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import gradio as gr
import os
from openai import OpenAI
import requests
from PIL import Image
import io
import tempfile
import base64
def analyze_environmental_impact(api_key, analysis_type, image=None, text_input=None, location=None, product_info=None):
"""
Analyze environmental impact based on user inputs using Gemini 2.5 Pro through OpenRouter.
"""
if not api_key:
return "Please provide an OpenRouter API key."
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=api_key,
)
# Prepare messages based on analysis type
if analysis_type == "Image Analysis":
if image is None:
return "Please upload an image for analysis."
# Save image to a temporary file
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
image_path = temp_file.name
image.save(image_path)
# Convert image to base64
with open(image_path, "rb") as img_file:
image_base64 = base64.b64encode(img_file.read()).decode("utf-8")
# Clean up temp file
os.unlink(image_path)
prompt = """
Analyze this image for environmental impact factors. Consider:
1. Visible ecosystems, wildlife, or natural resources
2. Human infrastructure and its potential environmental footprint
3. Evidence of pollution, waste, or environmental degradation
4. Sustainable or eco-friendly elements
Provide a comprehensive environmental impact assessment and suggest ways to improve
sustainability based on what you see.
"""
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{image_base64}"
}
}
]
}
]
elif analysis_type == "Geographical Assessment":
if not location:
return "Please provide a location for geographical assessment."
prompt = f"""
Provide an environmental impact assessment for the location: {location}.
Include information about:
1. Current environmental conditions (air quality, water resources, biodiversity)
2. Major environmental challenges and threats
3. Sustainability initiatives and progress
4. Carbon footprint and emissions data
5. Recommendations for improving environmental sustainability in this area
Present the information in a structured format with clear sections for each aspect.
"""
messages = [
{
"role": "user",
"content": prompt
}
]
elif analysis_type == "Product Assessment":
if not product_info:
return "Please provide product information for assessment."
prompt = f"""
Analyze the environmental impact of the following product:
{product_info}
Include in your assessment:
1. Materials and resources used
2. Manufacturing process impact
3. Transportation and distribution footprint
4. Usage phase environmental impact
5. End-of-life considerations
6. Overall sustainability score on a scale of 1-10
7. Recommendations for improving the product's environmental footprint
Be specific and provide actionable insights.
"""
messages = [
{
"role": "user",
"content": prompt
}
]
elif analysis_type == "Custom Query":
if not text_input:
return "Please provide a query for custom environmental analysis."
prompt = f"""
Provide an environmental impact analysis based on the following information:
{text_input}
Include in your response:
1. Key environmental concerns identified
2. Potential ecological impacts - short and long term
3. Carbon footprint considerations
4. Waste and pollution factors
5. Biodiversity impacts
6. Actionable recommendations for sustainability
7. References to relevant environmental principles or frameworks
Be specific, thorough, and provide practical advice.
"""
messages = [
{
"role": "user",
"content": prompt
}
]
# Make API call
try:
completion = client.chat.completions.create(
extra_headers={
"HTTP-Referer": "https://environmental-impact-analyzer.app",
"X-Title": "Smart Environmental Impact Analyzer",
},
model="google/gemini-2.5-pro-exp-03-25:free",
messages=messages
)
# Check if completion and choices exist before accessing
if completion and hasattr(completion, 'choices') and completion.choices and len(completion.choices) > 0:
if hasattr(completion.choices[0], 'message') and completion.choices[0].message:
return completion.choices[0].message.content
else:
return "Error: Received empty message from API."
else:
return "Error: Received incomplete response from API."
except Exception as e:
return f"Error during analysis: {str(e)}"
# Create Gradio interface
with gr.Blocks(title="Smart Environmental Impact Analyzer") as app:
gr.Markdown("# 🌍 Smart Environmental Impact Analyzer")
gr.Markdown("""
This tool analyzes environmental impacts using Gemini 2.5 Pro AI.
Choose an analysis type and provide the required information.
""")
api_key = gr.Textbox(label="OpenRouter API Key", placeholder="Enter your OpenRouter API key", type="password")
with gr.Tabs():
with gr.TabItem("Image Analysis"):
image_input = gr.Image(type="pil", label="Upload an image for environmental analysis")
image_submit = gr.Button("Analyze Image")
image_output = gr.Textbox(label="Analysis Results", lines=15)
image_submit.click(
analyze_environmental_impact,
inputs=[
api_key, # Directly pass the api_key component
gr.Textbox(value="Image Analysis", visible=False),
image_input,
gr.Textbox(value="", visible=False),
gr.Textbox(value="", visible=False),
gr.Textbox(value="", visible=False)
],
outputs=image_output
)
with gr.TabItem("Geographical Assessment"):
location_input = gr.Textbox(label="Location (city, region, or country)", placeholder="e.g., Paris, France")
location_submit = gr.Button("Analyze Location")
location_output = gr.Textbox(label="Analysis Results", lines=15)
location_submit.click(
analyze_environmental_impact,
inputs=[
api_key, # Directly pass the api_key component
gr.Textbox(value="Geographical Assessment", visible=False),
gr.Image(value=None, visible=False, type="pil"),
gr.Textbox(value="", visible=False),
location_input,
gr.Textbox(value="", visible=False)
],
outputs=location_output
)
with gr.TabItem("Product Assessment"):
product_info = gr.Textbox(
label="Product Information",
placeholder="Describe the product, materials, manufacturing process, lifecycle, etc.",
lines=5
)
product_submit = gr.Button("Analyze Product")
product_output = gr.Textbox(label="Analysis Results", lines=15)
product_submit.click(
analyze_environmental_impact,
inputs=[
api_key, # Directly pass the api_key component
gr.Textbox(value="Product Assessment", visible=False),
gr.Image(value=None, visible=False, type="pil"),
gr.Textbox(value="", visible=False),
gr.Textbox(value="", visible=False),
product_info
],
outputs=product_output
)
with gr.TabItem("Custom Query"):
custom_input = gr.Textbox(
label="Custom Environmental Query",
placeholder="Enter your environmental question or describe a scenario to analyze",
lines=5
)
custom_submit = gr.Button("Analyze")
custom_output = gr.Textbox(label="Analysis Results", lines=15)
custom_submit.click(
analyze_environmental_impact,
inputs=[
api_key, # Directly pass the api_key component
gr.Textbox(value="Custom Query", visible=False),
gr.Image(value=None, visible=False, type="pil"),
custom_input,
gr.Textbox(value="", visible=False),
gr.Textbox(value="", visible=False)
],
outputs=custom_output
)
gr.Markdown("""
### Privacy Notice
Your API key is used only for making requests to OpenRouter and is not stored or logged.
The images and text you submit are processed by Gemini 2.5 Pro through OpenRouter's API.
### Usage Instructions
1. Enter your OpenRouter API key (get one from https://openrouter.ai)
2. Select the type of analysis you want to perform
3. Provide the required information (image, location, product details, or custom query)
4. Click the "Analyze" button for your selected tab
""")
# Launch the app
if __name__ == "__main__":
app.launch() |