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app.py

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+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.")

requirements.txt

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+transformers
+streamlit
+torch
+safetensors
+