import google.generativeai as genai
from PIL import Image
import gradio as gr
import numpy as np
import os

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")

# Now you can use hugging_face_api_key in your code

genai.configure(api_key=GOOGLE_API_KEY)

model = genai.GenerativeModel('gemini-pro-vision')
def process_image_and_text(image, text):
  # Assuming image is the input from Gradio
  if text:
    image_array = np.asarray(image.data)  # Convert memoryview to NumPy array
    image = Image.fromarray(image_array.astype('uint8'), 'RGB')  # Now you can use astype
    response = model.generate_content([text, image])
    return response.text
  else:
    image_array = np.asarray(image.data)  # Convert memoryview to NumPy array
    image = Image.fromarray(image_array.astype('uint8'), 'RGB')  # Now you can use astype
    response = model.generate_content(["Tell me about this image in bulletin format", image])
    return response.text


iface = gr.Interface(
    process_image_and_text,
    inputs=["image", "textbox"],  # Specify image and text inputs
    outputs="textbox",          # Specify text output
    title="Image and Text Processor",  # Set the app title
)

iface.launch(debug=True, share=True)  # Launch the Gradio app



# import google.generativeai as genai
# import os
# import os
# from pdf2image import convert_from_path
# from PIL import Image
# import pdf2image
# import numpy as np
# GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")

# # Now you can use hugging_face_api_key in your code

# genai.configure(api_key=GOOGLE_API_KEY)


# import gradio as gr
# # print(llm.predict("Who is the PM of India?"))
# model = genai.GenerativeModel('gemini-pro-vision')
# def process_image_and_text(images):
#     response = {}
#     for i,image in enumerate(images):
#         # # Assuming image is the input from Gradio
#         # image_array = np.asarray(image.data)  # Convert memoryview to NumPy array
#         # image = Image.fromarray(image_array.astype('uint8'), 'RGB')  # Now you can use astype
#         response = model.generate_content(["You are act as a tutor Solve all the question in the image in step by step: ", image])
#         response[i] = response.text
#     return response

# def input_pdf_setup(uploaded_pdf):
#     # Convert PDF pages to images
#     images = convert_from_path(uploaded_pdf, dpi=200)
#     return images



# def extract_answer(uploaded_pdf):
#     """Retrieves answers from processed images and presents them clearly."""
#     images = input_pdf_setup(uploaded_pdf)
#     responses = process_image_and_text(images=images)

#     # Present results in a user-friendly format
#     answers = []
#     for i, response in enumerate(responses.values()):
#         answers.append(f"Answer for question {i+1}:\n {response}")

#     return "\n".join(answers)

# # Create Gradio interface
# iface = gr.Interface(
#     fn=extract_answer,
#     inputs="file",
#     outputs="text",
#     title="Question-Answering with Gemstone.ai",
#     description="Upload a PDF containing questions, and get step-by-step answers!",
#     allow_flagging=True,
# )


# # Launch the Gradio application
# iface.launch(share=True, debug=True)