Create gradio_interface.py

python_Code/gradio_interface.py

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+import os
+import gradio as gr
+import spacy
+
+# Updated model path
+model_path = "./my_trained_model"
+
+# Check if model path exists
+if not os.path.exists(model_path):
+    raise FileNotFoundError(f"Model not found at path: {model_path}")
+
+# Load the trained spaCy model
+nlp = spacy.load(model_path)
+
+# Function to classify text
+def classify_text(text):
+    doc = nlp(text)
+    predicted_labels = doc.cats
+    return predicted_labels
+
+# Function to save results to a file
+def save_to_file(text, predicted_labels):
+    with open("classification_results.txt", "w") as f:
+        f.write("Text: {}\n\n".format(text))
+        for label, score in predicted_labels.items():
+            f.write("{}: {}\n".format(label, score))
+
+# Gradio Interface
+inputs = [
+    gr.inputs.Textbox(lines=7, label="Enter your text"),
+    gr.inputs.File(label="Upload a file")
+]
+
+output = gr.outputs.Textbox(label="Classification Results")
+
+def classify_and_save(input_text, input_file):
+    if input_text:
+        text = input_text
+    elif input_file:
+        # Process the file and extract text
+        with open(input_file.name, "r") as f:
+            text = f.read()
+    
+    predicted_labels = classify_text(text)
+    save_to_file(text, predicted_labels)
+    return predicted_labels
+
+iface = gr.Interface(fn=classify_and_save, inputs=inputs, outputs=output, title="Text Classifier")
+iface.launch(share=True)