Create app.py

app.py

@@ -0,0 +1,167 @@
+import gradio as gr
+from transformers import pipeline
+import random
+from datetime import datetime
+import json
+import os
+
+# Initialize sentiment analysis pipeline
+sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+class JournalCompanion:
+    def __init__(self):
+        # Initialize storage for entries (in-memory for demo)
+        self.entries = []
+        
+        # Reflective prompts based on sentiment
+        self.prompts = {
+            "POSITIVE": [
+                "What made this experience particularly meaningful?",
+                "How can you carry this positive energy forward?",
+                "Who would you like to share this joy with?",
+                "What values of yours were honored in this moment?"
+            ],
+            "NEGATIVE": [
+                "What could help make this situation better?",
+                "What have you learned from this challenge?",
+                "Who could you reach out to for support?",
+                "What would be a small step toward improvement?"
+            ],
+            "NEUTRAL": [
+                "What's on your mind right now?",
+                "What would make today more meaningful?",
+                "What are you looking forward to?",
+                "What would you like to explore further?"
+            ]
+        }
+        
+        # Affirmations based on sentiment
+        self.affirmations = {
+            "POSITIVE": [
+                "You're radiating positive energy! Keep embracing joy.",
+                "Your optimism is inspiring. You're on the right path.",
+                "You have so much to be proud of. Keep shining!",
+                "Your positive mindset creates beautiful opportunities."
+            ],
+            "NEGATIVE": [
+                "It's okay to feel this way. You're stronger than you know.",
+                "Every challenge helps you grow. You've got this.",
+                "Tomorrow brings new opportunities. Be gentle with yourself.",
+                "Your feelings are valid, and this too shall pass."
+            ],
+            "NEUTRAL": [
+                "You're exactly where you need to be right now.",
+                "Your journey is unique and valuable.",
+                "Take a moment to appreciate your progress.",
+                "Every moment is a chance for a fresh perspective."
+            ]
+        }
+
+    def analyze_entry(self, entry_text):
+        """Analyze journal entry and provide feedback"""
+        if not entry_text.strip():
+            return {
+                "message": "Please write something in your journal entry.",
+                "sentiment": "",
+                "prompt": "",
+                "affirmation": ""
+            }
+
+        # Perform sentiment analysis
+        sentiment_result = sentiment_analyzer(entry_text)[0]
+        sentiment = "POSITIVE" if sentiment_result["label"] == "POSITIVE" else "NEGATIVE"
+        
+        # Store entry with metadata
+        entry_data = {
+            "text": entry_text,
+            "timestamp": datetime.now().isoformat(),
+            "sentiment": sentiment,
+            "sentiment_score": sentiment_result["score"]
+        }
+        self.entries.append(entry_data)
+        
+        # Generate response
+        prompt = random.choice(self.prompts[sentiment])
+        affirmation = random.choice(self.affirmations[sentiment])
+        
+        # Calculate sentiment score percentage
+        sentiment_percentage = f"{sentiment_result['score']*100:.1f}%"
+        
+        message = f"Entry analyzed! Sentiment: {sentiment} ({sentiment_percentage} confidence)"
+        
+        return {
+            "message": message,
+            "sentiment": sentiment,
+            "prompt": prompt,
+            "affirmation": affirmation
+        }
+
+    def get_monthly_insights(self):
+        """Generate monthly insights from stored entries"""
+        if not self.entries:
+            return "No entries yet to analyze."
+            
+        total_entries = len(self.entries)
+        positive_entries = sum(1 for entry in self.entries if entry["sentiment"] == "POSITIVE")
+        
+        insights = f"""Monthly Insights:
+        Total Entries: {total_entries}
+        Positive Entries: {positive_entries} ({(positive_entries/total_entries*100):.1f}%)
+        Negative Entries: {total_entries - positive_entries} ({((total_entries-positive_entries)/total_entries*100):.1f}%)
+        """
+        return insights
+
+def create_journal_interface():
+    # Initialize the journal companion
+    journal = JournalCompanion()
+    
+    # Define the interface
+    with gr.Blocks(title="AI Journal Companion") as interface:
+        gr.Markdown("# 📔 AI Journal Companion")
+        gr.Markdown("Write your thoughts and receive AI-powered insights, prompts, and affirmations.")
+        
+        with gr.Row():
+            with gr.Column():
+                # Input components
+                entry_input = gr.Textbox(
+                    label="Journal Entry",
+                    placeholder="Write your journal entry here...",
+                    lines=5
+                )
+                submit_btn = gr.Button("Submit Entry", variant="primary")
+
+            with gr.Column():
+                # Output components
+                result_message = gr.Textbox(label="Analysis Result")
+                sentiment_output = gr.Textbox(label="Detected Sentiment")
+                prompt_output = gr.Textbox(label="Reflective Prompt")
+                affirmation_output = gr.Textbox(label="Daily Affirmation")
+                
+        with gr.Row():
+            insights_btn = gr.Button("Show Monthly Insights")
+            insights_output = gr.Textbox(label="Monthly Insights")
+
+        # Set up event handlers
+        submit_btn.click(
+            fn=journal.analyze_entry,
+            inputs=[entry_input],
+            outputs=[
+                result_message,
+                sentiment_output,
+                prompt_output,
+                affirmation_output
+            ]
+        )
+        
+        insights_btn.click(
+            fn=journal.get_monthly_insights,
+            inputs=[],
+            outputs=[insights_output]
+        )
+
+    return interface
+
+# Create and launch the interface
+if __name__ == "__main__":
+    interface = create_journal_interface()
+    interface.launch()