app.py

import gradio as gr
from huggingface_hub import InferenceClient
import random
import whisper
from pydub import AudioSegment
import torch
import torchvision.transforms as transforms
import torchvision.models as models
from PIL import Image

# Load mode
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
whisper_model = whisper.load_model("base")

def transcribe_audio(file_path):
    if file_path is None:  
        return "❌ No audio file provided."

    try:
        audio = AudioSegment.from_file(file_path)
        converted_path = "converted.wav"
        audio.export(converted_path, format="wav")
        result = whisper_model.transcribe(converted_path, fp16=False)
        return result["text"]
    except Exception as e:
        return f"❌ ERROR: {str(e)}"

# Step 1: Set interview type
def set_type(choice, user_profile):
    user_profile["interview_type"] = choice
    return "Great! What’s your background and what field/role are you aiming for?", user_profile

# Step 2: Save background
def save_background(info, user_profile):
    user_profile["field"] = info
    return "Awesome! Type 'start' below to begin your interview.", user_profile

# Generate interview question
def generate_question(user_profile):
    system_prompt = f"You are a professional interviewer conducting a {user_profile['interview_type']} interview for a candidate in {user_profile['field']}. Generate one thoughtful, clear, and concise interview question."
    messages = [{"role": "system", "content": system_prompt}]
    response = client.chat_completion(messages, max_tokens=100, stream=False)
    return response.choices[0].message.content.strip()

# Generate feedback using LLM
def generate_feedback_llm(user_profile):
    feedback = []
    for i, (question, answer) in enumerate(zip(user_profile.get("questions", []), user_profile.get("user_answers", []))):
        messages = [
            {"role": "system", "content": f"You are a professional interviewer providing feedback for a candidate's response in a {user_profile['interview_type']} interview for a {user_profile['field']} role. DO NOT include any candidate responses or dialogue. DO NOT include any 'Interviewer:', 'Candidate:' prefixes"},
            {"role": "user", "content": f"Question: {question}\nAnswer: {answer}\nPlease give specific, constructive feedback."}
        ]
        response = client.chat_completion(messages, max_tokens=150, stream=False)
        feedback.append(f"Question {i+1}: {response.choices[0].message.content.strip()}")
    return "\n\n".join(feedback)

# Chat lo
def respond(message, chat_history, user_profile):
    message_lower = message.strip().lower()

    if not user_profile.get("interview_type") or not user_profile.get("field"):
        bot_msg = "Please finish steps 1 and 2 before starting the interview."
        chat_history.append({"role": "user", "content": message})
        chat_history.append({"role": "assistant", "content": bot_msg})
        return chat_history

    if message_lower == 'start':
        user_profile['questions'] = []
        user_profile['user_answers'] = []
        user_profile['current_q'] = 0
        user_profile['interview_in_progress'] = True

        intro = f"Welcome to your {user_profile['interview_type']} interview for a {user_profile['field']} position. I will ask you up to 10 questions. Type 'stop' anytime to end."
        first_q = generate_question(user_profile)
        user_profile['questions'].append(first_q)
        chat_history.append({"role": "user", "content": message})
        chat_history.append({"role": "assistant", "content": intro})
        chat_history.append({"role": "assistant", "content": f"First question: {first_q}"})
        return chat_history

    if message_lower == 'stop' and user_profile.get("interview_in_progress"):
        user_profile['interview_in_progress'] = False
        bot_msg = "Interview stopped. Type 'feedback' if you'd like me to analyze your answers. Thanks for interviewing with Intervu!"
        chat_history.append({"role": "user", "content": message})
        chat_history.append({"role": "assistant", "content": bot_msg})
        return chat_history

    if message_lower == 'feedback':
        feedback = generate_feedback_llm(user_profile)
        chat_history.append({"role": "user", "content": message})
        chat_history.append({"role": "assistant", "content": feedback})
        return chat_history

    if user_profile.get("interview_in_progress"):
        user_profile['user_answers'].append(message)
        user_profile['current_q'] += 1

        if user_profile['current_q'] < 10:
            next_q = generate_question(user_profile)
            user_profile['questions'].append(next_q)
            bot_msg = f"Next question: {next_q}"
        else:
            user_profile['interview_in_progress'] = False
            bot_msg = "Interview complete! Type 'feedback' if you'd like me to analyze your answers. Thanks for interviewing with Intervu!"

        chat_history.append({"role": "user", "content": message})
        chat_history.append({"role": "assistant", "content": bot_msg})
        return chat_history

