Update app.py

app.py

@@ -1,16 +1,4 @@
 import os
-import subprocess
-
-# Function to install a package if it is not already installed
-def install(package):
-    subprocess.check_call([os.sys.executable, "-m", "pip", "install", package])
-
-# Ensure the necessary packages are installed
-install("transformers")
-install("torch")
-install("pandas")
-install("gradio")
-
 import pandas as pd
 import gradio as gr
 from transformers import AutoModel, AutoTokenizer
@@ -43,25 +31,34 @@ def process_with_model(pec_number):
     return outputs.last_hidden_state.mean(dim=1).squeeze().tolist()
 
 # Combine both functions to create a prediction
-def predict(pec_number):
+def predict(pec_number, file):
     try:
-        df = load_dataset()
+        # Load the dataset from the uploaded file if provided
+        if file is not None:
+            df = pd.read_excel(file.name)
+        else:
+            df = load_dataset()
+
         name = get_name(pec_number, df)
         model_output = process_with_model(pec_number)
         return f"Name: {name}\nModel Output: {model_output}"
     except FileNotFoundError as e:
         return str(e)
 
-# Build the Gradio interface
+# Build the Gradio interface with file upload option
 iface = gr.Interface(
     fn=predict,
-    inputs=gr.Textbox(lines=1, placeholder="Enter PEC Number..."),
+    inputs=[
+        gr.Textbox(lines=1, placeholder="Enter PEC Number..."),
+        gr.File(label="Upload PEC Numbers and Names file (optional)")
+    ],
     outputs="text",
     title="PEC Number Lookup with Model Integration",
-    description="Enter a PEC number to retrieve the corresponding name and process it with a Hugging Face model."
+    description="Enter a PEC number to retrieve the corresponding name and process it with a Hugging Face model. Optionally, upload the Excel file if not found."
 )
 
 # Run the Gradio interface
 if __name__ == "__main__":
     iface.launch()
 
+