Update app.py

app.py

@@ -1,87 +1,15 @@
 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
 
-# Load CSV data
-data = pd.read_csv('RBDx10kstats.csv')
+def simple_greeting(name):
+    return f"Hello {name}!"
 
-# 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=retrieve_and_generate,
-    inputs=gr.Textbox(lines=2, placeholder="Ask a question about the documents..."),
+    fn=simple_greeting,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your name..."),
     outputs="text",
-    title="RAG Chatbot",
-    description="Ask questions about the documents in the CSV file."
+    title="Simple Greeting",
+    description="Test Gradio Interface"
 )
 
 # Launch the Gradio app
-interface.launch()
+interface.launch()