Create app.py

app.py

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+import streamlite as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+# Load chatbot model
+chatbot_model = "microsoft/DialoGPT-medium"
+tokenizer = AutoTokenizer.from_pretrained(chatbot_model)
+model = AutoModelForCausalLM.from_pretrained(chatbot_model)
+
+# Load emotion detection model
+emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
+
+st.title("🧠 Mental Health Chatbot")
+
+# Chat history
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# User Input
+user_input = st.text_input("You:", key="user_input")
+
+if st.button("Send"):
+    if user_input:
+        # Generate chatbot response
+        input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
+        output = model.generate(input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id)
+        response = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+
+        # Detect emotion
+        emotion_result = emotion_pipeline(user_input)
+        emotion = emotion_result[0]["label"]
+
+        # Store chat history
+        st.session_state.chat_history.append(("You", user_input))
+        st.session_state.chat_history.append(("Bot", response))
+
+        # Display chat
+        for sender, msg in st.session_state.chat_history:
+            st.write(f"**{sender}:** {msg}")
+
+        # Display emotion
+        st.write(f"🧠 **Emotion Detected:** {emotion}")