app.py

import gradio as gr
from transformers import pipeline
from PIL import Image
import pytesseract

# Initialize chat model
chat_model = pipeline("conversational", model="microsoft/DialoGPT-medium")

# Chat function
def chat_fn(history, user_input):
    conversation = {"user": user_input, "bot": None}
    if history:
        conversation["history"] = history
    response = chat_model(conversation["user"])
    conversation["bot"] = response[0]["generated_text"]
    history.append((user_input, conversation["bot"]))
    return history, ""

# OCR function
def ocr(image):
    text = pytesseract.image_to_string(Image.open(image))
    return text

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("### استخلاص النصوص من الصور والدردشة")
    
    # Image OCR section
    with gr.Tab("استخلاص النصوص من الصور"):
        with gr.Row():
            image_input = gr.Image(label="اختر صورة")
            ocr_output = gr.Textbox(label="النص المستخلص")
        submit_button = gr.Button("Submit")
        submit_button.click(ocr, inputs=[image_input], outputs=[ocr_output])
    
    # Chat section
    with gr.Tab("محادثة"):
        chatbot = gr.Chatbot()
        message = gr.Textbox(label="رسالتك هنا")
        state = gr.State([])
        send_button = gr.Button("إرسال")
        send_button.click(chat_fn, inputs=[state, message], outputs=[chatbot, state])

demo.launch()