Update app.py

app.py

@@ -51,7 +51,7 @@ def load_counseling_dataset():
 
 dataset = load_counseling_dataset()
 
-# Load a Hugging Face-compatible text-generation model (Open Access Alternative)
+# Load text-generation model
 @st.cache_resource
 def load_text_generation_model():
     return pipeline("text-generation", model="EleutherAI/gpt-neo-1.3B")
@@ -65,12 +65,21 @@ 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]
+
 # 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['context']}")
-        st.markdown(f"**Answer:** {example['response']}")
+        st.markdown(f"**Question:** {example[question_col]}")
+        st.markdown(f"**Answer:** {example[answer_col]}")
         st.markdown("---")
 
 # User input for mental health concerns
@@ -78,13 +87,16 @@ user_input = st.text_area("Your question or concern:", placeholder="Type here...
 
 if st.button("Get Supportive Response"):
     if user_input.strip():
-        # 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)
+        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.")