from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import gradio as gr
model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

def interview_chatbot(user_input, task):
    """
    Handles interview-specific chatbot tasks.

    Parameters:
    - user_input: str, the input text from the user.
    - task: str, the type of task (e.g., "Behavioral Question", "Technical Question", "General Advice").

    Returns:
    - str: The generated response.
    """
    if task == "Behavioral Question":
        prompt = f"You are an interview coach. Provide a strong response to the following behavioral question:\n{user_input}\nSuggested Response:"
    elif task == "Technical Question":
        prompt = f"You are a technical interview expert. Answer the following technical question clearly and concisely:\nQuestion: {user_input}\nAnswer:"
    elif task == "General Advice":
        prompt = f"You are an interview expert. Provide advice for the following situation:\n{user_input}\nAdvice:"
    else:
        return "Invalid task selected."

    response = text_generator(
        prompt,
        max_length=200,
        num_return_sequences=1,
        pad_token_id=tokenizer.eos_token_id,
        temperature=0.7,
        top_p=0.9
    )[0]["generated_text"]

    return response[len(prompt):].strip()

def gradio_interface(user_input, task):
    """
    Interface function for Gradio integration.
    """
    if not user_input.strip():
        return "Please enter some input."
    return interview_chatbot(user_input, task)
with gr.Blocks(theme=gr.themes.Monochrome()) as interview_chat_ui:
    gr.Markdown(
        """
        # 🌟 Interview Preparation Chatbot
        Welcome to your personal interview preparation assistant! This chatbot can help you tackle:
        - **Behavioral Questions**: Practice with confidence.
        - **Technical Questions**: Get clear and concise explanations.
        - **General Advice**: Learn how to ace your interviews.
        """,
        elem_id="main_header",
    )

    with gr.Row():
        with gr.Column():
            gr.Markdown(
                """### 🎯 Enter your query and select the task type:""",
                elem_id="sub_header",
            )
            user_input = gr.Textbox(
                lines=5, 
                placeholder="Enter your question or situation here...",
                label="Your Input",
                elem_id="input_box",
            )
            task = gr.Radio(
                ["Behavioral Question", "Technical Question", "General Advice"],
                label="Select Task",
                elem_id="task_selector",
            )
            submit_button = gr.Button("✨ Get Response", elem_id="submit_button")

        with gr.Column():
            gr.Markdown(
                """### 💡 Chatbot Response:""",
                elem_id="response_header",
            )
            output = gr.Textbox(
                lines=10, 
                label="Response",
                interactive=False,
                elem_id="output_box",
            )

    submit_button.click(gradio_interface, inputs=[user_input, task], outputs=output)
    clear_button = gr.Button("🧹 Clear All", elem_id="clear_button")
    clear_button.click(lambda: ("", ""), None, [user_input, output])

    gr.Markdown(
        """
        ---
        **Tip**: Practice regularly to build confidence and improve your interview skills! 🚀
        """,
        elem_id="footer_text",
    )

interview_chat_ui.launch()