    # fallback LLM response
    messages = [
        {"role": "system", "content": f"You are a professional interviewer conducting a {user_profile['interview_type']} interview for a candidate in {user_profile['field']}."},
        {"role": "user", "content": message}
    ]
    response = client.chat_completion(messages, max_tokens=150, stream=False)
    bot_msg = response.choices[0].message.content.strip()
    chat_history.append({"role": "user", "content": message})
    chat_history.append({"role": "assistant", "content": bot_msg})
    return chat_history

# Handle audio input
def handle_audio(audio_file, chat_history, user_profile):
    transcribed = transcribe_audio(audio_file)
    if transcribed.startswith("❌"):
        chat_history.append({"role": "assistant", "content": transcribed})
        return chat_history
    return respond(transcribed, chat_history, user_profile)
# Load ResNet18 model
model = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1)
model.fc = torch.nn.Linear(model.fc.in_features, 2)  # Adjust for two classes
model.eval()

# Define image transformation
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

# Function to classify posture images
def classify_image(image):
    if image is None:
        return "No image provided!"
    image = transform(image).unsqueeze(0)
    output = model(image)
    _, predicted = torch.max(output, 1)
    return "You have good posture! Keep it up!" if predicted.item() == 0 else "I suggest sitting straighter or getting more into frame. It will help for your future interviews."
# Full UI
with gr.Blocks(css="""

    body { background-color: #161b24; font-family: 'Nato', sans-serif !important; }
    h1 { text-align: center; color: #2c3e50; }
    img { display: block; margin: auto; width: 100px; border-radius: 20px; }
    button { 
    font-size: 16px; 
    padding: 10px 20px; 
    border-radius: 10px; 
    border: 2px solid rgba(124, 248, 255, 0.4); 
    background-color: rgba(124, 248, 255, 0.4); 
    color: #fafdff; 
    transition: all 0.2s ease;
}

    button:hover {
    background-color: #49888f;
    border-color: #7cf8ff;
    transform: scale(1.05);
}
    .gr-chatbot { background-color: white; border-radius: 15px; padding: 20px; }
    .tab-container {
    height: auto !important;
    min-height: 0 !important;
    flex: unset !important;
}

.tab-container[style] {
    height: auto !important;
    min-height: 0 !important;
    flex: unset !important;
    padding-bottom: 25px;
    margin-top: 40px;
}

.custom-tabs .tab-nav {
    display: flex;
    align-items: center;
    width: 100%;
    position: relative;
    overflow: hidden;
    background-color: white;
    padding: 8px;
    border-radius: 30px;
    height: auto !important;
    min-height: 0 !important
    padding-bottom: 25px !important;
    margin-top: 40px !important;
    gap: 16px;
}

.custom-tabs button[role="tab"] {
    border-radius: 50px !important;
    padding: 10 !important;
    font-weight: 500;
    background-color: transparent;
    border:none;
    justify-content: center;
    width: 40%;
    color: #fafdff !important;


}

.custom-tabs button[role="tab"][aria-selected="true"] {
    border: 2px solid #7cf8ff !important;
    color: #7cf8ff !important;
    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
    transform: none !important;
}

.custom-tabs button[role="tab"]:hover {
    background-color: #2e323a;
    transform: scale(1.05);
    transform: none !important;
}
/* REMOVE THE UGLY ORANGE UNDERLINE LINE */
.custom-tabs button[role="tab"] {
    border-bottom: none !important;
    box-shadow: none !important;
}

.custom-tabs button[role="tab"]::after {
    display: none !important;
    border-bottom: none !important;
}

    /*:root {
    --trim-region-color: rgba(255, 153, 0, 0.5);*/  


    .custom-tabs button[role="tab"]:nth-child(1)::before {
        content: "";
        background: url("https://cdn-uploads.huggingface.co/production/uploads/6841b10b397a67a7c7a39b89/6gGwgTv14woJpgh1G59Ad.png") no-repeat center center;
        background-size: contain;
        width: 20px;
        height: 20px;
        display: inline-block;
        margin-right: 8px;
        vertical-align: middle;
    }



    .custom-tabs button[role="tab"]:nth-child(2)::before {
        content: "";
        background: url("https://cdn-uploads.huggingface.co/production/uploads/6841b10b397a67a7c7a39b89/F0fJhrP6CtFFqhQWKb9dF.png") no-repeat center center;
        background-size: contain;
        width: 20px;
        height: 20px;
        display: inline-block;
        margin-right: 8px;
        vertical-align: middle;
    }

    .custom-tabs button[role="tab"]:nth-child(3)::before {
        content: "";
        background: url("https://cdn-uploads.huggingface.co/production/uploads/6841b10b397a67a7c7a39b89/tfjs2xtx5g61oqfcchHpd.png") no-repeat center center;
        background-size: contain;
        width: 20px;
        height: 20px;
        display: inline-block;
        margin-right: 8px;
        vertical-align: middle;
    }
    .bruh button{
font-size: 0px !important;
padding: 0px !important;
border-radius: 0px !important;
border: none !important;
background-color: #27272a !important;
color: #fafdff !important;
transition: none !important;

