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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+logo.png filter=lfs diff=lfs merge=lfs -text

Chatbot.py

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+import streamlit as st
+from google import genai
+from google.genai import types
+from transformers import pipeline
+from collections import Counter
+from huggingface_hub import login
+from streamlit_elements import elements, nivo, mui
+import os
+hf_token = os.getenv("HF_TOKEN")
+login(token=hf_token)
+
+# Handle full reset via query param at the very top
+if "reset" in st.query_params:
+    st.query_params.clear()  # Prevent infinite loop on rerun
+logo_path = "D:\Alpha\logo Background Removed.png"
+st.logo(logo_path,size="large")
+
+# Initialize Gemini client
+genai_token = os.getenv("G_TOKEN")
+client = genai.Client(api_key=genai_token)
+
+# Label mapping
+label_map = {
+    "LABEL_0": 'anxiety',
+    "LABEL_1": 'depression',
+    "LABEL_2": 'bipolar',
+    "LABEL_3": 'normal',
+    "LABEL_4": 'personality disorder',
+    "LABEL_5": 'stress',
+    "LABEL_6": 'suicidal'
+}
+
+# Load your fine-tuned model
+model_name = "Elite13/bert-finetuned-mental-health"
+classifier = pipeline("text-classification", model=model_name, tokenizer=model_name)
+
+# Initialize session state
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+if "total_messages" not in st.session_state:
+    st.session_state.total_messages = 0
+if "emotion_log" not in st.session_state:
+    st.session_state.emotion_log = []
+if "accepted" not in st.session_state:
+    st.session_state.accepted = False
+if "newchat" not in st.session_state:
+    st.session_state.newchat = False
+
+st.markdown("<h1 style='text-align: center;'>Mental Health Chatbot</h1>", unsafe_allow_html=True)
+st.markdown("---")
+
+# Terms and Conditions Screen
+if not st.session_state.accepted:
+    st.markdown("<h2 style ='text-align:center;'>Terms and Conditions</h2>", unsafe_allow_html=True)
+    st.markdown("""
+        **This is a University Project.**
+
+        - The application is part of academic coursework or research.
+        - Data entered here may be used for project evaluation or improvement.
+        - No personal data will be stored or shared beyond academic purposes.
+
+        By clicking **'I Agree'**, you acknowledge and accept the above.
+    """)
+    if st.button("✅ I Agree"):
+        st.session_state.accepted = True
+        st.rerun()
+
+# Main App Interface
+if st.session_state.accepted:
+
+    # Display chat history
+    for msg in st.session_state.messages:
+        with st.chat_message(msg["role"]):
+            st.markdown(msg["content"])
+
+    # Chat input
+    if prompt := st.chat_input("Say something..."):
+        # Display user input
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+        st.session_state.total_messages += 1
+
+        # Gemini response
+        response = client.models.generate_content(
+            model="gemini-2.0-flash",
+            config=types.GenerateContentConfig(system_instruction=(
+                "You are a kind and supportive friend. Speak casually like a real human, "
+                "offering empathy, emotional comfort, and helpful suggestions in a non-judgmental tone. "
+                "Always listen first, validate the user's feelings, and respond in a warm, understanding way. "
+                "Use short, friendly sentences like a close friend would. Avoid sounding robotic or overly formal. "
+                "If needed, gently encourage the user or suggest simple coping strategies — but never force advice. "
+                "Tone: supportive, chill, safe, and human-like. Don't diagnose, just be there like a good friend."
+            )),
+            contents=prompt
+        )
+        reply = response.text
+
+        # Display Gemini reply
+        st.session_state.messages.append({"role": "assistant", "content": reply})
+        with st.chat_message("assistant"):
+            st.markdown(reply)
+
+        # Run classifier
+        results = classifier(prompt, return_all_scores=True)
+        top_pred = max(results[0], key=lambda x: x['score'])
+        emotion = label_map[top_pred['label']]
+        confidence = top_pred['score']
+        st.session_state.emotion_log.append(emotion)
+
+        # Show detected emotion
+        st.markdown(f"🧠 **Detected Emotion:** `{emotion}` ({confidence:.2f} confidence)")
+
+    # Show emotional report
+    st.session_state.newchat = False
+    st.markdown("---")
+    if st.session_state.total_messages > 0:
+        if st.button("📊 View Emotional Report"):
+            emotion_counts = Counter(st.session_state.emotion_log)
+            total = st.session_state.total_messages
+            percentages = {e: (c / total) * 100 for e, c in emotion_counts.items()}
+
+            st.markdown("### Emotion Breakdown")
+            for e, p in percentages.items():
+                st.markdown(f"- **{e.capitalize()}**: {p:.2f}%")
+
+            # Prepare data for Nivo Pie chart
+            nivo_data = [
+                {"id": emotion, "label": emotion.capitalize(), "value": count}
+                for emotion, count in emotion_counts.items()
+            ]
+
+            # Show interactive pie chart using Nivo
+            with elements("nivo_pie"):
+                with mui.Box(sx={"height": 500}):
+                        nivo.Pie(
+                            data=nivo_data,
+                            margin={ "top": 40, "right": 80, "bottom": 80, "left": 80 },
+                            innerRadius=0.5,
+                            padAngle=0.7,
+                            cornerRadius=3,
+                            activeOuterRadiusOffset=8,
+                            borderWidth=1,
+                            borderColor={ "from": "color", "modifiers": [["darker", 0.2]] },
+                            arcLinkLabelsSkipAngle=10,
+                            arcLinkLabelsTextColor="#333333",
+                            arcLinkLabelsThickness=2,
+                            arcLinkLabelsColor={ "from": "color" },
+                            arcLabelsSkipAngle=10,
+                            arcLabelsTextColor={ "from": "color", "modifiers": [["darker", 2]] },
+                            legends=[
+                                {
+                                    "anchor": "bottom",
+                                    "direction": "row",
+                                    "translateY": 56,
+                                    "itemWidth": 100,
+                                    "itemHeight": 18,
+                                    "symbolSize": 18,
+                                    "symbolShape": "circle",
+                                    "itemTextColor": "#999",
+                                    "effects": [
+                                        {
+                                            "on": "hover",
+                                            "style": {
+                                                "itemTextColor": "#000"
+                                            }
+                                        }
+                                    ]
+                                }
+                            ],
+                            theme={
+                                "background": "#ffffff",
+                                "textColor": "#333333",
+                                "tooltip": {
+                                    "container": {
+                                        "background": "#ffffff",
+                                        "color": "#333333"
+                                    }
+                                }
+                            }
+                        )
+            st.markdown("---")
+            st.markdown("### Summary Report")
+            response = client.models.generate_content(
+            model="gemini-2.0-flash",
+            config=types.GenerateContentConfig(system_instruction=(
+                "You are a mental health chatbot that provides a summary of the user's emotional state based on their chat history. "
+                "Use the detected emotions to create a friendly and supportive report. "
+                "Include the most common emotions, their percentages, and any patterns you notice. "
+                "Keep the tone warm, empathetic, and encouraging."
+            )),
+            contents=st.session_state.emotion_log )
+            reply = response.text
+            st.markdown(reply)
+            st.session_state.newchat = True
+
+# Reset button after report
+if st.session_state.newchat:
+    st.markdown("---")
+    if st.button("🗑️ Clear Chat"):
+        st.session_state.clear()
+        st.query_params["reset"] = "true"
+        st.rerun()