app.py

import os
from flask import Flask, request, jsonify, render_template
from flask_cors import CORS

from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_chroma import Chroma
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_groq import ChatGroq

app = Flask(__name__)
CORS(app)

print("="*50)
print("--- Dr. Rajeev's AI: GROQ CLOUD v7.0 ---")
print("="*50)

pdf_path = "my_docs/Rajeev_CV_N.pdf"
print(f"[*] Reading {pdf_path}...")
loader = PyPDFLoader(pdf_path)
pages = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
texts = text_splitter.split_documents(pages)

print("[*] Loading Embeddings...")
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
print("    [✔] Embeddings Ready.")

print("[*] Building Vector Database...")
vector_db = Chroma.from_documents(documents=texts, embedding=embeddings)

print("[*] Connecting to Groq (Llama3)...")
llm = ChatGroq(
   model="llama-3.3-70b-versatile",
    groq_api_key=os.environ.get("GROQ_API_KEY"),
    temperature=0.5,
    max_tokens=512,
)

retriever = vector_db.as_retriever()

template = """You are Dr. Rajeev Kumar Chauhan's personal AI assistant on his website.
Answer professionally and helpfully using ONLY the context provided below.
If the answer is not in the context, say "I don't have that information in my knowledge base."
Keep answers clear and concise. If the User Asks an Analyzing Question like Latest or Oldest so Analyze with Data, if the exact date is not available so Do the most that is available like year of month of date.
Context: {context}
Question: {question}
Answer:"""

prompt = ChatPromptTemplate.from_template(template)

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

print("\n[✔] AI READY. Server Starting...")
print("="*50)

@app.route("/")
def home():
    return render_template("index.html")

@app.route("/ask", methods=["POST"])
def ask():
    data = request.get_json()
    question = data.get("question", "")
    if not question:
        return jsonify({"error": "No question provided"}), 400
    try:
        answer = chain.invoke(question)
        return jsonify({"answer": answer})
    except Exception as e:
        print(f"ERROR: {e}")
        return jsonify({"answer": f"Error: {str(e)}"}), 500

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860, debug=False)