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9b255cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | import gradio as gr
import PyPDF2
import cohere
from pinecone import Pinecone
from sentence_transformers import SentenceTransformer
import io
# Initialize Pinecone and connect to the index
pc = Pinecone(api_key="0f78bc1b-81f7-4a15-9af3-0fbcf0acdb4e")
index = pc.Index("quickstart")
# Load the sentence transformer model
model = SentenceTransformer('all-MiniLM-L6-v2')
# Initialize Cohere with your API key
co = cohere.Client("CxIrucBVA8NNJJOBUnxwRWq488MVydBku1DlqP1u")
def extract_text_from_pdf(pdf_file):
"""Extracts text from the uploaded PDF, with error handling."""
try:
if pdf_file is None:
return "No file uploaded."
# Read the PDF content from the file path
with open(pdf_file.name, 'rb') as f:
pdf_reader = PyPDF2.PdfReader(f)
text = ""
for page_num in range(len(pdf_reader.pages)):
text += pdf_reader.pages[page_num].extract_text() or ""
if not text.strip():
return "The uploaded PDF is empty or has no readable content."
return text
except PyPDF2.errors.PdfReadError:
return "The uploaded PDF is encrypted or unreadable."
except Exception as e:
return f"Error reading PDF: {str(e)}"
def store_pdf_embeddings(pdf_text):
"""Generate and store embeddings for the uploaded PDF content."""
segments = [pdf_text[i:i + 512] for i in range(0, len(pdf_text), 512)]
embeddings = model.encode(segments)
vectors = [(f"seg-{i}", embed.tolist()) for i, embed in enumerate(embeddings)]
index.upsert(vectors=vectors)
return "PDF uploaded and stored successfully!"
def ask_question(query):
"""Handle user questions and generate answers based on the PDF content."""
query_embedding = model.encode(query).tolist()
# Retrieve the most relevant segment from Pinecone
result = index.query(top_k=1, vector=query_embedding)
retrieved_seg_id = result['matches'][0]['id']
segment_text = f"Segment: {retrieved_seg_id}"
# Generate the answer using the retrieved segment as context
prompt = f"{segment_text}\nQuestion: {query}\nAnswer:"
response = co.generate(
model="command-xlarge-nightly",
prompt=prompt,
max_tokens=50
)
# Return both the segment and the answer
answer = response.generations[0].text.strip()
return segment_text, answer
# Gradio Interface Setup
with gr.Blocks() as demo:
gr.Markdown("# Interactive QA Bot with PDF Support")
# PDF Upload Section
pdf_input = gr.File(label="Upload PDF", type="filepath", file_types=[".pdf"])
upload_status = gr.Textbox(label="Upload Status", interactive=False)
upload_button = gr.Button("Upload and Store")
# Handle PDF Upload
upload_button.click(
lambda pdf: store_pdf_embeddings(extract_text_from_pdf(pdf))
if pdf is not None else "Please upload a valid PDF.",
inputs=pdf_input, outputs=upload_status
)
# Question and Answer Section
query_input = gr.Textbox(label="Enter your question")
segment_output = gr.Textbox(label="Retrieved Segment", interactive=False)
answer_output = gr.Textbox(label="Answer", interactive=False)
query_button = gr.Button("Ask")
# Handle User Questions
query_button.click(
ask_question, inputs=query_input, outputs=[segment_output, answer_output]
)
demo.launch(share=True) # Set share=True if you want a public link |