Update app.py

app.py

@@ -1,3 +1,27 @@
+import warnings
+# Suppress FutureWarnings
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+# --- Monkey Patch for Gradio's Schema Parsing ---
+# This patch prevents errors when a boolean appears in a schema where an iterable is expected.
+try:
+    import gradio_client.utils as client_utils
+    original_get_type = client_utils.get_type
+
+    def patched_get_type(schema):
+        # If schema is a bool, simply return a generic type string.
+        if isinstance(schema, bool):
+            return "Any"
+        if not isinstance(schema, dict):
+            return "Any"
+        # Otherwise, call the original function.
+        return original_get_type(schema)
+
+    client_utils.get_type = patched_get_type
+except Exception as e:
+    # If the patch fails for some reason, log the error and continue.
+    print("Warning: Failed to patch gradio_client.utils.get_type:", e)
+
 import random
 import pandas as pd
 import seaborn as sns
@@ -6,16 +30,14 @@ import nltk
 import gradio as gr
 from nltk.sentiment import SentimentIntensityAnalyzer
 from textblob import TextBlob
-import warnings
 from transformers import (
     AutoTokenizer,
     AutoModelForCausalLM,
     AutoModelForSequenceClassification,
 )
 
-# Suppress warnings and fix Gradio schema bug
-warnings.filterwarnings('ignore', category=FutureWarning)
-nltk.download('vader_lexicon', quiet=True)
+# Download necessary NLTK data
+nltk.download("vader_lexicon", quiet=True)
 
 # --- Emotion Analyzer ---
 class EmotionalAnalyzer:
@@ -84,9 +106,10 @@ class EmotionalAnalyzer:
             plt.close()
             return path
         except Exception:
-            return None  # Ensures that if there's an issue, we return None
+            return None
 
 # --- Text Completion LLM ---
+# Load the fine-tuned LLaMA model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("diabolic6045/ELN-Llama-1B-base")
 model = AutoModelForCausalLM.from_pretrained("diabolic6045/ELN-Llama-1B-base")
 
@@ -171,4 +194,5 @@ with gr.Blocks(title="ELN LLaMA 1B Enhanced Demo") as app:
         comp_button = gr.Button("Complete Text")
         comp_button.click(generate_completion, inputs=[comp_text, comp_temp, comp_len], outputs=comp_output)
 
+# Launch the Gradio app (remove share=True if not needed)
 app.launch(share=True)