Create app.py

app.py

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+# Install Required Libraries (if not already installed)
+# !pip install groq sentence-transformers faiss-cpu gradio pandas numpy langchain langchain-community langchain-groq python-dotenv
+
+# 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
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain.document_loaders import DataFrameLoader
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_groq import ChatGroq  # Updated Import
+
+# Step 1: Set up API Key for Groq
+os.environ["GROQ_API_KEY"] = "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"
+
+# Initialize Groq Client using LangChain Wrapper
+llm = ChatGroq(
+    groq_api_key=os.environ.get("GROQ_API_KEY"),
+    model="llama3-8b-8192"
+)
+
+# Step 2: Load Dataset
+df = pd.read_csv('environmental_impact_assessment.csv')
+
+# Step 3: Prepare Text Data for RAG
+# Create a 'text' column combining relevant columns
+df['text'] = (
+    "Project Type: " + df['Project Type'].astype(str) + "; " +
+    "Land Use: " + df['Land Use (sq km)'].astype(str) + "; " +
+    "Emissions: " + df['Emissions (tons/year)'].astype(str) + "; " +
+    "Water Requirement: " + df['Water Requirement (liters/day)'].astype(str) + "; " +
+    "Mitigation Measures: " + df['Mitigation Measures'].astype(str) + "; " +
+    "Legal Compliance: " + df['Legal Compliance'].astype(str)
+)
+
+# Step 4: Create Vector Store for Retrieval
+loader = DataFrameLoader(df, page_content_column="text")
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")  # Explicit model specified
+vectorstore = FAISS.from_documents(loader.load(), embeddings)
+
+# Step 5: Build RAG QA Chain
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=vectorstore.as_retriever()
+)
+
+# Step 6: Define Gradio Interface
+def generate_report(project_type, land_use, emissions, water_requirement):
+    """
+    Generate Environmental Impact Assessment Report using Groq API and RAG.
+    """
+    query = (
+        f"Generate an environmental impact assessment report for a project with the following details:\n"
+        f"Project Type: {project_type}, Land Use: {land_use} sq km, Emissions: {emissions} tons/year, "
+        f"Water Requirement: {water_requirement} liters/day."
+    )
+    try:
+        response = qa_chain.run(query)
+        return response
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+# Step 7: Build Gradio Interface
+iface = gr.Interface(
+    fn=generate_report,
+    inputs=[
+        gr.Textbox(label="Project Type"),
+        gr.Number(label="Land Use (sq km)"),
+        gr.Number(label="Emissions (tons/year)"),
+        gr.Number(label="Water Requirement (liters/day)")
+    ],
+    outputs=gr.Textbox(label="Generated Report"),
+    title="Environmental Impact Assessment Report Generator",
+    description="Enter project details to generate an environmental impact assessment report using RAG and Groq's API."
+)
+
+# Step 8: Launch the Gradio App
+iface.launch()