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# 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
from dotenv import load_dotenv
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
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
from youtube_transcript_api import YouTubeTranscriptApi
load_dotenv()
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)
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