Update app.py

app.py

@@ -2,25 +2,36 @@ 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.
+# --- Monkey Patch for Gradio Schema Parsing --- 
+# This patch prevents APIInfoParseError by handling boolean schema values.
 try:
     import gradio_client.utils as client_utils
+
+    # Patch the helper function to handle bool types in the schema.
+    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):
+            # If the schema is a boolean, simply return a generic type.
+            return "Any"
+        return original_json_schema_to_python_type(schema, defs)
+
+    client_utils._json_schema_to_python_type = patched_json_schema_to_python_type
+
+    # Also patch get_type to be extra safe.
     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)
+    print("Warning: Failed to patch gradio_client schema utils:", e)
 
 import random
 import pandas as pd
@@ -30,6 +41,7 @@ import nltk
 import gradio as gr
 from nltk.sentiment import SentimentIntensityAnalyzer
 from textblob import TextBlob
+import torch
 from transformers import (
     AutoTokenizer,
     AutoModelForCausalLM,
@@ -106,10 +118,9 @@ class EmotionalAnalyzer:
             plt.close()
             return path
         except Exception:
-            return None
+            return None  # Ensures that if there's an issue, we 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")
 
@@ -194,5 +205,5 @@ 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)
+# Launch the Gradio app (remove share=True if running in an environment that doesn't support it)
 app.launch(share=True)