Update app.py

app.py

@@ -155,51 +155,68 @@ def evolve_emotions():
 def predict_emotion(context):
     emotion_prediction_pipeline = pipeline('text-classification', model=emotion_prediction_model, tokenizer=emotion_prediction_tokenizer, top_k=None)
     predictions = emotion_prediction_pipeline(context)
-    emotion_scores = predictions[0]
-    emotion_pred = emotion_classes[np.argmax(emotion_scores)]
+    emotion_scores = {emotion['label']: emotion['score'] for emotion in predictions}
+    emotion_pred = emotion_classes[np.argmax([emotion_scores.get(label, 0) for label in emotion_classes])]
     return emotion_pred
 
-def generate_text(prompt, max_length=100, emotion=None):
-    finetuned_lm_tokenizer, finetuned_lm_model = get_finetuned_lm_model()
-    input_ids = finetuned_lm_tokenizer.encode(prompt, return_tensors='pt').to(finetuned_lm_model.device)
-
-    if emotion is not None:
-        emotion_intensity = emotions[emotion]['intensity']
-        top_p = 0.95 - (emotion_intensity / 10)  # Adjust top_p based on emotion intensity
-        temperature = 0.7 + (emotion_intensity / 5)  # Adjust temperature based on emotion intensity
-    else:
-        top_p = 0.95
-        temperature = 0.7
-
-    with torch.no_grad():
-        output = finetuned_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=top_p,
-            temperature=temperature
-        )
-    generated_text = finetuned_lm_tokenizer.decode(output[0], skip_special_tokens=True)
-    return generated_text
-
-def generate_response(context, emotion=None):
-    prompt = context
-    generated_text = generate_text(prompt, emotion=emotion)
-    return generated_text
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Emotion-Aware Language Model")
-    
-    context_input = gr.Textbox(label="Enter a context")
-    predict_btn = gr.Button("Predict Emotion and Generate Text")
+def generate_text(prompt, model, tokenizer, max_length=50):
+    input_ids = tokenizer.encode(prompt, return_tensors='pt')
+    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+def optimize_ai_model(emotion_history):
+    X = [normalize_context(e['context']) for e in emotion_history]
+    y = [e['emotion'] for e in emotion_history]
     
-    with gr.Row():
-        emotion_output = gr.Textbox(label="Predicted Emotion", show_label=True)
-        generated_text_output = gr.Textbox(label="Generated Text", show_label=True)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    rf = RandomForestClassifier(n_estimators=100, random_state=42)
+    rf.fit(X_train, y_train)
+    score = rf.score(X_test, y_test)
+    return rf, score
+
+def ai_response(input_text, emotion_model):
+    emotion_pred = predict_emotion(input_text)
+    context_emotion = {'context': input_text, 'emotion': emotion_pred}
+    emotion_history.append(context_emotion)
+    save_historical_data(emotion_history)
+
+    percentage_change = random.randint(1, 10)
+    intensity_change = random.randint(1, 10)
+    update_emotion(emotion_pred, percentage_change, intensity_change)
+
+    evolve_emotions()
     
-    predict_btn.click(fn=lambda context: (predict_emotion(context), generate_response(context, emotion=predict_emotion(context))), inputs=context_input, outputs=[emotion_output, generated_text_output])
+    finetuned_tokenizer, finetuned_model = get_finetuned_lm_model()
+    response_text = generate_text(input_text, finetuned_model, finetuned_tokenizer)
+
+    return response_text
+
+def run_genetic_algorithm_parallel():
+    num_cores = mp.cpu_count()
+    results = Parallel(n_jobs=num_cores)(delayed(evolve_emotions)() for _ in range(3))
+
+def clear_emotion_history():
+    global emotion_history
+    emotion_history = []
+    save_historical_data(emotion_history)
+
+# Gradio Interface
+def respond(input_text):
+    emotion_model, score = optimize_ai_model(emotion_history)
+    response = ai_response(input_text, emotion_model)
+    return response, score
+
+with gr.Blocks() as app:
+    gr.Markdown("# Advanced AI Model")
+    input_text = gr.Textbox(label="Input Text")
+    response_text = gr.Textbox(label="Response Text", interactive=False)
+    model_score = gr.Number(label="Model Accuracy", interactive=False)
+
+    respond_button = gr.Button("Respond")
+    respond_button.click(fn=respond, inputs=[input_text], outputs=[response_text, model_score])
+
+    clear_button = gr.Button("Clear Emotion History")
+    clear_button.click(fn=clear_emotion_history, inputs=[], outputs=[])
 
-demo.launch()
+app.launch()