Update app.py

app.py

@@ -135,8 +135,7 @@ emotions = {
 total_percentage = 200
 default_percentage = total_percentage / len(emotions)
 for emotion in emotions:
-    emotions[emotion]['percentage'] = default_percentage
-    emotion_history_file = 'emotion_history.json'
+    emotions[emotion]['emotion_history_file = 'emotion_history.json'
 
 def load_historical_data(file_path=emotion_history_file):
     if os.path.exists(file_path):
@@ -203,6 +202,71 @@ def evolve_emotions():
 
     emotions['ideal_state']['percentage'] = ideal_state
 
+# Lazy loading for the language models
+_bloom_tokenizer = None
+_bloom_lm_model = None
+def get_bloom_model():
+    global _bloom_tokenizer, _bloom_lm_model
+    if _bloom_tokenizer is None or _bloom_lm_model is None:
+        bloom_model_name = 'bigscience/bloom-1b7'
+        _bloom_tokenizer = AutoTokenizer.from_pretrained(bloom_model_name)
+        _bloom_lm_model = AutoModelForCausalLM.from_pretrained(bloom_model_name, device_map="auto", low_cpu_mem_usage=True)
+    return _bloom_tokenizer, _bloom_lm_model
+
+_gpt_tokenizer = None
+_gpt_lm_model = None
+def get_gpt_model():
+    global _gpt_tokenizer, _gpt_lm_model
+    if _gpt_tokenizer is None or _gpt_lm_model is None:
+        gpt_model_name = 'gpt2-medium'
+        _gpt_tokenizer = AutoTokenizer.from_pretrained(gpt_model_name)
+        _gpt_lm_model = AutoModelForCausalLM.from_pretrained(gpt_model_name, device_map="auto", low_cpu_mem_usage=True)
+    return _gpt_tokenizer, _gpt_lm_model
+
+def generate_text(prompt, max_length=100, model_type='bloom'):
+    if model_type == 'bloom':
+        bloom_tokenizer, bloom_lm_model = get_bloom_model()
+        input_ids = bloom_tokenizer.encode(prompt, return_tensors='pt').to(bloom_lm_model.device)
+        with torch.no_grad():
+            output = bloom_lm_model.generate(
+                input_ids,
+                max_length=max_length,
+                num_return_sequences=1,
+                no_repeat_ngram_size=2,
+                do_sample=True,
+                top_k=50,
+                top_p=0.95,
+                temperature=0.7
+            )
+        generated_text = bloom_tokenizer.decode(output[0], skip_special_tokens=True)
+    elif model_type == 'gpt':
+        gpt_tokenizer, gpt_lm_model = get_gpt_model()
+        input_ids = gpt_tokenizer.encode(prompt, return_tensors='pt').to(gpt_lm_model.device)
+        with torch.no_grad():
+            output = gpt_lm_model.generate(
+                input_ids,
+                max_length=max_length,
+                num_return_sequences=1,
+                no_repeat_ngram_size=2,
+                do_sample=True,
+                top_k=50,
+                top_p=0.95,
+                temperature=0.7
+            )
+        generated_text = gpt_tokenizer.decode(output[0], skip_special_tokens=True)
+    else:
+        raise ValueError("Invalid model type. Choose 'bloom' or 'gpt'.")
+
+    return generated_text
+
+model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+def get_sentiment(text):
+    result = sentiment_pipeline(text)[0]
+    return f"Sentiment: {result['label']}, Score: {result['score']:.4f}"
+
 def process_input(text):
     try:
         normalized_text = normalize_context(text)
@@ -214,23 +278,25 @@ def process_input(text):
         
         predicted_emotion = emotion_classes[rf_prediction]
         sentiment_score = isolation_score
-        generated_text = emotion_classes[nn_prediction]
+        bloom_generated_text = generate_text(normalized_text, model_type='bloom')
+        gpt_generated_text = generate_text(normalized_text, model_type='gpt')
 
         historical_data = load_historical_data()
         historical_data.append({
             'context': text,
             'predicted_emotion': predicted_emotion,
             'sentiment_score': sentiment_score,
-            'generated_text': generated_text
+            'bloom_generated_text': bloom_generated_text,
+            'gpt_generated_text': gpt_generated_text
         })
         save_historical_data(historical_data)
 
-        return predicted_emotion, sentiment_score, generated_text
+        return predicted_emotion, sentiment_score, bloom_generated_text, gpt_generated_text
 
     except Exception as e:
         error_message = f"An error occurred: {str(e)}"
         print(error_message)  # Logging the error
-        return error_message, error_message, error_message
+        return error_message, error_message, error_message, error_message
 
 iface = gr.Interface(
     fn=process_input,
@@ -238,9 +304,10 @@ iface = gr.Interface(
     outputs=[
         gr.Textbox(label="Emotional Response"),
         gr.Textbox(label="Sentiment Response"),
-        gr.Textbox(label="Generated Text")
+        gr.Textbox(label="BLOOM Generated Text"),
+        gr.Textbox(label="GPT Generated Text")
     ],
     live=True
 )
 
-iface.launch(share=True)
+iface.launch(share=True)