Update app.py

app.py

@@ -3,11 +3,23 @@ import sqlite3
 import requests
 import openai
 import gradio as gr
+import asyncio
+from langgraph import Graph, FunctionNode, RouterNode
+from gtts import gTTS
+
+def stt_agent(audio_path: str) -> str:
+    """Convert speech to text using OpenAI Whisper API"""
+    with open(audio_path, "rb") as afile:
+        transcript = openai.audio.transcriptions.create(
+            model="whisper-1",
+            file=afile
+        )
+    return transcript.text.strip()
 
 # Load API keys from environment
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
-# --- Agents ---
+# --- Business Logic Functions ---
 def db_agent(query: str) -> str:
     try:
         conn = sqlite3.connect("shop.db")
@@ -29,65 +41,99 @@ def db_agent(query: str) -> str:
             return "No transactions found for today."
         return None
     except sqlite3.OperationalError as e:
-        return f"Database error: {e}. Please initialize 'transactions' table in shop.db."
-
+        return f"Database error: {e}. Please initialize 'transactions' table in shop.db."  
 
 def web_search_agent(query: str) -> str:
-    # Try fetching a snippet from SerpAPI, otherwise fallback to direct LLM
     try:
         resp = requests.get(
             "https://serpapi.com/search",
             params={"q": query, "api_key": os.getenv("SERPAPI_KEY")}  
         )
-        data = resp.json()
-        snippet = data.get("organic_results", [{}])[0].get("snippet", "").strip()
+        snippet = resp.json().get("organic_results", [{}])[0].get("snippet", "").strip()
         if snippet:
             return llm_agent(f"Summarize: {snippet}")
     except Exception:
         pass
-    # Fallback for no snippet or errors
     return llm_agent(query)
 
-
-def llm_agent(prompt: str) -> str:
-    # Updated for openai>=1.0.0 interface
+def llm_agent(query: str) -> str:
     response = openai.chat.completions.create(
         model="gpt-4o-mini",
         messages=[
             {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": prompt},
+            {"role": "user", "content": query},
         ],
         temperature=0.2,
     )
     return response.choices[0].message.content.strip()
 
+# Text-to-Speech
+
+def tts_agent(text: str, lang: str = 'en') -> str:
+    """Convert text to speech mp3 and return filepath"""
+    tts = gTTS(text=text, lang=lang)
+    out_path = "response_audio.mp3"
+    tts.save(out_path)
+    return out_path
+
+# --- LangGraph Multi-Agent Setup ---
+router_node = RouterNode(
+    name="router",
+    routes=[
+        (lambda q: any(k in q.lower() for k in ["max revenue", "revenue"]), "db"),
+        (lambda q: any(k in q.lower() for k in ["who", "what", "when", "where"]), "web"),
+        (lambda q: True, "llm"),
+    ]
+)
+
+db_node = FunctionNode(func=db_agent, name="db")
+web_node = FunctionNode(func=web_search_agent, name="web")
+llm_node = FunctionNode(func=llm_agent, name="llm")
+
+# Build Graph
+graph = Graph("shop-assistant")
+graph.add_nodes([router_node, db_node, web_node, llm_node])
+graph.add_edge("router", "db", condition=lambda r: r == "db")
+graph.add_edge("router", "web", condition=lambda r: r == "web")
+graph.add_edge("router", "llm", condition=lambda r: r == "llm")
+
+async def graph_handler(query: str) -> str:
+    # If audio file path passed, convert to text first
+    if query.startswith("audio://"):
+        audio_path = query.replace("audio://", "")
+        query = stt_agent(audio_path)
+    text_resp = await graph.run(input=query, start_node="router")
+    return text_resp
 
-def handle_query(query: str) -> str:
-    q = query.lower()
-    if any(k in q for k in ["max", "revenue", "today", "product"]):
-        return db_agent(query)
-    elif any(k in q for k in ["who", "what", "when", "where"]):
-        return web_search_agent(query)
-    else:
-        return llm_agent(query)
+def handle_query(audio_or_text: str):
+    # Determine output type
+    is_audio = audio_or_text.endswith('.wav') or audio_or_text.endswith('.mp3')
+    text_input = f"audio://{audio_or_text}" if is_audio else audio_or_text
+    text_resp = asyncio.run(graph_handler(text_input))
+    if is_audio:
+        # Return both text and audio
+        audio_path = tts_agent(text_resp)
+        return text_resp, audio_path
+    return text_resp
 
 # --- Gradio UI ---
 with gr.Blocks() as demo:
-    gr.Markdown("## Shop Voice-Box Assistant")
-    user_input = gr.Textbox(placeholder="Type your question here...", lines=2)
-    submit_btn = gr.Button("Submit")
-    response_box = gr.Textbox(label="Answer")
-    # Sample questions
+    gr.Markdown("## Shop Voice-Box Assistant (Speech In/Out)")
+    inp = gr.Audio(source="microphone", type="filepath", label="Speak or type your question")
+    out_text = gr.Textbox(label="Answer (text)")
+    out_audio = gr.Audio(label="Answer (speech)")
+    submit = gr.Button("Submit")
+    # Examples
     gr.Examples(
         examples=[
             ["What is the max revenue product today?"],
             ["Who invented the light bulb?"],
             ["Tell me a joke about cats."],
         ],
-        inputs=user_input,
-        outputs=response_box,
+        inputs=inp,
+        outputs=[out_text, out_audio],
     )
-    submit_btn.click(fn=handle_query, inputs=user_input, outputs=response_box)
+    submit.click(fn=handle_query, inputs=inp, outputs=[out_text, out_audio])
 
 if __name__ == "__main__":
     demo.launch(share=False, server_name="0.0.0.0", server_port=7860)