Update app.py

app.py

@@ -7,8 +7,8 @@ from models import (
     load_question_answering_model,
     load_text_generation_model,
     load_ner_model,
-    load_text_classification_model,
-    load_text_to_sql_model
+    load_text_classification_model
+    # Removed: load_text_to_sql_model
 )
 
 # Title and Description for your app
@@ -26,7 +26,6 @@ To make powerful AI models accessible, understandable, and usable for everyone t
 • **Text Generation:** Create new text from prompts.
 • **Named Entity Recognition:** Identify people, organizations, locations, etc.
 • **Zero-Shot Classification:** Categorize text without pre-training.
-• **Text to SQL:** Convert natural language to SQL queries.
 **⚠️ Important Limitations:**  
 • **Summarization Length:** Works best with texts between **50 and 500 words**. Longer texts are automatically truncated.
 • **Processing Speed:** Hosted on free-tier CPU hardware. Processing may take 10-30 seconds.
@@ -42,7 +41,6 @@ qa_pipeline = load_question_answering_model()
 textgen_pipeline = load_text_generation_model()
 ner_pipeline = load_ner_model()
 classify_pipeline = load_text_classification_model()
-text_to_sql_pipeline = load_text_to_sql_model()
 
 def process_text(input_text, mode, context=None, candidate_labels=None):
     """The Conductor: routes input to the correct specialist model."""
@@ -142,16 +140,6 @@ def process_text(input_text, mode, context=None, candidate_labels=None):
         except Exception as e:
             return f"❌ Classification error: {str(e)}"
             
-    # Handle Text to SQL
-    elif mode == "Text to SQL":
-        try:
-            # Format the input for the SQL model
-            sql_input = f"Translate English to SQL: {input_text}"
-            result = text_to_sql_pipeline(sql_input, max_length=128)
-            return f"🗃️ **Generated SQL Query:**\n\n```sql\n{result[0]['generated_text']}\n```"
-        except Exception as e:
-            return f"❌ Text to SQL error: {str(e)}"
-            
     # Handle any other mode that might be added in the future
     else:
         return "Selected mode is not yet implemented."
@@ -172,8 +160,7 @@ with gr.Blocks(title=TITLE, css=".gradio-container {max-width: 800px; margin: au
                     "Question Answering",
                     "Text Generation",
                     "Named Entity Recognition",
-                    "Zero-Shot Classification",
-                    "Text to SQL"
+                    "Zero-Shot Classification"
                 ], 
                 label="🟰 Processing Mode", 
                 value="Sentiment Analysis"
@@ -216,8 +203,7 @@ with gr.Blocks(title=TITLE, css=".gradio-container {max-width: 800px; margin: au
             ["What is the capital of France?", "Question Answering", "France is a country in Europe. Paris is the capital city of France.", None],
             ["Once upon a time in a land far, far away", "Text Generation", None, None],
             ["Apple Inc. was founded by Steve Jobs in Cupertino, California.", "Named Entity Recognition", None, None],
-            ["The new smartphone has a great camera and long battery life", "Zero-Shot Classification", None, "technology, photography, travel"],
-            ["Find all customers from California", "Text to SQL", None, None]
+            ["The new smartphone has a great camera and long battery life", "Zero-Shot Classification", None, "technology, photography, travel"]
         ],
         inputs=[input_text, mode, context, candidate_labels],
         outputs=output_text,