Update app.py

app.py

@@ -1,4 +1,4 @@
-import streamlit as st
+import gradio as gr
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 
 # Load chatbot model
@@ -9,78 +9,20 @@ model = AutoModelForCausalLM.from_pretrained(chatbot_model)
 # Load emotion detection model
 emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
 
-st.title("🧠 Mental Health Chatbot")
+# Function to generate chatbot response and emotion analysis
+def generate_response(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)
 
-# Chat history
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
+    # Detect emotion
+    emotion_result = emotion_pipeline(user_input)
+    emotion = emotion_result[0]["label"]
 
-# User Input
-user_input = st.text_input("You:", key="user_input")
+    # Return chatbot response and detected emotion
+    return response, f"Emotion Detected: {emotion}"
 
-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}")
-
-
-
-# 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 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 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)
-
-# # 🏥 Streamlit UI
-# st.title("🧠 Mental Health Assistant Bot")
-
-# 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})")
-
-#         # ✅ 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})")
-
-#         # ✅ AI Chatbot Response
-#         deepseek_response = deepseek_pipeline(user_input, max_length=100, do_sample=True)[0]['generated_text']
-#         st.write(f"🤖 **Chatbot:** {deepseek_response}")
+# Gradio interface setup
+iface = gr.Interface(
+    fn