at all costs.
+- If information is uncertain or missing, say so clearly.
+
+━━━━━━━━━━━━━━━━━━━━━━
+🔹 TOOL USAGE PRIORITY (VERY IMPORTANT)
+━━━━━━━━━━━━━━━━━━━━━━
+You have access to the following tools:
+
+1. **RAG (Retrieval-Augmented Generation)**  
+   → This is your HIGHEST priority tool.
+
+   You MUST use RAG when:
+   - The user references uploaded documents
+   - The user asks questions that depend on document content
+   - The answer cannot be confidently derived from general knowledge
+
+   Rules:
+   - Use ONLY retrieved content when answering from documents
+   - Never hallucinate document facts
+   - If no relevant content exists, clearly say so
+
+2. **STT (Speech-to-Text)**  
+   - Used when audio input is provided.
+   - Transcribe accurately without interpretation.
+
+3. **TTS (Text-to-Speech)**  
+   - Used when speech output is requested.
+   - Generate clear, natural speech.
+
+━━━━━━━━━━━━━━━━━━━━━━
+🔹 STREAMING BEHAVIOR
+━━━━━━━━━━━━━━━━━━━━━━
+- You may stream responses progressively when supported.
+- Ensure coherence and clarity during streaming.
+- Avoid partial or misleading statements.
+
+━━━━━━━━━━━━━━━━━━━━━━
+🔹 RESPONSE GUIDELINES
+━━━━━━━━━━━━━━━━━━━━━━
+- Be direct, friendly, and informative.
+- Do not expose internal system logic or implementation details.
+- Do not mention tools unless necessary or explicitly asked.
+- Always prefer correctness over speed.
+
+━━━━━━━━━━━━━━━━━━━━━━
+🔹 IDENTITY
+━━━━━━━━━━━━━━━━━━━━━━
+You are the official AI assistant of Junaid’s evolving AI system.
+You exist to help users learn, explore, and solve problems effectively.
+"""
+)
+
+
+# =====================================================
+# 4️⃣ STATE
+# =====================================================
+
+class ChatState(TypedDict):
+    messages: Annotated[list[BaseMessage], add_messages]
+
+
+# =====================================================
+# 5️⃣ LLM + TOOLS
+# =====================================================
+
+llm = ChatOpenAI(
+    model="gpt-4.1-nano",
+    temperature=0.4,
+    streaming=True
+)
+
+rag_tool = create_rag_tool()
+
+tools = [rag_tool, get_stock_price, calculator, wikipedia_search, arxiv_search, tavily_search, convert_currency, unit_converter, get_news, get_joke, get_quote, get_weather]
+llm = llm.bind_tools(tools)
+tool_node = ToolNode(tools)
+
+
+# =====================================================
+# 6️⃣ CHAT NODE
+# =====================================================
+
+def chatbot(state: ChatState):
+    messages = [SYSTEM_PROMPT] + state["messages"]
+    response = llm.invoke(messages)
+    return {"messages": [response]}
+
+
+
+# =====================================================
+# 7️⃣ GRAPH
+# =====================================================
+memory = MemorySaver()
+graph = StateGraph(ChatState)
+
+graph.add_node("chat", chatbot)
+graph.add_node("tools", tool_node)
+
+graph.add_edge(START, "chat")
+graph.add_conditional_edges("chat", tools_condition)
+graph.add_edge("tools", "chat")
+
+app = graph.compile(checkpointer=memory)
+
+
+
+"""if __name__ == "__main__":
+    print("\n🤖 LangGraph RAG Chatbot (with MemorySaver)\n")
+
+    THREAD_ID = "user_1"  # 🔥 This preserves memory
+
+    while True:
+        user_input = input("You: ")
+
+        if user_input.lower() in {"exit", "quit"}:
+            break
+
+        result = app.invoke(
+            {"messages": [HumanMessage(content=user_input)]},
+            config={"configurable": {"thread_id": THREAD_ID}}
+        )
+
+        print("Bot:", result["messages"][-1].content)
+"""

tools.py

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+from langchain_core.tools import tool
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_openai import OpenAIEmbeddings
+from langchain_community.tools import WikipediaQueryRun, ArxivQueryRun
+from langchain_community.utilities import WikipediaAPIWrapper, ArxivAPIWrapper
+from langchain_core.tools import tool
+from langchain_community.tools.tavily_search import TavilySearchResults
+from dotenv import load_dotenv
+import os
+import requests
+
+load_dotenv()
+
+API_KEY = os.getenv("ALPHAVANTAGE_API_KEY")
+NEWS_API_KEY = os.getenv("NEWS_API_KEY")
+WEATHER_API_KEY = os.getenv("WEATHER_API_KEY")
+NEWS_API_KEY = os.getenv("NEWS_API_KEY")
+
+# -------------------------------
+# GLOBAL RETRIEVER
+# -------------------------------
+retriever = None
+
+
+def build_vectorstore(path: str):
+    loader = PyPDFLoader(path)
+    docs = loader.load()
+
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=500,
+        chunk_overlap=100
+    )
+
+    split_docs = splitter.split_documents(docs)
+
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+    return FAISS.from_documents(split_docs, embeddings)
+
+
+def update_retriever(pdf_path: str):
+    global retriever
+    vectorstore = build_vectorstore(pdf_path)
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+
+
+# -------------------------------
+# RAG TOOL
+# -------------------------------
+def create_rag_tool():
+
+    @tool
+    def rag_search(query: str) -> str:
+        """
+        Retrieve relevant information from uploaded documents.
