Create app.py

app.py

@@ -0,0 +1,108 @@
+import pandas as pd
+import torch
+import faiss
+from transformers import DistilBertTokenizer, DistilBertModel
+import streamlit as st
+import numpy as np
+
+# Initialize tokenizer and model
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+model = DistilBertModel.from_pretrained('distilbert-base-uncased')
+
+# Load and preprocess drug names
+def load_drug_names(file_path):
+    df = pd.read_csv(file_path)
+    if 'drug_name' in df.columns:
+        return df['drug_name'].str.lower().str.strip().tolist()
+    else:
+        st.error("Column 'drug_name' not found in the CSV file.")
+        st.stop()
+
+# Get embeddings
+def get_embeddings(texts):
+    inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    return outputs.last_hidden_state.mean(dim=1).numpy()
+
+# Create FAISS index
+def create_faiss_index(embeddings):
+    dimension = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dimension)
+    index.add(embeddings)
+    return index
+
+# Load FAISS index
+def load_faiss_index(index_file):
+    return faiss.read_index(index_file)
+
+# Check if FAISS index is empty
+def is_faiss_index_empty(index_file):
+    try:
+        index = faiss.read_index(index_file)
+        return index.ntotal == 0
+    except:
+        return True
+
+# Search FAISS index
+def search_index(index, embedding, k=1):
+    distances, indices = index.search(embedding, k)
+    return distances, indices
+
+# Load drug names
+drug_names = load_drug_names('drug_names.csv')
+
+# Check if FAISS index needs to be created
+if is_faiss_index_empty('faiss_index.index'):
+    embeddings = get_embeddings(drug_names)
+    index = create_faiss_index(embeddings)
+    faiss.write_index(index, 'faiss_index.index')
+else:
+    index = load_faiss_index('faiss_index.index')
+
+# Streamlit app
+st.title("Doctor's Handwritten Prescription Prediction")
+
+# Single input prediction
+single_drug_name = st.text_input("Enter the partial or misspelled drug name:")
+if st.button("Predict Single Drug Name"):
+    if single_drug_name:
+        single_embedding = get_embeddings([single_drug_name.lower().strip()])
+        distances, indices = search_index(index, single_embedding)
+        closest_drug_name = drug_names[indices[0][0]]
+        st.write(f"Predicted Drug Name: {closest_drug_name}")
+    else:
+        st.write("Please enter a drug name to predict.")
+
+# Batch prediction
+st.header("Batch Prediction")
+uploaded_pred_file = st.file_uploader("Choose a CSV file with predictions", type="csv")
+if uploaded_pred_file is not None:
+    st.write("Uploaded prediction file preview:")
+    pred_df = pd.read_csv(uploaded_pred_file)
+    st.write(pred_df.head())
+    
+    if 'predicted_drug_name' in pred_df.columns:
+        pred_texts = pred_df['predicted_drug_name'].str.lower().str.strip().tolist()
+    elif 'drug_name' in pred_df.columns:
+        pred_texts = pred_df['drug_name'].str.lower().str.strip().tolist()
+    else:
+        st.error("The CSV file must contain a column named 'predicted_drug_name' or 'drug_name'.")
+        st.stop()
+
+    pred_embeddings = get_embeddings(pred_texts)
+    predictions = []
+    for i, (pred_text, pred_embedding) in enumerate(zip(pred_texts, pred_embeddings), start=1):
+        distances, indices = search_index(index, np.expand_dims(pred_embedding, axis=0))
+        closest_drug_name = drug_names[indices[0][0]]
+        predictions.append((i, pred_text, closest_drug_name))
+
+    results_df = pd.DataFrame(predictions, columns=['Serial No', 'Original Prediction', 'Closest Drug Name'])
+    results_df.to_csv('predictions_with_matches.csv', index=False)
+    st.write("Batch prediction completed. You can download the results below.")
+    st.download_button(
+        label="Download Predictions",
+        data=results_df.to_csv(index=False).encode('utf-8'),
+        file_name='predictions_with_matches.csv',
+        mime='text/csv',
+    )