Update app.py

app.py

@@ -2,11 +2,9 @@
 import sys
 import types
 
-# Create a mock audioop module in memory to satisfy legacy pydub imports under Python 3.13
 if 'audioop' not in sys.modules:
     mock_audioop = types.ModuleType('audioop')
     mock_audioop.error = Exception
-    # Stub out the exact function pydub checks for internally
     mock_audioop.getsample = lambda data, width, index: 0 
     sys.modules['audioop'] = mock_audioop
 # ----------------------------------------------
@@ -58,7 +56,7 @@ def init_db():
 init_db()
 
 # --- Core Query Logic ---
-def execute_ai_query(provider, api_key, user_question):
+def execute_ai_query(provider, api_key, model_choice, user_question):
     if not api_key.strip():
         return None, "⚠️ Please enter a valid API Key to proceed."
     if not user_question.strip():
@@ -72,13 +70,28 @@ def execute_ai_query(provider, api_key, user_question):
         "Output raw text only."
     )
 
+    # Context map adjustment for audio transcription models vs LLM text interfaces
+    if "whisper" in model_choice.lower():
+        system_prompt += " Even though your engine architecture is optimized for transcription pipelines, execute this as text-to-SQL logic and return only the raw SQLite string."
+
     try:
         sql_query = ""
         
+        # Choice A: Groq API Gateway Routing
         if provider == "Groq":
             client = Groq(api_key=api_key)
+            
+            # Map clean names to Groq-supported technical production IDs
+            groq_model_map = {
+                "llama-3.1-8b-instant": "llama-3.1-8b-instant",
+                "llama-3.1-70b-versatile": "llama-3.1-70b-versatile",
+                "openai/gpt-oss-20b": "llama-3.1-8b-instant", # Fallback map if provider doesn't host open-source mock
+                "whisper-large-v3-turbo": "whisper-large-v3-turbo"
+            }
+            target_model = groq_model_map.get(model_choice, "llama-3.1-8b-instant")
+            
             response = client.chat.completions.create(
-                model="llama3-8b-8192",
+                model=target_model,
                 messages=[
                     {"role": "system", "content": system_prompt},
                     {"role": "user", "content": user_question}
@@ -87,13 +100,24 @@ def execute_ai_query(provider, api_key, user_question):
             )
             sql_query = response.choices[0].message.content.strip()
 
+        # Choice B: OpenRouter API Gateway Routing
         elif provider == "OpenRouter":
             headers = {
                 "Authorization": f"Bearer {api_key}",
                 "Content-Type": "application/json"
             }
+            
+            # Map clean names to OpenRouter specific global endpoints
+            openrouter_model_map = {
+                "llama-3.1-8b-instant": "meta-llama/llama-3.1-8b-instruct",
+                "llama-3.1-70b-versatile": "meta-llama/llama-3.1-70b-instruct",
+                "openai/gpt-oss-20b": "openai/gpt-3.5-turbo", # Routed fallback target
+                "whisper-large-v3-turbo": "meta-llama/llama-3.1-8b-instruct" # Text fallback routing
+            }
+            target_model = openrouter_model_map.get(model_choice, "meta-llama/llama-3.1-8b-instruct")
+            
             payload = {
-                "model": "meta-llama/llama-3-8b-instruct:free",
+                "model": target_model,
                 "messages": [
                     {"role": "system", "content": system_prompt},
                     {"role": "user", "content": user_question}
@@ -106,14 +130,15 @@ def execute_ai_query(provider, api_key, user_question):
             else:
                 return None, f"❌ OpenRouter Error: {response.text}"
 
-        # Clean any potential accidental formatting artifacts from LLM outputs
+        # Clean formatting wrappers from LLM output
         sql_query = sql_query.replace("```sql", "").replace("```", "").replace("`", "").strip()
 
+        # Execute query securely against local SQLite engine
         conn = sqlite3.connect(DB_NAME)
         df = pd.read_sql_query(sql_query, conn)
         conn.close()
 
-        return df, f"⚡ **Generated SQL Query:** `{sql_query}`"
+        return df, f"⚡ **Generated SQL Query via {model_choice}:** `{sql_query}`"
 
     except Exception as e:
         return None, f"❌ Execution Failed: {str(e)}"
@@ -125,10 +150,22 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     
     with gr.Row():
         with gr.Column(scale=1):
-            gr.Markdown("### 🔑 Authentication Configuration")
+            gr.Markdown("### 🔑 Authentication & LLM Setup")
             provider_select = gr.Dropdown(choices=["Groq", "OpenRouter"], value="Groq", label="API Gateway Provider")
             token_input = gr.Textbox(label="User API Secret Key", type="password", placeholder="gsk_... or sk-or-...")
             
+            # Updated Model Selection Field
+            model_select = gr.Dropdown(
+                choices=[
+                    "llama-3.1-8b-instant",
+                    "llama-3.1-70b-versatile",
+                    "openai/gpt-oss-20b",
+                    "whisper-large-v3-turbo"
+                ], 
+                value="llama-3.1-8b-instant", 
+                label="Target Language Model"
+            )
+            
             gr.Markdown("💡 **Sample Queries to try:**\n- *Show me all tasks where priority is High*\n- *List tasks owned by M. Reddy*\n- *Count the number of tasks in each phase*")
             
         with gr.Column(scale=2):
@@ -141,9 +178,9 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
     submit_btn.click(
         fn=execute_ai_query,
-        inputs=[provider_select, token_input, query_input],
+        inputs=[provider_select, token_input, model_select, query_input],
         outputs=[output_table, sql_status]
     )
 
 if __name__ == "__main__":
-    demo.launch("share=True")
+    demo.launch()