Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer
+import json
+
+# Load the dataset
+with open('faq_dataset.json') as f:
+    faq_data = json.load(f)
+
+# Initialize the model and tokenizer
+model_name = "distilbert-base-uncased-distilled-squad"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
+
+# Create a function to get answers
+def get_answer(question):
+    for item in faq_data:
+        context = item['answer']
+        result = nlp(question=question, context=context)
+        if result['score'] > 0.5:
+            return result['answer']
+    return "Sorry, I don't know the answer to that question."
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=get_answer,
+    inputs=gr.inputs.Textbox(label="Ask a Question"),
+    outputs=gr.outputs.Textbox(label="Answer"),
+    title="FAQ Chatbot",
+    description="Ask a question and get an answer from the FAQ dataset."
+)
+
+# Launch the interface
+iface.launch()