app.py

import gradio as gr
import os
os.environ["KERAS_BACKEND"] = "tensorflow"                                                                           
import keras
import keras_nlp


css = """
html, body {
    margin: 0;
    padding: 0;
    height: 100%;
    overflow: hidden;
}
body::before {
    content: '';
    position: fixed;
    top: 0;
    left: 0;
    width: 100vw;
    height: 100vh;
    background-image: url('https://github.com/ShebMichel/kagglex_imagebot/blob/main/geoBot_to_github.gif');
    background-size: cover;
    background-repeat: no-repeat;
    opacity: 0.65;             /* Faint background image */
    background-position: center;
    z-index: -1;    /* Keep the background behind text */
}
.gradio-container {
    display: flex;
    justify-content: center;
    align-items: center;
    height: 100vh;  /* Ensure the content is vertically centered */
}
"""


geomodel_llm = keras_nlp.models.CausalLM.from_preset("hf://ShebMichel/geobot_teacher-v0")

def launch(input):
    template = "Instruction:\n{instruction}\n\nResponse:\n{response}"
    prompt = template.format(
        instruction=input,                                                                   
        response="",
    )
    out = geomodel_llm.generate(prompt, max_length=1024)
    ind = out.index('Response') + len('Response')+2
    return out[ind:]

# Define the function to handle both text and file input
def analyze_response(text_input, file_input):
    # Process text and file inputs as required
    # For now, we'll just return a placeholder response
    response = f"Received text: {text_input}\n"
    if file_input is not None:
        response += f"File uploaded: {file_input.name}"
    else:
        response += "No file uploaded."
    return response






# Set up Gradio Interface
# iface = gr.Interface(
#     #fn=chatbot,
#     inputs=[
#         gr.File(label="Upload a file (PDF, JSON, DOCX)"),
#         gr.Textbox(label="Your Message"),
#         "state"  # Keeps chat history between turns
#     ],
#     outputs="chatbot",
#     live=True,
#     description="Drag and drop a file and start chatting with the bot based on its contents."
# )
# iface.launch()
#title="👋 Hola-Hello-Bonjour-Mbote-你好 👋 <br><br> I am geobot-teacher the student marker! <br><br> I am here to analyse each question to determine whether the response qualifies as a pass or fail. <br><br> Try me :)",
#description="Synthetic QA pairs (~1k) was finetuned on top of Gemma_2b_en.")
# inputs="text"

# iface = gr.Interface(launch,
#                      inputs="text",
#                      outputs="text",
#                      css=css,
#                      title="👋 Hi, I am geobot-teacher: The Student Marker 👋",
#                      description="Hola/Hello/Bonjour/Mbote/你好 \n\n"
#                                     "I am here to analyse each question to determine whether the response qualifies as a pass or fail.\n\n"
#                                     "Try me :)",
#                     )


iface = gr.Interface(fn=analyze_response,
                     inputs=[ 
                     gr.File(label="Upload a file (PDF, JSON, DOCX)"),gr.Textbox(label="Enter your response"), gr.Radio(
                    ["QCM","short_answer_questions","long_answer_questions"])],
                     outputs="text",
                     css=css,
                     title="👋 Hi, I am geobot-teacher: The Student Marker 👋",
                     description="Hola/Hello/Bonjour/Mbote/你好 \n\n"
                                    "I am here to analyse each question to determine whether the response qualifies as a pass or fail.\n\n"
                                    "Try me :)",
                    )
iface.launch(share=True)