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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()
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