app.py

import gradio as gr
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import cv2
import tempfile
import os

# Load the tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Function to generate responses from the model
def generate_response(question):
    inputs = tokenizer.encode(question, return_tensors='pt')
    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Function to process video and extract text question
def process_video(video_path):
    # Placeholder for video processing and speech-to-text conversion
    # Currently returning a dummy question for simplicity
    question = "What are your strengths and weaknesses?"
    return question

# Main function to handle video input and generate text response
def video_interview(video):
    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_file:
        temp_file.write(video.read())
        temp_video_path = temp_file.name
    
    question = process_video(temp_video_path)
    response = generate_response(question)
    
    os.remove(temp_video_path)
    return response

# Gradio interface
iface = gr.Interface(
    fn=video_interview, 
    inputs=gr.Video(), 
    outputs="text", 
    title="Mock Interviewee Video Bot",
    description="Upload a video question or use your webcam to ask a question and get a simulated interview response."
)

if __name__ == "__main__":
    iface.launch()