Update app.py

app.py

@@ -47,18 +47,21 @@ st.markdown('<div class="blurred-background"></div>', unsafe_allow_html=True)
 
 #"""""""""""""""""""""""""   Application Code Starts here   """""""""""""""""""""""""""""""""""""""""""""
 
-# Load the text generation model pipeline
+# Path to the GGUF model file
+MODEL_PATH = "QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF/model.gguf"
+
+# Load Llama model
 @st.cache_resource
-def load_text_generation_model():
+def load_llama_model():
     try:
-        return pipeline("text-generation", model="QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF")
+        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
 
-text_generator = load_text_generation_model()
+llama_model = load_llama_model()
 
-# Load the counseling dataset
+# Load counseling dataset
 @st.cache_resource
 def load_counseling_dataset():
     return load_dataset("Amod/mental_health_counseling_conversations")
@@ -73,8 +76,8 @@ This platform is designed to provide **supportive, positive, and encouraging res
 """)
 
 # Check if the model loaded correctly
-if text_generator is None:
-    st.error("The text generation model could not be loaded. Please check your Hugging Face configuration.")
+if llama_model is None:
+    st.error("The text generation model could not be loaded. Please check your configuration.")
 else:
     # Explore dataset for additional context or resources (optional)
     if st.checkbox("Show Example Questions and Answers from Dataset"):
@@ -90,14 +93,13 @@ else:
     if st.button("Get Supportive Response"):
         if user_input.strip():
             try:
-                # Generate response using the text generation pipeline
+                # Generate response using Llama
                 prompt = f"User: {user_input}\nCounselor:"
-                response = text_generator(prompt, max_length=200, num_return_sequences=1)
+                response = llama_model(prompt, max_tokens=200, stop=["\n", "User:"])
                 
                 # Extract and display the response
-                counselor_reply = response[0]["generated_text"].split("Counselor:")[-1].strip()
                 st.subheader("Counselor's Response:")
-                st.write(counselor_reply)
+                st.write(response["choices"][0]["text"].strip())
             except Exception as e:
                 st.error(f"An error occurred while generating the response: {e}")
         else:
@@ -112,3 +114,4 @@ else:
     """)
 
     st.sidebar.info("This application is not a replacement for professional counseling. If you are in crisis, please seek professional help immediately.")
+