Create chatbot.py

chatbot.py

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+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+from simple_salesforce import Salesforce
+from model import label_encoder  # Assuming the label_encoder was saved in the model.py file
+
+# Load pre-trained model and tokenizer
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+model = BertForSequenceClassification.from_pretrained("results")  # Path to your trained model
+
+# Salesforce authentication setup
+sf = Salesforce(username="your_username", password="your_password", security_token="your_token")
+
+# Function to classify the intent of user input
+def classify_intent(user_input):
+    # Tokenize the user input and make a prediction
+    encoded_input = tokenizer(user_input, padding=True, truncation=True, return_tensors="pt")
+    
+    # Run the model without gradients for efficiency
+    with torch.no_grad():
+        output = model(**encoded_input)
+    
+    # Get the predicted label and return it
+    predicted_label = torch.argmax(output.logits, dim=1).item()
+    return label_encoder.inverse_transform([predicted_label])[0]  # Return intent label
+
+# Function to generate a response based on the classified intent
+def generate_response(intent):
+    # Simple responses based on predefined intents
+    responses = {
+        "greeting": "Hi, how can I help you today?",
+        "goodbye": "Goodbye! Have a nice day!",
+        "question": "Hugging Face is a company that specializes in NLP models."
+    }
+    
+    # Return response for the given intent
+    return responses.get(intent, "Sorry, I didn't understand that.")
+
+# Function to create a case in Salesforce based on user input and chatbot response
+def create_case_in_salesforce(user_input, response):
+    case_data = {
+        'Subject': f"User Query: {user_input}",
+        'Description': f"User asked: {user_input}\nResponse: {response}"
+    }
+    
+    # Create the case in Salesforce
+    case = sf.Case.create(case_data)
+    print(f"Case Created with ID: {case['id']}")
+
+# Main function to handle the full interaction
+def chatbot_response(user_input):
+    # Step 1: Classify the user input into an intent
+    intent = classify_intent(user_input)
+    
+    # Step 2: Generate the appropriate response based on the intent
+    response = generate_response(intent)
+    
+    # Step 3: Optionally create a case in Salesforce (e.g., for queries or support requests)
+    create_case_in_salesforce(user_input, response)
+    
+    # Step 4: Return the chatbot's response
+    return response
+
+# Example of using the chatbot
+if __name__ == "__main__":
+    user_input = "Hello, tell me about Hugging Face."
+    print(chatbot_response(user_input))  # Should print the chatbot's response and create a Salesforce case