Create app.py

app.py

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+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()