Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the fine-tuned DialoGPT model and tokenizer from Hugging Face
+@st.cache_resource  # Cache the model to avoid reloading every time
+def load_model():
+    try:
+        # Load the fine-tuned model and tokenizer from the Hugging Face Hub
+        st.write("Loading model and tokenizer...")
+        model = AutoModelForCausalLM.from_pretrained("username/fine-tuned-dialoGPT-crm-chatbot")
+        tokenizer = AutoTokenizer.from_pretrained("username/fine-tuned-dialoGPT-crm-chatbot")
+        return model, tokenizer
+    except Exception as e:
+        st.error(f"Failed to load the model or tokenizer: {e}")
+        return None, None
+
+# Define the chatbot function that generates responses
+def generate_response(model, tokenizer, input_text, max_length=100):
+    try:
+        # Tokenize the input text
+        inputs = tokenizer.encode(input_text, return_tensors="pt")
+        
+        # Generate a response using the model
+        outputs = model.generate(inputs, max_length=max_length, pad_token_id=tokenizer.eos_token_id)
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        return response
+    except Exception as e:
+        st.error(f"Error during text generation: {e}")
+        return "Sorry, something went wrong while generating the response."
+
+# Main Streamlit app function
+def chatbot_app():
+    # Load model and tokenizer
+    model, tokenizer = load_model()
+    
+    if model is None or tokenizer is None:
+        st.error("Unable to load the chatbot model. Please check API or model availability.")
+        return
+    
+    st.title("CRM Chatbot")
+    st.write("This chatbot helps with customer service inquiries. Feel free to ask anything!")
+
+    # Chat history to maintain a conversation flow
+    if 'chat_history' not in st.session_state:
+        st.session_state['chat_history'] = []
+
+    # Input box for user message
+    user_input = st.text_input("You:", value="", key="input")
+    
+    # Submit button to generate a response
+    if st.button("Send") and user_input:
+        # Display the user's message
+        st.session_state.chat_history.append(f"You: {user_input}")
+
+        # Generate chatbot's response
+        bot_response = generate_response(model, tokenizer, user_input)
+        st.session_state.chat_history.append(f"Chatbot: {bot_response}")
+
+    # Display the chat history
+    for message in st.session_state.chat_history:
+        st.write(message)
+
+# Run the chatbot app
+if __name__ == "__main__":
+    chatbot_app()