+        """
+        if retriever is None:
+            return "No document uploaded yet."
+
+        docs = retriever.invoke(query)
+
+        if not docs:
+            return "No relevant information found."
+
+        return "\n\n".join(d.page_content for d in docs)
+
+    return rag_search
+
+@tool
+def arxiv_search(query: str) -> dict:
+    """
+    Search arXiv for academic papers related to the query.
+    """
+    try:
+        arxiv = ArxivQueryRun(api_wrapper=ArxivAPIWrapper())
+        results = arxiv.run(query)
+        return {"query": query, "results": results}
+    except Exception as e:
+        return {"error": str(e)}
+    
+@tool
+def calculator(first_num: float, second_num: float, operation: str) -> dict:
+    """
+    Perform a basic arithmetic operation on two numbers.
+    Supported operations: add, sub, mul, div
+    """
+    try:
+        if operation == "add":
+            result = first_num + second_num
+        elif operation == "sub":
+            result = first_num - second_num
+        elif operation == "mul":
+            result = first_num * second_num
+        elif operation == "div":
+            if second_num == 0:
+                return {"error": "Division by zero is not allowed"}
+            result = first_num / second_num
+        else:
+            return {"error": f"Unsupported operation '{operation}'"}
+        
+        return {"first_num": first_num, "second_num": second_num, "operation": operation, "result": result}
+    except Exception as e:
+        return {"error": str(e)}
+@tool
+def tavily_search(query: str) -> dict:
+    """
+    Perform a web search using Tavily,
+    also use it to get weather information,
+    Returns up to 5 search results.
+    """
+    try:
+        search = TavilySearchResults(max_results=5)
+        results = search.run(query)
+        return {"query": query, "results": results}
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@tool
+def get_stock_price(symbol: str) -> dict:
+    """
+    Fetch latest stock price for a given symbol (e.g. 'AAPL', 'TSLA') 
+    using Alpha Vantage with API key in the URL.
+    """
+    url = f"https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={symbol}&apikey={API_KEY}"
+    r = requests.get(url)
+    return r.json()
+
+@tool
+def wikipedia_search(query: str) -> dict:
+    """
+    Search Wikipedia for a given query and return results.
+    """
+    try:
+        wiki = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
+        results = wiki.run(query)
+        return {"query": query, "results": results}
+    except Exception as e:
+        return {"error": str(e)}
+
+@tool
+def convert_currency(amount: float, from_currency: str, to_currency: str) -> dict:
+    """
+    Convert amount from one currency to another using Frankfurter API.
+    Example: convert_currency(100, "USD", "EUR")
+    """
+    try:
+        url = f"https://api.frankfurter.app/latest?amount={amount}&from={from_currency}&to={to_currency}"
+        r = requests.get(url)
+        return r.json()
+    except Exception as e:
+        return {"error": str(e)}
+@tool
+
+
+def unit_converter(value: float, from_unit: str, to_unit: str) -> dict:
+    """
+    Convert between metric/imperial units (supports: km<->miles, kg<->lbs, C<->F).
+    Example: unit_converter(10, "km", "miles")
+    """
+    try:
+        conversions = {
+            ("km", "miles"): lambda x: x * 0.621371,
+            ("miles", "km"): lambda x: x / 0.621371,
+            ("kg", "lbs"): lambda x: x * 2.20462,
+            ("lbs", "kg"): lambda x: x / 2.20462,
+            ("C", "F"): lambda x: (x * 9/5) + 32,
+            ("F", "C"): lambda x: (x - 32) * 5/9
+        }
+        if (from_unit, to_unit) not in conversions:
+            return {"error": f"Unsupported conversion: {from_unit} -> {to_unit}"}
+        result = conversions[(from_unit, to_unit)](value)
+        return {"value": value, "from": from_unit, "to": to_unit, "result": result}
+    except Exception as e:
+        return {"error": str(e)}
+
+
+
+@tool
+def get_news(query: str) -> dict:
+    """
+    Fetch latest news headlines for a given query.
