app.py

import gradio as gr
from transformers import pipeline
import PyPDF2

# ---------------- Load Hugging Face Pipelines ----------------
# Lightweight summarizer
summarizer = pipeline("summarization", model="sshleifer/distilbart-xsum-12-6")
# Instruction-tuned generator for eco tips
generator = pipeline("text2text-generation", model="google/flan-t5-small")

# ---------------- Core Functions ----------------
def extract_text_from_pdf(pdf_file):
    """Extract text from uploaded PDF file."""
    if pdf_file is None:
        return ""
    try:
        pdf_reader = PyPDF2.PdfReader(pdf_file)
        text = ""
        for page in pdf_reader.pages:
            if page.extract_text():
                text += page.extract_text() + "\n"
        return text
    except Exception as e:
        return f"Error reading PDF: {str(e)}"

def eco_tips_generator(problem_keywords):
    """Generate eco-friendly tips based on keywords."""
    if not problem_keywords.strip():
        return "Please enter some keywords (e.g., plastic, solar, energy saving)."
    prompt = (
        f"Give practical eco-friendly tips for sustainable living related to: {problem_keywords}. "
        f"Provide specific, clear, and actionable solutions."
    )
    result = generator(prompt, max_length=150)
    return result[0]["generated_text"]

def policy_summarization(pdf_file, policy_text):
    """Summarize either uploaded PDF or pasted text."""
    if pdf_file is not None:
        content = extract_text_from_pdf(pdf_file)
        text_to_summarize = content if content.strip() else policy_text
    else:
        text_to_summarize = policy_text

    if not text_to_summarize.strip():
        return "Please upload a PDF or paste policy text."

    try:
        summary = summarizer(
            text_to_summarize,
            max_length=120,
            min_length=30,
            do_sample=False
        )
        return summary[0]['summary_text']
    except Exception as e:
        return f"Error during summarization: {str(e)}"

# ---------------- Gradio Interface ----------------
with gr.Blocks() as app:
    gr.Markdown("# 🌍 Eco Assistant & Policy Analyzer")

    with gr.Tabs():
        # Eco Tips Generator
        with gr.TabItem("♻️ Eco Tips Generator"):
            with gr.Row():
                with gr.Column():
                    keywords_input = gr.Textbox(
                        label="Environmental Problem/Keywords",
                        placeholder="e.g., plastic, solar, water waste, energy saving...",
                        lines=3
                    )
                    generate_tips_btn = gr.Button("Generate Eco Tips")

                with gr.Column():
                    tips_output = gr.Textbox(label="Sustainable Living Tips", lines=15)

            generate_tips_btn.click(eco_tips_generator, inputs=keywords_input, outputs=tips_output)

        # Policy Summarization
        with gr.TabItem("📑 Policy Summarization"):
            with gr.Row():
                with gr.Column():
                    pdf_upload = gr.File(label="Upload Policy PDF", file_types=[".pdf"])
                    policy_text_input = gr.Textbox(
                        label="Or paste policy text here",
                        placeholder="Paste policy document text...",
                        lines=5
                    )
                    summarize_btn = gr.Button("Summarize Policy")

                with gr.Column():
                    summary_output = gr.Textbox(label="Policy Summary & Key Points", lines=20)

            summarize_btn.click(policy_summarization, inputs=[pdf_upload, policy_text_input], outputs=summary_output)

# ---------------- Run App ----------------
if __name__ == "__main__":
    app.launch(server_name="0.0.0.0", server_port=7860)