File size: 3,051 Bytes
fa8df3d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import os
import gradio as gr
from groq import Groq
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
# Initialize Groq API Client
GROQ_API_KEY = "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"
client = Groq(api_key=GROQ_API_KEY)
# Load Pretrained Embedding Model
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
# Load Sample Environmental Dataset (Replace with your own)
environmental_data = [
{"text": "Deforestation leads to loss of biodiversity.", "category": "Biodiversity"},
{"text": "Construction projects can increase carbon emissions.", "category": "Air Quality"},
{"text": "Water usage must be monitored to prevent scarcity.", "category": "Water Resources"},
# Add more entries as needed
]
# Generate embeddings for the dataset
def create_dataset_index(data):
texts = [entry["text"] for entry in data]
embeddings = embedding_model.encode(texts)
faiss_index = faiss.IndexFlatL2(embeddings.shape[1])
faiss_index.add(np.array(embeddings))
return faiss_index, data
index, indexed_data = create_dataset_index(environmental_data)
# Function to retrieve relevant data
def retrieve_relevant_data(query, top_k=3):
query_embedding = embedding_model.encode([query])
distances, indices = index.search(np.array(query_embedding), top_k)
relevant_texts = [indexed_data[i]["text"] for i in indices[0]]
return relevant_texts
# Function to generate an EIA report
def generate_eia_report(project_type, location, size):
# Combine user input into a query
query = f"Project Type: {project_type}, Location: {location}, Size: {size}. Provide related environmental impact details."
# Retrieve relevant context
relevant_data = retrieve_relevant_data(query)
context = " ".join(relevant_data)
# Use Groq API to generate a detailed report
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": f"Generate an Environmental Impact Assessment report based on the following details:\n{query}\nContext:\n{context}"
}
],
model="llama3-8b-8192",
stream=False,
)
return chat_completion.choices[0].message.content
# Define Gradio Interface
def eia_interface(project_type, location, size):
try:
report = generate_eia_report(project_type, location, size)
return report
except Exception as e:
return f"An error occurred: {e}"
# Gradio App
interface = gr.Interface(
fn=eia_interface,
inputs=[
gr.Textbox(label="Project Type (e.g., Solar Farm, Highway)"),
gr.Textbox(label="Location (e.g., California, USA)"),
gr.Textbox(label="Project Size (e.g., 50 acres, 100 MW)"),
],
outputs="text",
title="Environmental Impact Assessment Generator",
description="Enter project details to generate a detailed Environmental Impact Assessment (EIA) report."
)
# Launch Gradio App
if __name__ == "__main__":
interface.launch()
|