app.py

import os
from dotenv import load_dotenv
import gradio as gr


load_dotenv()
COHERE_API_KEY = os.getenv("COHERE_API_KEY")
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")

if not COHERE_API_KEY or not GEMINI_API_KEY:
    raise ValueError("COHERE_API_KEY or GEMINI_API_KEY is missing")


import cohere
import chromadb
from google import genai
from google.genai import types


co = cohere.Client(COHERE_API_KEY)


genai_client = genai.Client(api_key=GEMINI_API_KEY)


client = chromadb.Client()


collection = client.get_or_create_collection(name="inha-well", embedding_function=None)


total_docs = collection.count() if hasattr(collection, 'count') else len(collection.get()['documents'])

if total_docs == 0:
    content_chunks = []
    for i in range(1, 4):

        folder_path = os.path.join(os.getcwd(), "docs", f"p0000{i}")
        

        if not os.path.exists(folder_path):
            print(f"Warning: Folder {folder_path} not found")
            continue
            
        for filename in os.listdir(folder_path):
            if filename.endswith(".txt"):
                with open(os.path.join(folder_path, filename), "r") as f:
                    content = f.read()
                    content_chunks.append(f"search_document: {content}")
    
    if content_chunks:
        response = co.embed(
            texts=content_chunks,
            model="embed-english-v3.0",
            input_type="search_document"
        )
        embeddings = response.embeddings
        
        collection.add(
            ids=[str(i) for i in range(len(content_chunks))],
            documents=content_chunks,
            embeddings=embeddings
        )

def retrieve_context(question, collection, top_k=2):
    qr = co.embed(
        texts=[question],
        model="embed-english-v3.0",
        input_type="search_query"
    )
    emb = qr.embeddings[0]
    results = collection.query(query_embeddings=[emb], n_results=top_k)
    return "\n".join(results["documents"][0])

def get_prompt_plain(context: str, question: str) -> str:
    return f"""
<<START>>
You are a responsible person for answering Inha University (South Korea) information. Using the context below, answer within 300 tokens.
Create interactive, well-structured answers using bullet points, bold text, and proper formatting to make the information concise, answer-oriented, clear and easy to read.
Do not repeat the prompt text in your output.
And when context doesn't provide what user hasn't asked, don't mention it. Instead, just say in polite way you don't know it 
And in context text, there always will be link where this info is taken. at the end of your response, say that user can visit this link for official information and provide link when it is valid real question


And when user asks non-question things, for example saying just "Hello or Hi" or write any unpredicted letters or numbers or any non question phrases, sentences, don't provide link, again don't provide link. 
examples: 
User: Hello
You(Assistant): Hi, how can i help you? what do you wanna know about Inha SGCS?
or
User: 32e32x23e
You(Assistant): Sorry, if you write clear questions, I would help you find specific answers
Context:
"{context}"

Question: {question}

Answer:
<<END>>"""

def generate_agent_answer(context: str, question: str) -> str:
    prompt = get_prompt_plain(context, question)
    response = genai_client.models.generate_content(
        model="gemini-2.5-flash-lite",
        contents=prompt,
        config=types.GenerateContentConfig(
            temperature=0.01,
            top_p=0.8,
            stop_sequences=["<<END>>", "<<START>>"]
        )
    )

    return response.text.strip()

def rag_answer(question: str, collection) -> str:
    context = retrieve_context(question, collection, top_k=2)
    return generate_agent_answer(context, question)

from datasets import Dataset, load_dataset
from huggingface_hub import HfApi
from datetime import datetime
import pandas as pd
import uuid
import os


# gradio interface code below
def answer_question(question):
    """
    Main function that processes the question and returns the answer
    """
    if not question.strip():
        return "Please enter a question about Inha University."
    
    try:
        answer = rag_answer(question, collection)
        return answer
    except Exception as e:
        return f"Sorry, I encountered an error: {str(e)}"

# ─── 6. Gradio Frontend ─────────────────────────────────────────────────────

demo = gr.Interface(
    fn=answer_question,
    inputs=gr.Textbox(
        label="Ask me anything about Inha University SGCS…",
        placeholder="e.g. How many Major Required credits should I take for graduation? ",
        lines=2
    ),
    outputs=gr.Markdown(
        label="📌 Answer",
        show_copy_button=True
    ),
    title="📚 Inha University SGCS Info Assistant",
    description="Get answers to your questions about Inha University SGCS .",
    theme=gr.themes.Soft(),
    examples=[
        ["What classes should I normally take as 3nd semester ISE student?"],
        ["Tell me about student organizations and activities"],
        ["What percentage scholarship could I recieve with IELTS 7.0"]
    ]
)



if __name__ == "__main__":
    demo.launch(
        share=True,  
        server_name="0.0.0.0", 
        
    )