Upload app.py

app.py

@@ -1,3 +1,59 @@
-import gradio as gr
+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
 
-gr.load("models/amiguel/fintune_naming_model").launch()
+model_name = "amiguel/fintune_naming_model"  # Replace with your model repo
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def classify_review(text, model, tokenizer, device, max_length=512):
+    model.eval()
+    inputs = tokenizer.encode_plus(
+        text,
+        truncation=True,
+        padding='max_length',
+        max_length=max_length,
+        return_tensors="pt"
+    )
+    input_ids = inputs['input_ids'].to(device)
+    attention_mask = inputs['attention_mask'].to(device)
+
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask=attention_mask)
+        logits = outputs.logits
+    predicted_label = torch.argmax(logits, dim=-1).item()
+    return "Proper Naming otfcn" if predicted_label == 1 else "Wrong Naming notfcn"
+
+def main():
+    st.title("Notifications Naming Classifier")
+
+    input_option = st.radio("Select input option", ("Single Text Query", "Upload Table"))
+
+    if input_option == "Single Text Query":
+        text_query = st.text_input("Enter text query")
+        if st.button("Classify"):
+            if text_query:
+                predicted_label = classify_review(text_query, model, tokenizer, device)
+                st.write("Predicted Label:")
+                st.write(predicted_label)
+            else:
+                st.warning("Please enter a text query.")
+
+    elif input_option == "Upload Table":
+        uploaded_file = st.file_uploader("Choose a file", type=["csv", "xlsx"])
+        if uploaded_file is not None:
+            import pandas as pd
+            if uploaded_file.name.endswith(".csv"):
+                df = pd.read_csv(uploaded_file)
+            else:
+                df = pd.read_excel(uploaded_file)
+
+            text_column = st.selectbox("Select the text column", df.columns)
+            predicted_labels = [classify_review(text, model, tokenizer, device) for text in df[text_column]]
+            df["Predicted Label"] = predicted_labels
+            st.write(df) 
+
+if __name__ == "__main__":
+    main()