Update app.py

app.py

@@ -95,28 +95,24 @@ def find_best_fit(embeddings, model, user_query, metadata_list):
     return metadata_list[best_match_index]
 
 def create_prompt(user_query, table_metadata):
-    """Generates a prompt for the AI model."""
-    system_prompt = """
-    You are a SQL query generator specialized in generating SQL queries for a single table at a time. 
-    Your task is to accurately convert natural language queries into SQL statements based on the user's intent and the provided table metadata.
-    
-    Rules:
-    - Assume all queries relate to a single table provided in the metadata. Ignore references to other tables.
-    - Ensure the generated query matches the table name, columns, and data types in the metadata.
-    - Capture filters, sorting, or aggregations as per user intent.
-    - Use standard SQL syntax.
-    
-    Input:
-    User Query: {user_query}
-    Table Metadata: {table_metadata}
-    
-    Output:
-    - Provide only the SQL query in a single line. No extra words.
+    """Generates a direct and structured SQL prompt."""
+    system_prompt = f"""
+    You are an AI SQL query generator. Your job is to convert natural language questions into precise SQL queries. 
+
+    **Table Information:**
+    {table_metadata}
+
+    **User Question:** 
+    {user_query}
+
+    **Output:**
+    Provide ONLY the SQL query. Do not add any explanation, comments, or extra text.
     """
     return system_prompt
 
+
 def generate_sql_query(system_prompt):
-    """Uses Groq API to generate an SQL query and prints debugging info."""
+    """Uses Groq API to generate an SQL query with better debugging."""
     try:
         client = Groq(api_key=groq_api_key)
         chat_completion = client.chat.completions.create(
@@ -124,11 +120,14 @@ def generate_sql_query(system_prompt):
             model="llama3-70b-8192"
         )
 
-        # Extract response
+        # Debug: Print entire response
+        print("🔍 Full API Response:", chat_completion)
+
+        # Extract AI response
         result = chat_completion.choices[0].message.content.strip()
         print(f"✅ AI Response: {result}")  # Debugging
 
-        # Check if result is a valid SQL query
+        # Check if the response starts with "SELECT"
         if result.lower().startswith("select"):
             return result
         else:
@@ -139,6 +138,7 @@ def generate_sql_query(system_prompt):
         print(f"❌ API Error: {e}")
         return "⚠️ API failed. Check logs."
 
+
 def response(user_query, dataset_folder):
     """Processes the user query and returns an SQL query."""
     dataframes, metadata_list = load_dataset_metadata(dataset_folder)
@@ -163,4 +163,4 @@ iface = gr.Interface(
 
 # Run Gradio app
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()