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import os | |
import gradio as gr | |
import google.generativeai as genai | |
# Configure Gemini API | |
GEMINI_API_KEY = "AIzaSyDooG1UD_7ae5lgl7HwfsVyGlwn2XycXko" | |
genai.configure(api_key=GEMINI_API_KEY) | |
# Create the model with the same configuration as the sample | |
generation_config = { | |
"temperature": 1, | |
"top_p": 0.95, | |
"top_k": 40, | |
"max_output_tokens": 8192, | |
"response_mime_type": "text/plain", | |
} | |
model = genai.GenerativeModel( | |
model_name="gemini-1.5-pro", | |
generation_config=generation_config, | |
system_instruction="You are an expert in detecting objects from xray image. Your job is to detect the objects from x-ray images.", | |
) | |
def analyze_image(image): | |
"""Analyze the uploaded image using Gemini model""" | |
try: | |
if image is None: | |
return "Please upload an image to analyze." | |
# Start a new chat session | |
chat = model.start_chat() | |
# Upload and analyze the image | |
image_file = genai.upload_file(image) | |
# Send the image with a prompt | |
response = chat.send_message([ | |
image_file, | |
"Here is an xray image, describe all objects you can see in this image and their relative positions to each other." | |
]) | |
return response.text | |
except Exception as e: | |
return f"Error analyzing image: {str(e)}\nPlease make sure you've uploaded a valid image file." | |
# Get example images from the examples directory | |
examples_dir = os.path.join(os.path.dirname(__file__), "examples") | |
example_images = [] | |
if os.path.exists(examples_dir): | |
example_images = [ | |
os.path.join(examples_dir, f) | |
for f in os.listdir(examples_dir) | |
if f.lower().endswith(('.png', '.jpg', '.jpeg')) | |
] | |
# Create Gradio interface | |
iface = gr.Interface( | |
fn=analyze_image, | |
inputs=gr.Image(type="filepath", label="Upload X-ray Image"), | |
outputs=gr.Textbox(label="Analysis Result", lines=10), | |
title="X-ray Image Object Detection", | |
description="Upload an X-ray image and get a detailed description of the objects detected in it.", | |
examples=example_images, | |
cache_examples=True | |
) | |
# Launch the app | |
if __name__ == "__main__": | |
iface.launch() |