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import groq # This assumes Groq provides a Python package named "groq"
import pdfplumber
import gradio as gr
import os
from dotenv import load_dotenv
load_dotenv() # Loads environment variables from a .env file
# Replace with your Groq API key in your .env file as GROQ_API_KEY
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
groq.configure(api_key=GROQ_API_KEY)
# Load Groq's generative model (the model name 'groq-pro' is illustrative)
model = groq.GenerativeModel('groq-pro')
def extract_text_from_pdf(pdf_file):
text = ""
with pdfplumber.open(pdf_file) as pdf:
for page in pdf.pages:
page_text = page.extract_text()
if page_text:
text += page_text
return text
def summarize_pdf(pdf_file):
text = extract_text_from_pdf(pdf_file)
if not text.strip():
return "No extractable text found in the PDF."
# Optional: Limit the text if needed for token limits
text = text[:15000]
prompt = f"Summarize the following PDF content:\n\n{text}"
try:
response = model.generate_content(prompt) # Adjust parameters per Groq's API
return response.text.strip()
except Exception as e:
return f"Error during summarization: {e}"
# Gradio interface
iface = gr.Interface(
fn=summarize_pdf,
inputs=gr.File(label="Upload PDF", file_types=[".pdf"]),
outputs="text",
title="PDF Summarizer with Groq",
description="Upload a PDF and get a summary using Groq's generative AI API."
)
if __name__ == "__main__":
iface.launch()
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