Create app.py

app.py

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+# Import necessary libraries
+import os
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import gradio as gr
+from groq import Groq
+
+# Set up Groq API
+GROQ_API = "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"  # Replace with your Groq API key
+client = Groq(api_key=GROQ_API)
+
+# # Load environmental dataset (upload to Colab)
+# from google.colab import files
+# uploaded = files.upload()  # Upload 'environmental_impact_assessment_dataset.csv'
+
+# Load the dataset
+df = pd.read_csv('/content/environmental_impact_assessment_dataset.csv')  # Replace with the uploaded file name
+
+# Combine relevant text columns for embeddings
+text_column = df['Project Type'] + ' ' + df['Mitigation Measures']  # Adjust based on your dataset columns
+
+# Use SentenceTransformers to generate text embeddings
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')  # Lightweight embedding model
+embeddings = embedding_model.encode(text_column.tolist())
+
+# Convert embeddings to numpy array
+embeddings_np = np.array(embeddings).astype(np.float32)
+
+# Build FAISS index for document retrieval
+index = faiss.IndexFlatL2(embeddings_np.shape[1])  # L2 distance for similarity search
+index.add(embeddings_np)  # Add the document embeddings to the FAISS index
+
+# Function to retrieve the most relevant document from the dataset
+def retrieve_relevant_document(query):
+    # Generate query embedding
+    query_embedding = embedding_model.encode([query])
+    query_embedding_np = np.array(query_embedding).astype(np.float32)
+
+    # Perform similarity search in FAISS
+    _, indices = index.search(query_embedding_np, k=1)  # Top 1 match
+    retrieved_text = text_column.iloc[indices[0][0]]  # Retrieve corresponding text
+
+    return retrieved_text
+
+# Function to generate an EIA report using Groq's API
+def generate_report(user_input):
+    # Check if input is empty
+    if not user_input.strip():
+        return "Please provide project details to generate the Environmental Impact Assessment report."
+
+    # Retrieve relevant information using FAISS
+    relevant_document = retrieve_relevant_document(user_input)
+
+    # Use Groq API to generate a report based on the retrieved document
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {"role": "user", 
+             "content": f"Generate an environmental impact assessment report based on the following details:\n\n{relevant_document}\n\nUser Query: {user_input}"}
+        ],
+        model="llama3-8b-8192",  # Groq model
+    )
+
+    # Return the Groq-generated content
+    return chat_completion.choices[0].message.content
+
+# Gradio interface for user interaction
+def gradio_interface(project_details):
+    return generate_report(project_details)
+
+# Launch Gradio app
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs="text",  # Input: text box for project details
+    outputs="text",  # Output: text box for the generated report
+    live=False  # Set to False for non-live mode
+)
+
+iface.launch()