Create app.py

app.py

@@ -0,0 +1,47 @@
+from flask import Flask, request, jsonify
+from transformers import pipeline
+from simple_salesforce import Salesforce
+
+app = Flask(__name__)
+
+# Load the Hugging Face model for Question Answering (FAQ-based)
+faq_model = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+
+# Connect to Salesforce (replace with your credentials)
+sf = Salesforce(username='your_username', password='your_password', security_token='your_security_token')
+
+# Function to get the answer from the model
+def get_answer(question, context):
+    result = faq_model(question=question, context=context)
+    return result['answer']
+
+# Function to create a case in Salesforce if the chatbot requires more assistance
+def create_case(subject, description):
+    case = sf.Case.create({
+        'Subject': subject,
+        'Description': description,
+        'Status': 'New',
+        'Priority': 'Medium'
+    })
+    return case
+
+# Route to handle the user’s question
+@app.route('/ask', methods=['POST'])
+def ask_question():
+    data = request.get_json()
+    question = data.get('question')
+
+    # Define the FAQ context (static or fetched from a dynamic source)
+    faq_context = "Here are some frequently asked questions and answers about our service..."
+
+    # Get the answer to the question
+    answer = get_answer(question, faq_context)
+
+    # If the answer requires further assistance, create a case in Salesforce
+    if "need help" in answer or "contact us" in answer:
+        create_case('Customer Support Needed', answer)
+
+    return jsonify({"answer": answer})
+
+if __name__ == '__main__':
+    app.run(debug=True, host='0.0.0.0', port=5000)