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from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
MODEL_ID = "Fayza38/Question_and_Answer"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float32,
device_map="cpu"
)
def generate_questions(prompt):
messages = [
{"role": "system", "content": "You are a professional interview question generator."},
{"role": "user", "content": prompt}
]
formatted_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer(formatted_prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=800,
temperature=0.7
)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
return decoded
iface = gr.Interface(
fn=generate_questions,
inputs="text",
outputs="text",
title="Interview Question Generator"
)
iface.launch()
# # =========================================
# # ENUM MAPPINGS (Match Backend Enums)
# # =========================================
# SESSION_TYPES = {
# 1: "technical",
# 2: "softskills"
# }
# TRACKS = {
# 19: "generalprogramming"
# }
# # =========================================
# # LOAD MODEL ONCE (Global)
# # =========================================
# MODEL_PATH = "Fayza38/Question_and_Answer"
# tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
# model = AutoModelForCausalLM.from_pretrained(
# MODEL_PATH,
# torch_dtype=torch.float32,
# device_map="cpu"
# )
# app = FastAPI()
# # =========================================
# # REQUEST MODEL
# # =========================================
# class QuestionRequest(BaseModel):
# sessionType: int
# difficultyLevel: int | None = None
# trackName: int
# # =========================================
# # HELPER: GENERATE TEXT USING QWEN TEMPLATE
# # =========================================
# def generate_from_model(prompt: str):
# messages = [
# {"role": "system", "content": "You are a professional interview question generator."},
# {"role": "user", "content": prompt}
# ]
# formatted_prompt = tokenizer.apply_chat_template(
# messages,
# tokenize=False,
# add_generation_prompt=True
# )
# inputs = tokenizer(formatted_prompt, return_tensors="pt")
# with torch.no_grad():
# outputs = model.generate(
# **inputs,
# max_new_tokens=1200,
# temperature=0.7
# )
# decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
# return decoded
# # =========================================
# # PARSE Q/A FORMAT
# # =========================================
# def parse_qa_blocks(text: str):
# blocks = text.split("\n\n")
# results = []
# for block in blocks:
# if "Q:" in block and "A:" in block:
# parts = block.split("A:")
# question = parts[0].replace("Q:", "").strip()
# answer = parts[1].strip()
# results.append((question, answer))
# return results
# # =========================================
# # MAIN ENDPOINT
# # =========================================
# @app.post("/generate-questions")
# def generate_questions(request: QuestionRequest):
# if request.sessionType not in SESSION_TYPES:
# raise HTTPException(status_code=400, detail="Invalid session type")
# session_type = SESSION_TYPES[request.sessionType]
# # ---------------- SOFT SKILLS ----------------
# if session_type == "softskills":
# prompt = """
# Generate 10 behavioral interview questions.
# Format exactly as:
# Q: ...
# A: ...
# """
# # ---------------- TECHNICAL ----------------
# elif session_type == "technical":
# if request.trackName not in TRACKS:
# raise HTTPException(status_code=400, detail="Track not supported")
# difficulty = request.difficultyLevel or 1
# prompt = f"""
# Generate 10 General Programming interview questions.
# Difficulty level: {difficulty}
# Format exactly as:
# Q: ...
# A: ...
# """
# else:
# raise HTTPException(status_code=400, detail="Invalid session type")
# # -------- Generate once --------
# raw_output = generate_from_model(prompt)
# qa_pairs = parse_qa_blocks(raw_output)
# if len(qa_pairs) == 0:
# raise HTTPException(status_code=500, detail="Model failed to generate valid Q/A format")
# response = []
# for idx, (question, answer) in enumerate(qa_pairs[:10], 1):
# response.append({
# "questionText": question,
# "questionId": idx,
# "questionIdealAnswer": answer
# })
# return response |