Update app.py

app.py

@@ -1,76 +1,351 @@
 import streamlit as st
-from transformers import pipeline
+import requests
+from datetime import datetime
+import pandas as pd
+import os
+import uuid
+import json
 
-st.set_page_config(page_title="Mental Wellness Diary Analyzer", page_icon="🧠")
+# Page configuration
+st.set_page_config(
+    page_title="Mental Wellness Diary Analyzer",
+    page_icon="🧠",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for better appearance
+st.markdown("""
+<style>
+    .main .block-container {
+        padding-top: 2rem;
+        padding-bottom: 2rem;
+    }
+    .stTextArea textarea {
+        font-size: 16px;
+    }
+    .journal-history {
+        border-left: 2px solid #f0f2f6;
+        padding-left: 20px;
+    }
+    .css-1v3fvcr {
+        background-color: #fcfcff;
+    }
+    .stButton button {
+        background-color: #4e89ae;
+        color: white;
+        border-radius: 5px;
+        padding: 0.5rem 1rem;
+        font-weight: bold;
+    }
+    .stButton button:hover {
+        background-color: #3d7092;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# App title and description
 st.title("🧠 Mental Wellness Diary Analyzer")
+st.markdown("""
+Track your mental wellness journey with AI-powered insights. Write your daily thoughts, 
+and receive personalized analysis to help you understand your emotional patterns.
+""")
+
+# Initialize session state for journal entries
+if 'journal_entries' not in st.session_state:
+    # Try to load from file if it exists
+    if os.path.exists('journal_entries.json'):
+        try:
+            with open('journal_entries.json', 'r') as f:
+                st.session_state.journal_entries = json.load(f)
+        except:
+            st.session_state.journal_entries = []
+    else:
+        st.session_state.journal_entries = []
+
+# Hugging Face API setup
+def get_hf_api_token():
+    # First check if token exists in secrets
+    if hasattr(st, 'secrets') and 'huggingface' in st.secrets:
+        return st.secrets["huggingface"]["api_token"]
+    
+    # For local development, check environment variable
+    return os.environ.get("HF_API_TOKEN")
 
-# Load a lightweight language model
-@st.cache_resource
-def load_model():
-    return pipeline("text-generation", model="distilgpt2", max_new_tokens=200)
+# Function to analyze journal entries
+def analyze_journal(entry, model_name="deepseek-ai/deepseek-v1"):
+    api_token = get_hf_api_token()
+    if not api_token:
+        return "Error: No API token found. Please configure your Hugging Face API token in the Spaces secrets."
+    
+    API_URL = f"https://api-inference.huggingface.co/models/{model_name}"
+    
+    headers = {
+        "Authorization": f"Bearer {api_token}",
+        "Content-Type": "application/json",
+    }
+    
+    prompt = f"""You are a compassionate mental wellness assistant. Analyze the following journal entry with empathy and provide helpful insights.
 
-generator = load_model()
+Journal entry: "{entry}"
 
-def analyze_journal(entry):
-    prompt = f"""
-Analyze the following journal entry:
-"{entry}"
-Determine the following:
-1. Emotional tone: [Identify the primary emotions]
-2. Recurring themes: [Identify key themes]
-3. Advice: [Provide encouraging advice]
+Please provide:
+1. Emotional tone: Identify the primary emotions expressed in the entry (like anxious, happy, stressed, etc.)
+2. Recurring themes: Note any patterns or important topics mentioned
+3. Advice: Offer kind, supportive guidance tailored to what was shared
+
+Format your response clearly with these exact headings (Emotional tone, Recurring themes, Advice) but without using JSON.
 """
-    output = generator(prompt)[0]["generated_text"]
     
-    # Extract the analysis part
-    analysis = output.split("Analyze the following journal entry:")[-1].strip()
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "max_new_tokens": 300,
+            "temperature": 0.7,
+            "top_p": 0.95,
+            "do_sample": True
+        }
+    }
     
