Nimzi/Environment

1import os2import pandas as pd3from langchain.vectorstores import FAISS4from langchain.embeddings import SentenceTransformerEmbeddings5from groq import Groq6import gradio as gr7 8# Load data from uploaded CSV9def load_data():10    file_path = "environmental_project_data.csv"  # Make sure the file is uploaded in the root directory11    if not os.path.exists(file_path):12        raise FileNotFoundError("The CSV file is missing. Please upload the file to the app directory.")13    return pd.read_csv(file_path)14 15# Index documents16def index_documents(df):17    if "Mitigation measures" not in df.columns:18        raise ValueError("The CSV file must contain a 'Mitigation measures' column.")19    documents = [{"content": row} for row in df["Mitigation measures"].dropna().tolist()]20    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")21    vector_store = FAISS.from_texts([doc["content"] for doc in documents], embeddings)22    return vector_store23 24# Setup Groq API25def setup_groq_api():26    os.environ["GROQ_API_KEY"] = "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"27    api_key = os.environ.get("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")28    if not api_key:29        raise ValueError("GROQ_API_KEY environment variable is not set.")30    client = Groq(api_key=api_key)31    return client32 33# Generate report34def generate_report(project_details, vector_store, client):35    try:36        retriever = vector_store.as_retriever()37        relevant_docs = retriever.get_relevant_documents(project_details)38 39        if not relevant_docs:40            return "No relevant documents found for the given project details."41 42        context = "\n".join([doc["content"] for doc in relevant_docs])43        prompt = f"""44        Using the following context, write an environmental input assessment report:45        Context:46        {context}47        Project Details:48        {project_details}49        """50 51        chat_completion = client.chat.completions.create(52            messages=[{"role": "user", "content": prompt}],53            model="llama3-8b-8192",54            stream=False,55        )56        return chat_completion.choices[0].message.content57    except Exception as e:58        return f"Error during report generation: {e}"59 60# Build Gradio interface61def build_interface(vector_store, client):62    def wrapper(project_details):63        return generate_report(project_details, vector_store, client)64 65    with gr.Blocks() as interface:66        gr.Markdown("# Environmental Assessment Report Generator")67        project_details = gr.Textbox(68            label="Enter Project Details",69            placeholder="Describe the project (e.g., type, location, environmental factors)."70        )71        generate_button = gr.Button("Generate Report")72        report_output = gr.Textbox(label="Generated Report", lines=10)73 74        generate_button.click(75            fn=wrapper,76            inputs=[project_details],77            outputs=[report_output]78        )79    return interface80 81# Main script82if __name__ == "__main__":83    try:84        # Load data85        print("Loading data...")86        df = load_data()87 88        # Index documents89        print("Indexing documents...")90        vector_store = index_documents(df)91 92        # Setup Groq API93        print("Setting up Groq API...")94        client = setup_groq_api()95 96        # Launch Gradio interface97        print("Launching the app...")98        interface = build_interface(vector_store, client)99        interface.launch(server_name="0.0.0.0", server_port=7860)100    except Exception as e:101        print(f"Error: {e}")102