Update app.py

app.py

@@ -1,86 +1,86 @@
-# import streamlit as st
-# from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import streamlit 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 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")
+# Load emotion detection model
+emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
 
-# st.title("🧠 Mental Health Chatbot")
+st.title("🧠 Mental Health Chatbot")
 
-# # Chat history
-# if "chat_history" not in st.session_state:
-#     st.session_state.chat_history = []
+# 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")
+# 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)
+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"]
+        # 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))
+        # 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 chat
+        for sender, msg in st.session_state.chat_history:
+            st.write(f"**{sender}:** {msg}")
 
-#         # Display emotion
-#         st.write(f"🧠 **Emotion Detected:** {emotion}")
+        # Display emotion
+        st.write(f"🧠 **Emotion Detected:** {emotion}")
 
 
 
-import streamlit as st
-from transformers import pipeline, AutoTokenizer
+# import streamlit as st
+# from transformers import pipeline, AutoTokenizer
 
-# ✅ Load Emotion Recognition Model
-emotion_pipeline = pipeline("text-classification", model="ahmettasdemir/distilbert-base-uncased-finetuned-emotion")
+# # ✅ Load Emotion Recognition Model
+# emotion_pipeline = pipeline("text-classification", model="ahmettasdemir/distilbert-base-uncased-finetuned-emotion")
 
-# ✅ Load Stress Detection Model
-stress_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
+# # ✅ Load Stress Detection Model
+# stress_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
 
-# ✅ Load Mental Disorder Detection Model
-mental_bert_pipeline = pipeline("text-classification", model="nlpconnect/vit-gpt2-image-captioning")
+# # ✅ Load Mental Disorder Detection Model
+# mental_bert_pipeline = pipeline("text-classification", model="nlpconnect/vit-gpt2-image-captioning")
 
-# ✅ Load PHQ-9 Depression Severity Classifier
-phq9_pipeline = pipeline("text-classification", model="PHQ-9 Depression Classifier")
+# # ✅ Load PHQ-9 Depression Severity Classifier
+# phq9_pipeline = pipeline("text-classification", model="PHQ-9 Depression Classifier")
 
-# ✅ Load Chatbot Model (DeepSeek)
-deepseek_model = "deepseek-ai/deepseek-llm-7b"
-deepseek_tokenizer = AutoTokenizer.from_pretrained(deepseek_model)
-deepseek_pipeline = pipeline("text-generation", model=deepseek_model, tokenizer=deepseek_tokenizer)
+# # ✅ Load Chatbot Model (DeepSeek)
+# deepseek_model = "deepseek-ai/deepseek-llm-7b"
+# deepseek_tokenizer = AutoTokenizer.from_pretrained(deepseek_model)
+# deepseek_pipeline = pipeline("text-generation", model=deepseek_model, tokenizer=deepseek_tokenizer)
 
-# 🏥 Streamlit UI
-st.title("🧠 Mental Health Assistant Bot")
+# # 🏥 Streamlit UI
+# st.title("🧠 Mental Health Assistant Bot")
 
-user_input = st.text_input("How are you feeling today?", "")
+# user_input = st.text_input("How are you feeling today?", "")
 
-if st.button("Submit"):
-    if user_input:
-        # ✅ Emotion Analysis
-        emotion_result = emotion_pipeline(user_input)[0]
-        st.write(f"**Emotion Detected:** {emotion_result['label']} ({emotion_result['score']:.2f})")
+# if st.button("Submit"):
+#     if user_input:
+#         # ✅ Emotion Analysis
+#         emotion_result = emotion_pipeline(user_input)[0]
+#         st.write(f"**Emotion Detected:** {emotion_result['label']} ({emotion_result['score']:.2f})")
 
-        # ✅ Stress Level Analysis
-        stress_result = stress_pipeline(user_input)[0]
-        st.write(f"**Stress Level:** {stress_result['label']} ({stress_result['score']:.2f})")
+#         # ✅ Stress Level Analysis
+#         stress_result = stress_pipeline(user_input)[0]
+#         st.write(f"**Stress Level:** {stress_result['label']} ({stress_result['score']:.2f})")
 
-        # ✅ Mental Health Condition Detection
-        mental_health_result = mental_bert_pipeline(user_input)[0]
-        st.write(f"**Possible Mental Health Condition:** {mental_health_result['label']} ({mental_health_result['score']:.2f})")
+#         # ✅ Mental Health Condition Detection
+#         mental_health_result = mental_bert_pipeline(user_input)[0]
+#         st.write(f"**Possible Mental Health Condition:** {mental_health_result['label']} ({mental_health_result['score']:.2f})")
 
-        # ✅ AI Chatbot Response
-        deepseek_response = deepseek_pipeline(user_input, max_length=100, do_sample=True)[0]['generated_text']
-        st.write(f"🤖 **Chatbot:** {deepseek_response}")
+#         # ✅ AI Chatbot Response
+#         deepseek_response = deepseek_pipeline(user_input, max_length=100, do_sample=True)[0]['generated_text']
+#         st.write(f"🤖 **Chatbot:** {deepseek_response}")