app.py

from fastapi import FastAPI, HTTPException
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