Create app.py

app.py

@@ -0,0 +1,36 @@
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load pre-trained models
+model_small_name = "microsoft/DialoGPT-small"
+model_large_name = "microsoft/DialoGPT-medium"
+
+tokenizer_small = AutoTokenizer.from_pretrained(model_small_name)
+model_small = AutoModelForCausalLM.from_pretrained(model_small_name)
+
+tokenizer_large = AutoTokenizer.from_pretrained(model_large_name)
+model_large = AutoModelForCausalLM.from_pretrained(model_large_name)
+
+# Function to generate responses
+def generate_response(input_text, model, tokenizer):
+    inputs = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors='pt')
+    outputs = model.generate(inputs, max_length=1000, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Streamlit app
+st.title("Mental Health Chatbot")
+
+# User input
+user_input = st.text_input("You:", "")
+
+if user_input:
+    # Generate responses for both models
+    response_small = generate_response(user_input, model_small, tokenizer_small)
+    response_large = generate_response(user_input, model_large, tokenizer_large)
+
+    # Display responses
+    st.subheader("DialoGPT-small Response:")
+    st.write(response_small)
+
+    st.subheader("DialoGPT-medium Response:")
+    st.write(response_large)