app.py

import gradio as gr
from huggingface_hub import InferenceClient
from PyPDF2 import PdfReader

# Models Setup
models = {
    "Job Consultant (Zephyr)": {
        "client": InferenceClient(model="HuggingFaceH4/zephyr-7b-beta"),
    },
    "PDF Summarizer (T5)": {
        "client": InferenceClient(model="aaliyaan/t5-small-finetuned-career"),
    },
    "Broken Answer (T0pp)": {
        "client": InferenceClient(model="bigscience/T0p"),
    },
}

# Chat Function with Context
def chat_with_model(model_choice, user_message, chat_history, file=None):
    if model_choice == "Resume Summarizer (T5)" and file is not None:
        pdf_text = extract_text_from_pdf(file)
        user_message += f"\n\nPDF Content:\n{pdf_text}"

    if not user_message.strip():
        return chat_history, ""

    model_info = models[model_choice]
    client = model_info["client"]

    # Prepare messages for the InferenceClient including chat history
    messages = [{"role": "system", "content": "You are a helpful assistant."}]
    
    # Add previous conversation to the messages
    for user_msg, bot_msg in chat_history:
        messages.append({"role": "user", "content": user_msg})
        messages.append({"role": "assistant", "content": bot_msg})
    
    # Add the current user message
    messages.append({"role": "user", "content": user_message})

    # Generate Response
    response = ""
    for message in client.chat_completion(
        messages,
        max_tokens=150,
        stream=True,
        temperature=0.7,
        top_p=0.95
    ):
        token = message.choices[0].delta.content
        response += token

    # Update Chat History
    chat_history.append((user_message, response))
    return chat_history, ""

# Function to Extract Text from PDF
def extract_text_from_pdf(file):
    reader = PdfReader(file.name)
    text = "\n".join(page.extract_text() for page in reader.pages if page.extract_text())
    return text

# Interface Setup
def create_chat_interface():
    with gr.Blocks(css=""" 
        .chatbox {
            background-color: #f7f7f8;
            border-radius: 12px;
            padding: 16px;
            font-family: 'Segoe UI', Tahoma, sans-serif;
        }
        .chat-title {
            font-size: 24px;
            font-weight: bold;
            text-align: center;
            margin-bottom: 12px;
            color: #3a9fd6;
        }
    """) as interface:
        gr.Markdown("<div class='chat-title'>Job Consultant AI</div>")

        with gr.Row():
            model_choice = gr.Dropdown(
                choices=list(models.keys()),
                value="Job Consultant (Zephyr)",
                label="Select Model"
            )

        chat_history = gr.Chatbot(label="Chat History", elem_classes="chatbox")

        user_message = gr.Textbox(
            placeholder="Type your message here and press Enter...",
            show_label=False,
            elem_classes="chatbox",
        )

        file_input = gr.File(label="Upload PDF", visible=False, file_types=[".pdf"])

        def toggle_pdf_input(selected_model):
            return gr.update(visible=(selected_model == "Resume Summarizer (T5)"))

        model_choice.change(fn=toggle_pdf_input, inputs=model_choice, outputs=file_input)

        # Link the input box to send messages on Enter
        user_message.submit(
            chat_with_model,
            inputs=[model_choice, user_message, chat_history, file_input],
            outputs=[chat_history, user_message],
        )

    return interface

if __name__ == "__main__":
    interface = create_chat_interface()
    interface.launch(server_name="0.0.0.0", server_port=7860)