-    # Format the output in a cleaner way without JSON
-    formatted_analysis = ""
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload)
+        response.raise_for_status()
+        result = response.json()
+        
+        # Extract the text from the response
+        if isinstance(result, list) and len(result) > 0:
+            output = result[0].get("generated_text", "")
+        else:
+            output = str(result)
+        
+        # Format the output nicely
+        formatted_output = ""
+        
+        if "Emotional tone:" in output:
+            emotional_tone = output.split("Emotional tone:")[1].split("Recurring themes:")[0].strip()
+            formatted_output += f"😊 **Emotional tone:** {emotional_tone}\n\n"
+        
+        if "Recurring themes:" in output:
+            themes = output.split("Recurring themes:")[1].split("Advice:")[0].strip()
+            formatted_output += f"🔍 **Recurring themes:** {themes}\n\n"
+        
+        if "Advice:" in output:
+            advice = output.split("Advice:")[1].strip()
+            formatted_output += f"💡 **Advice:** {advice}"
+        
+        return formatted_output if formatted_output else output
+        
+    except requests.exceptions.RequestException as e:
+        st.error(f"API Error: {str(e)}")
+        if hasattr(e, 'response') and e.response is not None:
+            st.error(f"Response: {e.response.text}")
+        return f"Error connecting to the language model: {str(e)}"
+    except Exception as e:
+        return f"An unexpected error occurred: {str(e)}"
+
+# Function to save an entry
+def save_entry(entry, analysis):
+    entry_id = str(uuid.uuid4())
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
+    # Extract emotional tone for tagging (simplified)
+    emotional_tone = "neutral"
     if "Emotional tone:" in analysis:
-        emotional_tone = analysis.split("Emotional tone:")[1].split("Recurring themes:")[0].strip()
-        formatted_analysis += f"😊 **Emotional tone:** {emotional_tone}\n\n"
+        tone_text = analysis.split("Emotional tone:")[1].split("\n\n")[0].strip()
+        if "anxious" in tone_text.lower() or "stress" in tone_text.lower():
+            emotional_tone = "anxious"
+        elif "happy" in tone_text.lower() or "joy" in tone_text.lower():
+            emotional_tone = "happy"
+        elif "sad" in tone_text.lower() or "depress" in tone_text.lower():
+            emotional_tone = "sad"
+        elif "angry" in tone_text.lower() or "frustrat" in tone_text.lower():
+            emotional_tone = "angry"
     
-    if "Recurring themes:" in analysis:
-        themes = analysis.split("Recurring themes:")[1].split("Advice:")[0].strip()
-        formatted_analysis += f"🔍 **Recurring themes:** {themes}\n\n"
+    new_entry = {
+        "id": entry_id,
+        "date": timestamp,
+        "entry": entry,
+        "analysis": analysis,
+        "emotion": emotional_tone
+    }
     
-    if "Advice:" in analysis:
-        advice = analysis.split("Advice:")[1].strip()
-        formatted_analysis += f"💡 **Advice:** {advice}"
+    st.session_state.journal_entries.append(new_entry)
     
-    return formatted_analysis if formatted_analysis else analysis
-
-# User interface
-st.markdown("### Share what's on your mind today")
-journal_entry = st.text_area("✍️ Write your journal entry here:", height=150)
+    # Save to file (for spaces persistence)
+    try:
+        with open('journal_entries.json', 'w') as f:
+            json.dump(st.session_state.journal_entries, f)
+    except Exception as e:
+        st.warning(f"Could not save entries to file: {str(e)}")
 
-col1, col2, col3 = st.columns([1, 1, 1])
-with col2:
-    analyze_button = st.button("🧠 Analyze My Entry", use_container_width=True)
+# Sidebar for navigation and settings
+with st.sidebar:
+    st.header("Navigation")
+    page = st.radio("", ["Write New Entry", "View Journal History"])
+    
+    st.header("Settings")
+    model_choice = st.selectbox(
+        "Language Model",
+        ["deepseek-ai/deepseek-v1", "meta-llama/Llama-2-13b-chat-hf", "google/flan-t5-xxl"],
+        index=0,
+        help="Select which AI model to use for analysis"
+    )
+    
+    st.markdown("---")
+    st.markdown("### About")
+    st.info("""
+    This Mental Wellness Diary Analyzer helps you track your emotional wellbeing.
+    All entries are stored locally in your browser session.
+    
+    **Disclaimer**: This app is not a substitute for professional mental health support.
+    """)
 
-if analyze_button:
-    if journal_entry.strip():
+# Main content based on selected page
+if page == "Write New Entry":
+    st.header("📝 New Journal Entry")
+    
+    # Current date display
+    st.markdown(f"**Today**: {datetime.now().strftime('%A, %B %d, %Y')}")
+    
+    # Journal input
+    journal_entry = st.text_area(
+        "How are you feeling today?",
+        height=200,
+        placeholder="Write about your day, feelings, thoughts, or anything on your mind..."
+    )
+    
+    # Mood tracker (optional quick selection)
+    cols = st.columns(5)
+    with cols[0]:
+        mood_happy = st.button("😊 Happy")
+    with cols[1]:
+        mood_calm = st.button("😌 Calm")
+    with cols[2]:
+        mood_anxious = st.button("😰 Anxious") 
+    with cols[3]:
+        mood_sad = st.button("😢 Sad")
+    with cols[4]:
+        mood_angry = st.button("😠 Angry")
+    
+    # Handle mood button clicks
+    mood_prefix = ""
+    if mood_happy:
+        mood_prefix = "I'm feeling happy today. "
+    elif mood_calm:
+        mood_prefix = "I'm feeling calm and relaxed today. "
+    elif mood_anxious:
+        mood_prefix = "I'm feeling anxious today. "
+    elif mood_sad:
+        mood_prefix = "I'm feeling sad today. "
+    elif mood_angry:
+        mood_prefix = "I'm feeling angry and frustrated today. "
+    
+    if mood_prefix and not journal_entry.startswith(mood_prefix):
+        journal_entry = mood_prefix + journal_entry
+    
+    # Analyze button
+    col1, col2, col3 = st.columns([1, 1, 1])
+    with col2:
+        analyze_button = st.button("🧠 Analyze My Entry", use_container_width=True)
+    
+    # Sample entries
+    with st.expander("Need inspiration? Try a sample entry"):
+        sample_1 = st.button("Sample: Stressed at work")
+        sample_2 = st.button("Sample: Good day")
+        sample_3 = st.button("Sample: Relationship issues")
+        
+        if sample_1:
+            journal_entry = "I feel really stressed and anxious about my work. I haven't been sleeping well, and I'm worried about meeting deadlines. My boss keeps adding more tasks, and I don't know how to say no."
+        elif sample_2:
+            journal_entry = "Today was a great day! I finished a project I've been working on for weeks, and my team was really impressed. I feel proud of myself and motivated to keep up the good work."
+        elif sample_3:
+            journal_entry = "I had an argument with my partner today. We've been fighting a lot lately about small things. I'm not sure if it's just stress or if there's a bigger issue we need to address."
+    
+    # Analysis section
+    if analyze_button and journal_entry.strip():
         with st.spinner("Analyzing your thoughts..."):
-            result = analyze_journal(journal_entry)
-            
+            analysis = analyze_journal(journal_entry, model_choice)
+        
         st.markdown("### 💭 Your Wellness Insights")
-        st.markdown(result)
+        st.markdown(analysis)
+        
+        # Save option
+        if st.button("📋 Save this entry to your journal"):
+            save_entry(journal_entry, analysis)
+            st.success("Entry saved successfully!")
         
