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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 | |
| # 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) | |