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# ✅ Install dependencies
# 📚 Imports
import fitz # PyMuPDF
import requests
import gradio as gr
import tempfile
import os
import io
# 🔑 Enter your OpenRouter API key here
OPENROUTER_API_KEY = "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"
# Global variable to store the extracted text
pdf_text = ""
# 📄 Extract text from PDF
def extract_text_from_pdf(file_obj):
global pdf_text
if file_obj is None:
return "Please upload a PDF file first."
try:
# Get the file path from the file object
# In Gradio, the file object has a name attribute that contains the path
file_path = file_obj.name
# Now open the file with PyMuPDF
doc = fitz.open(file_path)
text = ""
for page in doc:
text += page.get_text()
doc.close()
# Store the text for later use
pdf_text = text
# Return preview of the extracted text
preview = text[:500] + "..." if len(text) > 500 else text
return f"✅ PDF uploaded and processed successfully. Preview:\n\n{preview}"
except Exception as e:
return f"❌ Error processing PDF: {str(e)}"
# 💬 Ask the open-source LLM (Mistral-7B via OpenRouter)
def ask_open_source_llm(question, model_choice="nvidia/llama-3.1-nemotron-nano-8b-v1:free"):
global pdf_text
if not pdf_text:
return "⚠️ Please upload a PDF document first."
# Limit text to prevent token overflow
limited_text = pdf_text[:3000] # First 3000 characters
# Create prompt based on question
if not question:
prompt = f"Summarize the following document:\n\n{limited_text}"
else:
prompt = f"The document says:\n\n{limited_text}\n\nNow answer this: {question}"
# Call the API
url = "https://openrouter.ai/api/v1/chat/completions"
headers = {
"Authorization": f"Bearer {OPENROUTER_API_KEY}",
"Content-Type": "application/json"
}
data = {
"model": model_choice,
"messages": [{"role": "user", "content": prompt}]
}
try:
response = requests.post(url, headers=headers, json=data)
if response.status_code == 200:
return response.json()["choices"][0]["message"]["content"]
else:
return f"❌ Error: {response.text}"
except Exception as e:
return f"❌ An error occurred: {str(e)}"
# Gradio app function
def process_query(pdf_file, question, model_choice):
# First extract text if a PDF is uploaded
if pdf_file is not None:
result = extract_text_from_pdf(pdf_file)
if result.startswith("❌ Error"):
return result
# Then process the question
if question:
return ask_open_source_llm(question, model_choice)
else:
return ask_open_source_llm("Please summarize this document.", model_choice)
# Create Gradio interface
with gr.Blocks(title="PDF Document Analysis") as app:
gr.Markdown("# 📚 PDF Document Analysis with LLM")
gr.Markdown("Upload a PDF document and ask questions about its content.")
with gr.Row():
with gr.Column(scale=1):
pdf_input = gr.File(label="Upload PDF Document", file_types=[".pdf"])
model_choice = gr.Dropdown(
choices=[
"nvidia/llama-3.1-nemotron-nano-8b-v1:free",
"mistralai/mistral-7b-instruct-v0.1:free",
"meta-llama/llama-2-13b-chat:free"
],
label="LLM Model",
value="nvidia/llama-3.1-nemotron-nano-8b-v1:free"
)
question_input = gr.Textbox(label="Ask a question (or leave empty for summary)", lines=2)
submit_btn = gr.Button("Process", variant="primary")
with gr.Column(scale=2):
output = gr.Textbox(label="Response", lines=15)
# Set up event handlers
submit_btn.click(
fn=process_query,
inputs=[pdf_input, question_input, model_choice],
outputs=output
)
gr.Markdown("### 📝 Notes")
gr.Markdown("- For large documents, only the first 3000 characters are analyzed")
gr.Markdown("- You can change the LLM model from the dropdown menu")
gr.Markdown("- Leave the question field empty to get a general summary")
# Launch the app
app.launch(debug=True, share=True) |