Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+@st.cache_resource
+def load_model_and_tokenizer():
+    model_name = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        device_map="auto", 
+        trust_remote_code=False, 
+        revision="main"
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+    return model, tokenizer
+
+model, tokenizer = load_model_and_tokenizer()
+
+# Define the prompt template
+def generate_prompt(comment):
+    instructions = f"""Virtual Psychologist, communicates with empathy and understanding, focusing on mental health support and providing advice within its expertise. \
+    It actively listens, acknowledges emotions, and avoids overly clinical or technical language unless specifically requested. \
+    It reacts to feedback with warmth and adjusts its tone to match the individual's needs, offering encouragement and validation as appropriate. \
+    Responses are tailored in length and tone to ensure a supportive and conversational experience.
+    """
+    return f"[INST] {instructions} \n{comment} \n[/INST]"
+
+# Define the response generator
+def get_response(comment):
+    prompt = generate_prompt(comment)
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
+    outputs = model.generate(
+        input_ids=inputs["input_ids"].to("cuda"), 
+        attention_mask=inputs["attention_mask"].to("cuda"), 
+        max_new_tokens=140, 
+        pad_token_id=tokenizer.eos_token_id
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response.split("[/INST]")[-1].strip()
+
+# Streamlit app layout
+st.title("Virtual Psychologist")
+st.markdown("This virtual psychologist offers empathetic responses to your comments or questions. Enter your message below.")
+
+user_input = st.text_input("Your Comment/Question:", placeholder="Type here...")
+
+if user_input:
+    with st.spinner("Generating response..."):
+        response = get_response(user_input)
+    st.write("### Response:")
+    st.write(response)
+
+st.markdown("Built with ❤️ using [Hugging Face Transformers](https://huggingface.co/transformers/) and [Streamlit](https://streamlit.io/).")