Update app.py

app.py

@@ -1,17 +1,87 @@
 import gradio as gr
+import pandas as pd
+import numpy as np
+from transformers import pipeline, BertTokenizer, BertModel
+import faiss
+import torch
+import json
 import spaces
 
-@spaces.GPU
-def simple_greeting(name):
-    return f"Hello {name}!"
+# Load CSV data
+data = pd.read_csv('RBDx10kstats.csv')
 
+# Function to safely convert JSON strings to numpy arrays
+def safe_json_loads(x):
+    try:
+        return np.array(json.loads(x), dtype=np.float32)  # Ensure the array is of type float32
+    except json.JSONDecodeError as e:
+        print(f"Error decoding JSON: {e}")
+        return np.array([], dtype=np.float32)  # Return an empty array or handle it as appropriate
+
+# Apply the safe_json_loads function to the embedding column
+data['embedding'] = data['embedding'].apply(safe_json_loads)
+
+# Filter out any rows with empty embeddings
+data = data[data['embedding'].apply(lambda x: x.size > 0)]
+
+# Initialize FAISS index
+dimension = len(data['embedding'].iloc[0])
+res = faiss.StandardGpuResources()  # use a single GPU
+
+# Create FAISS index
+if faiss.get_num_gpus() > 0:
+    gpu_index = faiss.IndexFlatL2(dimension)
+    gpu_index = faiss.index_cpu_to_gpu(res, 0, gpu_index)  # move to GPU
+else:
+    gpu_index = faiss.IndexFlatL2(dimension)  # fall back to CPU
+
+# Ensure embeddings are stacked as float32
+embeddings = np.vstack(data['embedding'].values).astype(np.float32)
+gpu_index.add(embeddings)
+
+# Check if GPU is available
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Load QA model
+qa_model = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad", device=0 if torch.cuda.is_available() else -1)
+
+# Load BERT model and tokenizer
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertModel.from_pretrained('bert-base-uncased').to(device)
+
+# Function to embed the question using BERT
+def embed_question(question, model, tokenizer):
+    inputs = tokenizer(question, return_tensors='pt').to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    return outputs.last_hidden_state.mean(dim=1).cpu().numpy().astype(np.float32)
+
+# Function to retrieve the relevant document and generate a response
+@spaces.GPU(duration=120)
+def retrieve_and_generate(question):
+    # Embed the question
+    question_embedding = embed_question(question, model, tokenizer)
+    
+    # Search in FAISS index
+    _, indices = gpu_index.search(question_embedding, k=1)
+    
+    # Retrieve the most relevant document
+    relevant_doc = data.iloc[indices[0][0]]
+    
+    # Use the QA model to generate the answer
+    context = relevant_doc['Abstract']
+    response = qa_model(question=question, context=context)
+    
+    return response['answer']
+
+# Create a Gradio interface
 interface = gr.Interface(
-    fn=simple_greeting,
-    inputs=gr.Textbox(lines=2, placeholder="Enter your name..."),
+    fn=retrieve_and_generate,
+    inputs=gr.Textbox(lines=2, placeholder="Ask a question about the documents..."),
     outputs="text",
-    title="Simple Greeting",
-    description="Test Gradio Interface"
+    title="RAG Chatbot",
+    description="Ask questions about the documents in the CSV file."
 )
 
 # Launch the Gradio app
-interface.launch()
+interface.launch()