Delete app (3).py

app (3).py

@@ -1,108 +0,0 @@
-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',
-    )