Update app.py

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 from transformers import AutoTokenizer
-from transformers import pipeline
+from llama_cpp import Llama
+#from transformers import pipeline
 #from peft import PeftModel, PeftConfig
 from transformers import AutoModelForCausalLM
 from datasets import load_dataset
@@ -44,6 +45,20 @@ st.markdown(
 # Add the blurred background div
 st.markdown('<div class="blurred-background"></div>', unsafe_allow_html=True)
 
+# Path to your GGUF model file
+MODEL_PATH = "/path/to/QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF"
+
+# Load Llama model
+@st.cache_resource
+def load_llama_model():
+    try:
+        return Llama(model_path=MODEL_PATH, n_threads=8)  # Adjust `n_threads` based on your system
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+llama_model = load_llama_model()
+
 # Load dataset for context
 @st.cache_resource
 def load_counseling_dataset():
@@ -51,13 +66,6 @@ def load_counseling_dataset():
 
 dataset = load_counseling_dataset()
 
-# Load text-generation model
-@st.cache_resource
-def load_text_generation_model():
-    return pipeline("text-generation", model="EleutherAI/gpt-neo-1.3B")
-
-text_generator = load_text_generation_model()
-
 # Streamlit app
 st.title("Mental Health Counseling Chat")
 st.markdown("""
@@ -65,47 +73,41 @@ Welcome to the Mental Health Counseling Chat application.
 This platform is designed to provide supportive, positive, and encouraging responses based on mental health counseling expertise.
 """)
 
-# Check dataset columns
-st.markdown("### Dataset Structure")
-column_names = dataset["train"].column_names
-st.write(f"Columns available in dataset: {column_names}")
-
-# Assuming "question" and "answer" columns exist; adjust as per dataset
-question_col = "question" if "question" in column_names else column_names[0]
-answer_col = "answer" if "answer" in column_names else column_names[1]
+# Check if the model loaded correctly
+if llama_model is None:
+    st.error("The text generation model could not be loaded. Please check the model path and configuration.")
+else:
+    # Explore dataset for additional context or resources (optional)
+    if st.checkbox("Show Example Questions and Answers from Dataset"):
+        sample = dataset["train"].shuffle(seed=42).select(range(3))  # Display 3 random samples
+        for example in sample:
+            st.markdown(f"**Question:** {example.get('context', 'N/A')}")
+            st.markdown(f"**Answer:** {example.get('response', 'N/A')}")
+            st.markdown("---")
 
-# Explore dataset for additional context or resources (optional)
-if st.checkbox("Show Example Questions and Answers from Dataset"):
-    sample = dataset["train"].shuffle(seed=42).select(range(3))  # Display 3 random samples
-    for example in sample:
-        st.markdown(f"**Question:** {example[question_col]}")
-        st.markdown(f"**Answer:** {example[answer_col]}")
-        st.markdown("---")
+    # User input for mental health concerns
+    user_input = st.text_area("Your question or concern:", placeholder="Type here...")
 
-# User input for mental health concerns
-user_input = st.text_area("Your question or concern:", placeholder="Type here...")
+    if st.button("Get Supportive Response"):
+        if user_input.strip():
+            try:
+                # Generate response using Llama
+                prompt = f"User: {user_input}\nCounselor:"
+                response = llama_model(prompt, max_tokens=200, stop=["\n", "User:"])
+                
+                st.subheader("Counselor's Response:")
+                st.write(response["choices"][0]["text"].strip())
+            except Exception as e:
+                st.error(f"An error occurred while generating the response: {e}")
+        else:
+            st.error("Please enter a question or concern to receive a response.")
 
-if st.button("Get Supportive Response"):
-    if user_input.strip():
-        try:
-            # Generate response using the model
-            prompt = f"User: {user_input}\nCounselor:"
-            response = text_generator(prompt, max_length=200, num_return_sequences=1)
-            counselor_reply = response[0]["generated_text"].split("Counselor:")[-1].strip()
-            
-            st.subheader("Counselor's Response:")
-            st.write(counselor_reply)
-        except Exception as e:
-            st.error(f"An error occurred while generating the response: {e}")
-    else:
-        st.error("Please enter a question or concern to receive a response.")
-
-# Sidebar resources
-st.sidebar.header("Additional Mental Health Resources")
-st.sidebar.markdown("""
-- [Mental Health Foundation](https://www.mentalhealth.org)
-- [Mind](https://www.mind.org.uk)
-- [National Suicide Prevention Lifeline](https://suicidepreventionlifeline.org)
-""")
+    # Sidebar resources
+    st.sidebar.header("Additional Mental Health Resources")
+    st.sidebar.markdown("""
+    - [Mental Health Foundation](https://www.mentalhealth.org)
+    - [Mind](https://www.mind.org.uk)
+    - [National Suicide Prevention Lifeline](https://suicidepreventionlifeline.org)
+    """)
 
-st.sidebar.info("This application is not a replacement for professional counseling. If you are in crisis, please seek professional help immediately.")
+    st.sidebar.info("This application is not a replacement for professional counseling. If you are in crisis, please seek professional help immediately.")