Update app.py

app.py

@@ -3,7 +3,7 @@ from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 import random
 from datetime import datetime
 
-# Initialize models (same as before)
+# Initialize models
 sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
 model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -18,8 +18,88 @@ text_generator = pipeline(
     pad_token_id=tokenizer.eos_token_id
 )
 
-# JournalCompanion class remains the same as in previous example
-# ... (insert the entire JournalCompanion class here)
+class JournalCompanion:
+    def __init__(self):
+        self.entries = []
+    
+    def generate_prompts(self, sentiment):
+        prompt_template = f"""Generate three reflective journal prompts for someone feeling {sentiment.lower()}. 
+        Make them thoughtful and encouraging. Format them as a bullet point list."""
+        
+        try:
+            response = text_generator(prompt_template)[0]['generated_text']
+            # Extract the generated prompts after the input prompt
+            prompts = response[len(prompt_template):]
+            return "\n\nReflective Prompts:" + prompts
+        except Exception as e:
+            print("Error generating prompts:", e)
+            return "\n\nReflective Prompts:\n- What thoughts and feelings are you experiencing right now?\n- How has this experience affected you?\n- What would be helpful for you at this moment?"
+
+    def generate_affirmation(self, sentiment):
+        affirmation_template = f"Generate a short, encouraging affirmation for someone feeling {sentiment.lower()}."
+        
+        try:
+            response = text_generator(affirmation_template)[0]['generated_text']
+            # Extract the generated affirmation after the input prompt
+            affirmation = response[len(affirmation_template):].strip()
+            return affirmation
+        except Exception as e:
+            print("Error generating affirmation:", e)
+            return "I acknowledge my feelings and trust in my ability to handle this moment."
+
+    def analyze_entry(self, entry_text):
+        if not entry_text.strip():
+            return ("Please write something in your journal entry.", "", "", "")
+
+        try:
+            # Perform sentiment analysis
+            sentiment_result = sentiment_analyzer(entry_text)[0]
+            sentiment = sentiment_result["label"].upper()
+            sentiment_score = sentiment_result["score"]
+        except Exception as e:
+            print("Error during sentiment analysis:", e)
+            return (
+                "An error occurred during analysis. Please try again.",
+                "Error",
+                "Could not analyze sentiment due to an error.",
+                "Could not generate affirmation due to an error."
+            )
+
+        entry_data = {
+            "text": entry_text,
+            "timestamp": datetime.now().isoformat(),
+            "sentiment": sentiment,
+            "sentiment_score": sentiment_score
+        }
+        self.entries.append(entry_data)
+
+        # Generate responses using TinyLlama
+        prompts = self.generate_prompts(sentiment)
+        affirmation = self.generate_affirmation(sentiment)
+        sentiment_percentage = f"{sentiment_score * 100:.1f}%"
+        message = f"Entry analyzed! Sentiment: {sentiment} ({sentiment_percentage} confidence)"
+        
+        return message, sentiment, prompts, affirmation
+
+    def get_monthly_insights(self):
+        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")
+        
+        try:
+            percentage_positive = (positive_entries / total_entries * 100)
+            percentage_negative = ((total_entries - positive_entries) / total_entries * 100)
+            
+            insights = f"""Monthly Insights:
+            Total Entries: {total_entries}
+            Positive Entries: {positive_entries} ({percentage_positive:.1f}%)
+            Negative Entries: {total_entries - positive_entries} ({percentage_negative:.1f}%)
+            """
+            return insights
+        except ZeroDivisionError:
+            return "No entries available for analysis."
 
 def create_journal_interface():
     journal = JournalCompanion()