Update app.py

app.py

@@ -1,51 +1,53 @@
 import gradio as gr
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+from transformers import pipeline
 import random
 from datetime import datetime
 
-# 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)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-text_generator = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    max_new_tokens=50,
-    temperature=0.7,
-    top_p=0.9,
-    pad_token_id=tokenizer.eos_token_id
+# Initialize models with smaller, faster alternatives
+sentiment_analyzer = pipeline(
+    "sentiment-analysis",
+    model="distilbert-base-uncased-finetuned-sst-2-english",
+    device=-1  # Force CPU usage
 )
 
+# Pre-defined prompts and affirmations for different sentiments
+PROMPT_TEMPLATES = {
+    "POSITIVE": [
+        "- What made this positive experience particularly meaningful to you?",
+        "- How can you carry this positive energy forward?",
+        "- Who would you like to share this joy with and why?"
+    ],
+    "NEGATIVE": [
+        "- What can you learn from this challenging situation?",
+        "- What small step could you take to feel better?",
+        "- Who or what helps you feel supported during difficult times?"
+    ]
+}
+
+AFFIRMATIONS = {
+    "POSITIVE": [
+        "I deserve this joy and all good things coming my way.",
+        "My positive energy creates positive experiences.",
+        "I choose to embrace and celebrate this moment."
+    ],
+    "NEGATIVE": [
+        "This too shall pass, and I am growing stronger.",
+        "I trust in my ability to handle challenging situations.",
+        "Every experience is teaching me something valuable."
+    ]
+}
+
 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 get_prompts(self, sentiment):
+        prompts = PROMPT_TEMPLATES.get(sentiment, PROMPT_TEMPLATES["POSITIVE"])
+        return "\n\nReflective Prompts:\n" + "\n".join(prompts)
 
-    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 get_affirmation(self, sentiment):
+        affirmations = AFFIRMATIONS.get(sentiment, AFFIRMATIONS["POSITIVE"])
+        return random.choice(affirmations)
 
     def analyze_entry(self, entry_text):
         if not entry_text.strip():
@@ -73,9 +75,9 @@ class JournalCompanion:
         }
         self.entries.append(entry_data)
 
-        # Generate responses using TinyLlama
-        prompts = self.generate_prompts(sentiment)
-        affirmation = self.generate_affirmation(sentiment)
+        # Get pre-defined responses
+        prompts = self.get_prompts(sentiment)
+        affirmation = self.get_affirmation(sentiment)
         sentiment_percentage = f"{sentiment_score * 100:.1f}%"
         message = f"Entry analyzed! Sentiment: {sentiment} ({sentiment_percentage} confidence)"
         
@@ -101,6 +103,8 @@ class JournalCompanion:
         except ZeroDivisionError:
             return "No entries available for analysis."
 
+# Rest of the code (create_journal_interface function and CSS) remains the samepad_token_id=tokenizer.eos_token_id
+)
 def create_journal_interface():
     journal = JournalCompanion()