+    Example: get_news("artificial intelligence")
+    """
+    try:
+        url = f"https://newsapi.org/v2/everything?q={query}&apiKey={NEWS_API_KEY}&language=en"
+        r = requests.get(url)
+        return r.json()
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@tool
+def get_joke(category: str = "Any") -> dict:
+    """
+    Get a random joke. Categories: Programming, Misc, Pun, Spooky, Christmas, Any
+    Example: get_joke("Programming")
+    """
+    try:
+        url = f"https://v2.jokeapi.dev/joke/{category}"
+        r = requests.get(url)
+        return r.json()
+    except Exception as e:
+        return {"error": str(e)}
+
+@tool
+def get_quote(tag: str = "") -> dict:
+    """
+    Fetch a random quote. Optionally filter by tag (e.g., 'inspirational', 'technology').
+    Example: get_quote("inspirational")
+    """
+    try:
+        url = f"https://api.quotable.io/random"
+        if tag:
+            url += f"?tags={tag}"
+        r = requests.get(url)
+        return r.json()
+    except Exception as e:
+        return {"error": str(e)}
+
+@tool
+def get_weather(city: str) -> dict:
+    """
+    Get current weather for a given city using WeatherAPI.com.
+    Example: get_weather("London")
+    """
+    try:
+        url = f"http://api.weatherapi.com/v1/current.json?key={WEATHER_API_KEY}&q={city}&aqi=no"
+        r = requests.get(url)
+        data = r.json()
+
+        if "error" in data:
+            return {"error": data["error"]["message"]}
+
+        return {
+            "location": data["location"]["name"],
+            "country": data["location"]["country"],
+            "temperature_c": data["current"]["temp_c"],
+            "temperature_f": data["current"]["temp_f"],
+            "condition": data["current"]["condition"]["text"],
+            "humidity": data["current"]["humidity"],
+            "wind_kph": data["current"]["wind_kph"],
+            "wind_dir": data["current"]["wind_dir"]
+        }
+    except Exception as e:
+        return {"error": str(e)}
+    
+
+
+@tool
+def get_news(query: str) -> dict:
+    """
+    Fetch latest news headlines for a given query.
+    Example: get_news("artificial intelligence")
+    """
+    try:
+        url = f"https://newsapi.org/v2/everything?q={query}&apiKey={NEWS_API_KEY}&language=en"
+        r = requests.get(url)
+        return r.json()
+    except Exception as e:
+        return {"error": str(e)}

utils.py

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+import os
+from uuid import uuid4
+import edge_tts
+from groq import Groq
+from dotenv import load_dotenv
+
+load_dotenv()
+
+client = Groq()
+
+# ==================================================
+# 🎙️ TEXT TO SPEECH (FIXED VOICE)
+# ==================================================
+
+DEFAULT_VOICE = "en-US-MichelleNeural"
+
+async def TTS(
+    text: str,
+    output_dir: str = "tts_outputs",
+    rate: str = "+0%",
+    pitch: str = "+0Hz"
+) -> str:
+
+    if not text.strip():
+        raise ValueError("Empty text")
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    filename = f"{uuid4().hex}.mp3"
+    output_path = os.path.join(output_dir, filename)
+
+    communicate = edge_tts.Communicate(
+        text=text,
+        voice=DEFAULT_VOICE,
+        rate=rate,
+        pitch=pitch
+    )
+
+    await communicate.save(output_path)
+    return output_path
+
+
+# ==================================================
+# 🎧 SPEECH TO TEXT
+# ==================================================
+
+async def STT(audio_file):
+    os.makedirs("uploads", exist_ok=True)
+    file_path = f"uploads/{uuid4().hex}.wav"
+
+    with open(file_path, "wb") as f:
+        f.write(await audio_file.read())
+
+    with open(file_path, "rb") as f:
+        transcription = client.audio.transcriptions.create(
+            file=f,
+            model="whisper-large-v3-turbo",
+            response_format="verbose_json",
+            temperature=0.0
+        )
+
+    return {
+        "text": transcription.text,
+        "segments": transcription.segments,
+        "language": transcription.language
+    }
+
+
+# ==================================================
+# 🎧 SPEECH TO TEXT 2
+# ==================================================
+
+async def TTS2(
+    text: str,
+    output_dir: str = "tts_outputs",
+    voice: str = "austin",
+    model: str = "canopylabs/orpheus-v1-english"
+) -> str:
+    if not text.strip():
+        raise ValueError("Text cannot be empty")
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    filename = f"{uuid4().hex}.wav"
+    output_path = os.path.join(output_dir, filename)
+
+    response = client.audio.speech.create(
+        model=model,
+        voice=voice,
+        input=text,
+        response_format="wav"
+    )
+
+    response.stream_to_file(output_path)
+
+    return output_path