Update app.py

app.py

@@ -5,6 +5,7 @@ import torch
 import weaviate
 import cohere
 
+# Initialize Weaviate and Cohere clients
 auth_config = weaviate.AuthApiKey(api_key="16LRz5YwOtnq8ov51Lhg1UuAollpsMgspulV")
 client = weaviate.Client(
     url="https://wkoll9rds3orbu9fhzfr2a.c0.asia-southeast1.gcp.weaviate.cloud",
@@ -12,6 +13,7 @@ client = weaviate.Client(
 )
 cohere_client = cohere.Client("LEvCVeZkqZMW1aLYjxDqlstCzWi4Cvlt9PiysqT8")
 
+# Function to extract text from uploaded PDF
 def load_pdf(file):
     reader = PyPDF2.PdfReader(file)
     text = ''
@@ -19,15 +21,18 @@ def load_pdf(file):
         text += reader.pages[page].extract_text()
     return text
 
+# Initialize transformer model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
 model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
 
+# Function to get embeddings for text
 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
 
+# Upload document chunks to Weaviate
 def upload_document_chunks(chunks):
     for idx, chunk in enumerate(chunks):
         embedding = get_embeddings(chunk)
@@ -37,6 +42,7 @@ def upload_document_chunks(chunks):
             vector=embedding.tolist()
         )
 
+# Query Weaviate for relevant document chunks
 def query_answer(query):
     query_embedding = get_embeddings(query)
     result = client.query.get("Document", ["content"])\
@@ -45,6 +51,7 @@ def query_answer(query):
                 .do()
     return result
 
+# Generate answer using Cohere
 def generate_response(context, query):
     response = cohere_client.generate(
         model='command',
@@ -53,32 +60,90 @@ def generate_response(context, query):
     )
     return response.generations[0].text.strip()
 
+# Function to handle the full pipeline: uploading PDF, generating embeddings, answering queries
 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 to Weaviate
     upload_document_chunks(document_chunks)
 
+    # Query Weaviate for document segments related to the query
     response = query_answer(query)
     context = ' '.join([doc['content'] for doc in response['data']['Get']['Document']])
 
+    # Generate response from the retrieved context
     answer = generate_response(context, query)
 
     return context, answer
 
-with gr.Blocks() as demo:
-    gr.Markdown("# Interactive QA Bot")
-
-    pdf_input = gr.File(label="Upload a PDF file", file_types=[".pdf"])
-    query_input = gr.Textbox(label="Ask a question")
-
-    doc_segments_output = gr.Textbox(label="Retrieved Document Segments")
-    answer_output = gr.Textbox(label="Answer")
+# Define Gradio interface with enhanced UI
+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.<br>
+            Powered by <b>Weaviate</b> for document retrieval and <b>Cohere</b> for generating answers.
+        </p>
+        <hr style="border: 1px solid #CBD5E0; margin: 20px 0;">
+        """
+    )
 
-    gr.Button("Submit").click(
+    with gr.Row():
+        with gr.Column(scale=1):
+            pdf_input = gr.File(label="📁 Upload PDF", file_types=[".pdf"], show_label=True)
+            query_input = gr.Textbox(
+                label="❓ Ask a Question",
+                placeholder="Enter your question here...",
+                lines=1
+            )
+            submit_button = gr.Button("🔍 Submit")
+
+        with gr.Column(scale=2):
+            doc_segments_output = gr.Textbox(label="📜 Retrieved Document Segments", placeholder="Document segments will be displayed here...", lines=10)
+            answer_output = gr.Textbox(label="💬 Answer", placeholder="The answer will appear here...", 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>
+        """
+    )
+
+# Launch the Gradio interface
 demo.launch()