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import gradio as gr
import PyPDF2
from transformers import AutoTokenizer, AutoModel
import torch
from weaviate import WeaviateClient
from weaviate.auth import AuthApiKey
import cohere
auth = AuthApiKey(api_key="7VoeYTjkOS4aHINuhllGpH4JPgE2QquFmSMn")
client = WeaviateClient(
url="https://vgwhgmrlqrqqgnlb1avjaa.c0.us-west3.gcp.weaviate.cloud",
auth_client=auth
)
cohere_client = cohere.Client("LEvCVeZkqZMW1aLYjxDqlstCzWi4Cvlt9PiysqT8")
def load_pdf(file):
reader = PyPDF2.PdfReader(file)
text = ''
for page in reader.pages:
text += page.extract_text()
return text
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
def get_embeddings(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
embeddings = model(**inputs).last_hidden_state.mean(dim=1).squeeze().cpu().numpy()
return embeddings
def upload_document_chunks(chunks):
doc_collection = client.collections.get("Document")
for chunk in chunks:
embedding = get_embeddings(chunk)
doc_collection.data.insert(
properties={"content": chunk},
vector=embedding.tolist()
)
def query_answer(query):
query_embedding = get_embeddings(query)
response = client.collections.get("Document").query.near_vector(
near_vector=query_embedding.tolist(),
limit=3
)
return response.objects
def generate_response(context, query):
response = cohere_client.generate(
model='command',
prompt=f"Context: {context}\n\nQuestion: {query}?\nAnswer:",
max_tokens=100
)
return response.generations[0].text.strip()
def qa_pipeline(pdf_file, query):
document_text = load_pdf(pdf_file)
document_chunks = [document_text[i:i+500] for i in range(0, len(document_text), 500)]
upload_document_chunks(document_chunks)
top_docs = query_answer(query)
context = ' '.join([doc.properties['content'] for doc in top_docs])
answer = generate_response(context, query)
return context, answer
with gr.Blocks(theme="compact") as demo:
gr.Markdown(
"""
<div style="text-align: center; font-size: 28px; font-weight: bold; margin-bottom: 20px; color: #2D3748;">
📄 Interactive QA Bot 🔍
</div>
<p style="text-align: center; font-size: 16px; color: #4A5568;">
Upload a PDF document, ask questions, and receive answers based on the document content.
</p>
<hr style="border: 1px solid #CBD5E0; margin: 20px 0;">
"""
)
with gr.Row():
with gr.Column(scale=1):
pdf_input = gr.File(label="📁 Upload PDF", file_types=[".pdf"])
query_input = gr.Textbox(label="❓ Ask a Question", placeholder="Enter your question here...")
submit_button = gr.Button("🔍 Submit")
with gr.Column(scale=2):
doc_segments_output = gr.Textbox(label="📜 Retrieved Document Segments", lines=10)
answer_output = gr.Textbox(label="💬 Answer", lines=3)
submit_button.click(
qa_pipeline,
inputs=[pdf_input, query_input],
outputs=[doc_segments_output, answer_output]
)
gr.Markdown(
"""
<style>
body {
background-color: #EDF2F7;
}
input[type="file"] {
background-color: #3182CE;
color: white;
padding: 8px;
border-radius: 5px;
}
button {
background-color: #3182CE;
color: white;
padding: 10px;
font-size: 16px;
border-radius: 5px;
cursor: pointer;
}
button:hover {
background-color: #2B6CB0;
}
textarea {
border: 2px solid #CBD5E0;
border-radius: 8px;
padding: 10px;
background-color: #FAFAFA;
}
</style>
"""
)
demo.launch() |