Update app.py

app.py

@@ -107,6 +107,8 @@ rf_model.fit(X_train, y_train)
 
 # Isolation Forest Anomaly Detection Model (memory-efficient)
 isolation_forest = IsolationForest(contamination=0.1, random_state=42, n_jobs=-1, max_samples='auto')
+isolation_forest.fit(X_train)  # Fit the model before using it
+
 # Enhanced Emotional States
 emotions = {
     'joy': {'percentage': 10, 'motivation': 'positive', 'intensity': 0},
@@ -128,10 +130,8 @@ emotions = {
     'optimism': {'percentage': 10, 'motivation': 'hopeful', 'intensity': 0},
     'pessimism': {'percentage': 10, 'motivation': 'doubtful', 'intensity': 0},
     'boredom': {'percentage': 10, 'motivation': 'indifferent', 'intensity': 0},
-    'envy': {'percentage': 10, 'motivation': 'jealous', 'intensity': 0}
-}
-
-total_percentage = 200
+    'envy': {'percentage': 10, 'motivation':  'jealous' , 'intensity': 0},
+    total_percentage = 200
 default_percentage = total_percentage / len(emotions)
 for emotion in emotions:
     emotions[emotion]['percentage'] = default_percentage
@@ -208,12 +208,15 @@ def evolve_emotions():
         else:
             emotions[emotion]['percentage'] = best_individual[-1]
 
-# Initialize the pre-trained language model (BLOOM-1b7)
-model_name = 'bigscience/bloom-1b7'
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-lm_model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", low_cpu_mem_usage=True)
+# Lazy loading for the language model
+def get_language_model():
+    model_name = 'bigscience/bloom-1b7'
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    lm_model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", low_cpu_mem_usage=True)
+    return tokenizer, lm_model
 
 def generate_text(prompt, max_length=100):
+    tokenizer, lm_model = get_language_model()
     input_ids = tokenizer.encode(prompt, return_tensors='pt').to(lm_model.device)
     with torch.no_grad():
         output = lm_model.generate(
@@ -229,7 +232,7 @@ def generate_text(prompt, max_length=100):
     generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
     return generated_text
 
-sentiment_pipeline = pipeline("sentiment-analysis", model=model_name, tokenizer=tokenizer, device_map="auto")
+sentiment_pipeline = pipeline("sentiment-analysis", model='distilbert-base-uncased-finetuned-sst-2-english', device_map="auto")
 
 def get_sentiment(text):
     result = sentiment_pipeline(text)[0]
@@ -249,46 +252,38 @@ def get_emotional_response(context):
     
     # Weighted ensemble
     ensemble_prediction = (0.6 * nn_prediction + 0.4 * rf_prediction)
-    predicted_emotion = emotion_classes[int(round(ensemble_prediction))]
-
-    # Anomaly detection
+    predicted_emotion = emotion_classes[int(ensemble_prediction)]
+    
+    # Isolation Forest anomaly detection
     anomaly_score = isolation_forest.decision_function(context_encoded.toarray())
-    is_anomaly = anomaly_score < 0
-
-    # Calculate emotion intensity based on model confidence
-    nn_proba = torch.softmax(nn_output, dim=1).max().item()
-    rf_proba = rf_model.predict_proba(context_encoded).max()
-    intensity = (nn_proba + rf_proba) / 2 * 10  # Scale to 0-10
-
-    update_emotion(predicted_emotion, 20, intensity)
-    evolve_emotions()
-
-    emotion_history.append(emotions.copy())
-    save_historical_data(emotion_history)
-
-    response = f"Predicted Emotion: {predicted_emotion}\n"
-    response += f"Emotion Details: {emotions[predicted_emotion]}\n"
-    response += f"Anomaly Detected: {'Yes' if is_anomaly else 'No'}\n"
-    response += f"Emotion Intensity: {intensity:.2f}/10\n"
-    response += f"Current Emotional State: {json.dumps(emotions, indent=2)}"
-
-    return response
+    is_anomaly = isolation_forest.predict(context_encoded.toarray())[0] == -1
+    
+    if is_anomaly:
+        return "Anomaly detected in the emotional response."
+    
+    return f"Predicted Emotion: {predicted_emotion}"
 
+# Gradio interface
 def process_input(input_text):
     emotional_response = get_emotional_response(input_text)
-    sentiment = get_sentiment(input_text)
-    generated_text = generate_text(f"Based on the emotion analysis: {emotional_response}\nGenerate a response: {input_text}")
-    
-    return f"{emotional_response}\n\nSentiment Analysis: {sentiment}\n\nGenerated Response: {generated_text}"
+    sentiment_response = get_sentiment(input_text)
+    generated_text = generate_text(input_text)
+    return {
+        "Emotional Response": emotional_response,
+        "Sentiment Response": sentiment_response,
+        "Generated Text": generated_text
+    }
 
-# Gradio Interface
 iface = gr.Interface(
     fn=process_input,
     inputs="text",
-    outputs="text",
-    title="Advanced Emotion Analysis and Text Generation with BLOOM-1b7",
-    description="Enter a sentence for comprehensive emotion analysis, sentiment analysis, and context-aware text generation using advanced AI models."
+    outputs=[
+        gr.outputs.Textbox(label="Emotional Response"),
+        gr.outputs.Textbox(label="Sentiment Response"),
+        gr.outputs.Textbox(label="Generated Text")
+    ],
+    title="Emotion and Sentiment Analysis",
+    description="Analyze emotions and sentiments from text input."
 )
 
-if __name__ == "__main__":
-    iface.launch()
+iface.launch()