-        # Add some supportive message
+        # Supportive message
         st.markdown("---")
         st.markdown("💪 **Remember**: Writing about your feelings is already a positive step for your mental wellness!")
-    else:
-        st.warning("Please share your thoughts in the journal entry box first.")
+    
+    elif analyze_button:
+        st.warning("Please write in your journal entry first.")
 
-# Add a sample entry option
-with st.expander("Need inspiration? Try a sample entry"):
-    if st.button("Use sample entry"):
-        sample_text = "I feel really stressed and anxious about my work. I haven't been sleeping well, and I'm worried about meeting deadlines."
-        st.session_state["sample_text"] = sample_text
-        st.experimental_rerun()
+else:  # View Journal History page
+    st.header("📚 Your Journal History")
+    
+    if not st.session_state.journal_entries:
+        st.info("You haven't saved any journal entries yet. Head to 'Write New Entry' to get started!")
+    else:
+        # Filter options
+        col1, col2 = st.columns(2)
+        with col1:
+            filter_option = st.selectbox(
+                "Filter by emotion:",
+                ["All", "happy", "anxious", "sad", "angry", "neutral"]
+            )
+        with col2:
+            sort_option = st.selectbox(
+                "Sort by:",
+                ["Newest first", "Oldest first"]
+            )
+        
+        # Filter and sort entries
+        filtered_entries = st.session_state.journal_entries
+        if filter_option != "All":
+            filtered_entries = [e for e in filtered_entries if e.get("emotion") == filter_option]
+        
+        if sort_option == "Newest first":
+            filtered_entries = sorted(filtered_entries, key=lambda x: x.get("date", ""), reverse=True)
+        else:
+            filtered_entries = sorted(filtered_entries, key=lambda x: x.get("date", ""))
+        
+        # Display entries
+        for i, entry in enumerate(filtered_entries):
+            with st.expander(f"📝 {entry.get('date', 'Unknown date')}"):
+                st.markdown("#### Journal Entry")
+                st.write(entry.get("entry", ""))
+                
+                st.markdown("#### Analysis")
+                st.markdown(entry.get("analysis", "No analysis available"))
+                
+                # Delete option
+                if st.button(f"🗑️ Delete this entry", key=f"delete_{i}"):
+                    st.session_state.journal_entries.remove(entry)
+                    with open('journal_entries.json', 'w') as f:
+                        json.dump(st.session_state.journal_entries, f)
+                    st.experimental_rerun()
+        
+        # Add insights section if there are enough entries
+        if len(st.session_state.journal_entries) >= 3:
+            st.markdown("---")
+            st.header("📊 Emotional Trends")
+            
+            # Create a dataframe for visualization
+            entries_df = pd.DataFrame(st.session_state.journal_entries)
+            entries_df['date'] = pd.to_datetime(entries_df['date'])
+            
+            # Count emotions
+            emotion_counts = entries_df['emotion'].value_counts()
+            st.bar_chart(emotion_counts)
+            
+            # Most common themes - this would require more sophisticated analysis
+            st.markdown("### Common Themes")
+            st.info("This feature will analyze common themes across your entries in a future update.")
 
-# Use sample if available in session state
-if "sample_text" in st.session_state:
-    journal_entry = st.session_state["sample_text"]
-    del st.session_state["sample_text"]
+# Footer
+st.markdown("---")
+st.caption("""
+**Disclaimer**: This app is for educational purposes only and is not a substitute for professional mental health advice, diagnosis, or treatment. 
+Always seek the advice of your physician or other qualified health provider with any questions regarding a medical condition.
+""")