Update app.py

app.py

@@ -1,48 +1,48 @@
-import streamlit as st
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
-
-# Define model and tokenizer paths from Hugging Face
-MODEL_PATH = "DrSyedFaizan/mindBERT"
-
-# Load tokenizer and model from Hugging Face Hub
-tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH, from_safetensors=True)
-
-# Streamlit UI setup
-st.title("MindBERT - Mental Health Analysis Chat")
-st.write("Enter a message, and the model will analyze the mental state of the writer.")
-
-user_input = st.text_area("Type your message here:")
-
-if st.button("Analyze Mental State"):
-    if user_input.strip():
-        # Tokenize input
-        inputs = tokenizer(user_input, return_tensors="pt", truncation=True, padding=True)
-        
-        # Make prediction
-        with torch.no_grad():
-            outputs = model(**inputs)
-            logits = outputs.logits
-            predicted_class = torch.argmax(logits, dim=1).item()
-        
-        # Mapping predicted class to mental state
-        label_map = {
-            0: "Anxiety",
-            1: "Bipolar",
-            2: "Depression",
-            3: "Normal",
-            4: "Personality Disorder",
-            5: "Stress",
-            6: "Suicidal"
-        }
-        mental_state = label_map.get(predicted_class, "Unknown")
-        
-        # Display results
-        st.write(f"Predicted Mental State: **{mental_state}**")
-    else:
-        st.warning("Please enter some text for analysis.")
-
-# Footer
-st.markdown("---")
-st.markdown("Developed by Dr. Syed Faizan using MindBERT on Hugging Face.")
+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Define model and tokenizer paths from Hugging Face
+MODEL_PATH = "DrSyedFaizan/mindBERT"
+
+# Load tokenizer and model from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+
+# Streamlit UI setup
+st.title("MindBERT - Mental Health Analysis Chat")
+st.write("Enter a message, and the model will analyze the mental state of the writer.")
+
+user_input = st.text_area("Type your message here:")
+
+if st.button("Analyze Mental State"):
+    if user_input.strip():
+        # Tokenize input
+        inputs = tokenizer(user_input, return_tensors="pt", truncation=True, padding=True)
+        
+        # Make prediction
+        with torch.no_grad():
+            outputs = model(**inputs)
+            logits = outputs.logits
+            predicted_class = torch.argmax(logits, dim=1).item()
+        
+        # Mapping predicted class to mental state
+        label_map = {
+            0: "Anxiety",
+            1: "Bipolar",
+            2: "Depression",
+            3: "Normal",
+            4: "Personality Disorder",
+            5: "Stress",
+            6: "Suicidal"
+        }
+        mental_state = label_map.get(predicted_class, "Unknown")
+        
+        # Display results
+        st.write(f"Predicted Mental State: **{mental_state}**")
+    else:
+        st.warning("Please enter some text for analysis.")
+
+# Footer
+st.markdown("---")
+st.markdown("Developed by Dr. Syed Faizan using MindBERT on Hugging Face.")