Update app.py

app.py

@@ -17,14 +17,23 @@ warnings.filterwarnings('ignore', category=FutureWarning)
 # --- Monkey Patch for Gradio Client JSON Schema Bug ---
 import gradio_client.utils as client_utils
 
+# Patch get_type to check for non-dict types.
 original_get_type = client_utils.get_type
-
 def patched_get_type(schema):
     if not isinstance(schema, dict):
         return type(schema).__name__
     return original_get_type(schema)
-
 client_utils.get_type = patched_get_type
+
+# Additionally, patch _json_schema_to_python_type to handle boolean schemas.
+if not hasattr(client_utils, "_original_json_schema_to_python_type"):
+    client_utils._original_json_schema_to_python_type = client_utils._json_schema_to_python_type
+
+def patched_json_schema_to_python_type(schema, defs=None):
+    if isinstance(schema, bool):
+        return "bool"
+    return client_utils._original_json_schema_to_python_type(schema, defs)
+client_utils._json_schema_to_python_type = patched_json_schema_to_python_type
 # --- End of Monkey Patch ---
 
 # Download necessary NLTK data
@@ -34,7 +43,6 @@ nltk.download('vader_lexicon', quiet=True)
 # Backend Support for GGUF Models
 # ---------------------------
 try:
-    # Attempt to import a hypothetical llama_cpp binding for GGUF models.
     from llama_cpp import Llama
     BACKEND = "llama_cpp"
 except ImportError:
@@ -45,14 +53,12 @@ except ImportError:
 # ---------------------------
 class EmotionalAnalyzer:
     def __init__(self):
-        # Load a pre-trained emotion classifier and tokenizer
         self.emotion_model = AutoModelForSequenceClassification.from_pretrained(
             "bhadresh-savani/distilbert-base-uncased-emotion"
         )
         self.emotion_tokenizer = AutoTokenizer.from_pretrained(
             "bhadresh-savani/distilbert-base-uncased-emotion"
         )
-        # Define the emotion labels as per the model card
         self.emotion_labels = ["sadness", "joy", "love", "anger", "fear", "surprise"]
         self.sia = SentimentIntensityAnalyzer()
 
@@ -101,19 +107,16 @@ class LLMResponder:
     def __init__(self, model_name="SicariusSicariiStuff/Impish_LLAMA_3B_GGUF"):
         self.model_name = model_name
         if BACKEND == "llama_cpp":
-            # Initialize using the llama_cpp backend.
-            # Replace "path/to/your/gguf/file.gguf" with the actual path to your GGUF file.
+            # Replace with the actual path to your GGUF file.
             self.llm = Llama(model_path="path/to/your/gguf/file.gguf", n_ctx=1024)
             self.backend = "llama_cpp"
         else:
-            # Load model via Hugging Face transformers.
             self.llm_tokenizer = AutoTokenizer.from_pretrained(model_name)
             self.llm_model = AutoModelForCausalLM.from_pretrained(model_name)
             self.backend = "transformers"
 
     def generate_response(self, prompt):
         if self.backend == "llama_cpp":
-            # Use llama_cpp inference (adjust parameters as needed)
             result = self.llm(prompt=prompt, max_tokens=256, temperature=0.95, top_p=0.95)
             response = result.get("response", "")
         else:
@@ -137,9 +140,7 @@ def interactive_interface(input_text):
     emotion_analyzer = EmotionalAnalyzer()
     llm_responder = LLMResponder()
 
-    # Perform detailed emotional analysis on the input
     emotional_data = emotion_analyzer.detailed_emotional_analysis(input_text)
-    # Simulate dynamic emotional state (could be updated based on conversation history)
     current_emotions = {
         'joy': random.randint(10, 30),
         'sadness': random.randint(5, 20),
@@ -150,7 +151,6 @@ def interactive_interface(input_text):
     }
     emotion_image = emotion_analyzer.visualize_emotions(current_emotions)
 
-    # Create a prompt combining the input and detected emotion data
     prompt = (
         f"Input: {input_text}\n"
         f"Detected Emotion: {emotional_data['predicted_emotion']}\n"