}
    




""") as demo:

    gr.Markdown("""
<div style='width: 100%; text-align: center;'>
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6841b10b397a67a7c7a39b89/iudiYIVkr1ZcAqpEWaW3Z.png" style="width: 100%; height: auto; object-fit: contain;">
</div>
""")
#https://cdn-uploads.huggingface.co/production/uploads/6841b10b397a67a7c7a39b89/MTR_dMHte-RCIbyujLW83.png
#     gr.Markdown("""
# <div style='width: 90%; text-align: center;'>
#   <img src="https://cdn-uploads.huggingface.co/production/uploads/6841b10b397a67a7c7a39b89/H6VqF7bDhPQ9k8lQo7fqO.png" style="width: 100%; height: auto; object-fit: contain;">
# </div>
# """)
    



    user_profile = gr.State({"interview_type": "", "field": "", "interview_in_progress": False})
    chat_history = gr.State([])


#     gr.Markdown("""<div style='
#     font-family: 'Lato', sans-serif;
#     text-align: center;
#     padding: 30px;
#     box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
#     margin-bottom: 20px;
# '>
#     <h1 style='font-size: 89.5px; color: #fafdff;'>Welcome to <b><span style='color: #7cf8ff;'>Intervu</span></b></h1>
#     <p style='font-size: 22.1px; color: #fafdff; margin-top: 30px; text-align: center; margin-bottom: 30px;'>Before you begin, complete Step 1 to select your interview type and Step 2 to enter your background. Practice is available through text, speech, or webcam.</p>
# </div>
# """)

    # Step 1: Interview Type
    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown("""
                <div style="
                    background: rgba(255, 255, 255, 0.1); 
                    padding: 20px; 
                    border-radius: 15px; 
                    backdrop-filter: blur(5px); 
                    box-shadow: 0 4px 10px rgba(0,0,0,0.2);
                ">
                    <h3>Step 1: Choose Interview Type</h3>
                    <p>Select the type of interview you want to practice.</p>
                </div>
            """)
            btn1 = gr.Button("Technical")
            btn2 = gr.Button("Competency-Based Interview")
            btn3 = gr.Button("Case")

        with gr.Column(scale=2):
            type_output = gr.Textbox(label="Bot Response", interactive=False)

    btn1.click(set_type, inputs=[gr.Textbox(value="Technical", visible=False), user_profile], outputs=[type_output, user_profile])
    btn2.click(set_type, inputs=[gr.Textbox(value="Competency-Based Interview", visible=False), user_profile], outputs=[type_output, user_profile])
    btn3.click(set_type, inputs=[gr.Textbox(value="Case", visible=False), user_profile], outputs=[type_output, user_profile])

    # Step 2: Background
    gr.Markdown("### Step 2: Enter Your Background")
    background = gr.Textbox(label="Your background and field/goal")
    background_btn = gr.Button("Submit")
    background_output = gr.Textbox(label="Bot response", interactive=False)
    background_btn.click(save_background, inputs=[background, user_profile], outputs=[background_output, user_profile])

    # Step 3: Chat Mode Tabs
    gr.Markdown("### Step 3: Choose Chat Mode")
    with gr.Row():
        with gr.Column(elem_classes=["custom-tabs"]):
            with gr.Tabs():

                with gr.Tab('Text Mode'):
                    chatbot_text = gr.Chatbot(label="Interview Chat (Text Mode)", type="messages")
                    msg = gr.Textbox(label="Type 'start' to begin")
                    send_btn = gr.Button("Send")
                    send_btn.click(respond, inputs=[msg, chat_history, user_profile], outputs=[chatbot_text], queue=False)
                    send_btn.click(lambda: "", None, msg, queue=False)

                with gr.Tab("Audio Mode"):
                    chatbot_audio = gr.Chatbot(label="Interview Chat (Audio Mode)", type="messages")
                    audio_input = gr.Audio(type="filepath", label="Record Your Answer", elem_classes=["bruh"])
                    audio_btn = gr.Button("Send Audio")
                    audio_btn.click(handle_audio, inputs=[audio_input, chat_history, user_profile], outputs=[chatbot_audio], queue=False, )

                with gr.Tab("Webcam Mode"):
                    img_upload = gr.Image(type="pil", label="Upload an Image", elem_classes=["bruh"])
                    posture_output = gr.Textbox(label="Posture Feedback", elem_classes=["bruh"])
                    posture_btn = gr.Button("Analyze Posture")
                    posture_btn.click(classify_image, inputs=[img_upload], outputs=[posture_output])


demo.launch()