Update app.py

app.py

@@ -1,35 +1,25 @@
 import os
-import re
-import requests
-import json
 import gradio as gr
-
-# Google Gemini imports
+import requests
 import google.generativeai as genai
 
-# LangChain imports
 from langchain_core.prompts import PromptTemplate
-from langchain_classic.chains import LLMChain
 from langchain_classic.memory import ConversationBufferMemory
 
-# -----------------------------
-# READ API KEYS FROM ENVIRONMENT
-# -----------------------------
+# ------------------------------------
+# Load API keys from environment
+# ------------------------------------
 GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
 ELEVENLABS_API_KEY = os.environ.get("ELEVENLABS_API_KEY")
-
-# ElevenLabs Voice ID
 ELEVENLABS_VOICE_ID = "21m00Tcm4TlvDq8ikWAM"
 
 # Configure Gemini
 genai.configure(api_key=GEMINI_API_KEY)
 
-print("✅ API keys loaded safely from environment!")
-
-# -----------------------------
-# PROMPT + MEMORY
-# -----------------------------
-template = """You are a helpful assistant to answer user queries.
+# ------------------------------------
+# Prompt + Memory
+# ------------------------------------
+template = """You are a helpful assistant.
 {chat_history}
 User: {user_message}
 Chatbot:"""
@@ -41,12 +31,10 @@ prompt = PromptTemplate(
 
 memory = ConversationBufferMemory(memory_key="chat_history")
 
-print("✅ Prompt template created!")
-
-# -----------------------------
-# CUSTOM GEMINI WRAPPER
-# -----------------------------
-gemini_model = genai.GenerativeModel('gemini-2.5-flash')
+# ------------------------------------
+# Gemini Wrapper
+# ------------------------------------
+gemini_model = genai.GenerativeModel("gemini-2.5-flash")
 
 class GeminiLLM:
     def __init__(self, model):
@@ -56,17 +44,15 @@ class GeminiLLM:
     def predict(self, user_message):
         full_prompt = "You are a helpful assistant.\n"
         for msg in self.memory_history:
-            full_prompt += f"{msg}\n"
+            full_prompt += msg + "\n"
         full_prompt += f"User: {user_message}\nChatbot:"
 
         response = self.model.generate_content(full_prompt)
         answer = response.text
 
-        # Add to memory
         self.memory_history.append(f"User: {user_message}")
         self.memory_history.append(f"Chatbot: {answer}")
 
-        # Keep last 20 messages
         if len(self.memory_history) > 20:
             self.memory_history = self.memory_history[-20:]
 
@@ -74,76 +60,47 @@ class GeminiLLM:
 
 llm_chain = GeminiLLM(gemini_model)
 
-print("✅ Gemini wrapper initialized!")
-
-# -----------------------------
-# ELEVENLABS AUDIO FUNCTION
-# -----------------------------
+# ------------------------------------
+# ElevenLabs Audio (Hugging Face friendly)
+# ------------------------------------
 def generate_audio_elevenlabs(text):
     from elevenlabs.client import ElevenLabs
     from elevenlabs import save
 
     try:
         client = ElevenLabs(api_key=ELEVENLABS_API_KEY)
-
         audio = client.generate(
             text=text,
             voice=ELEVENLABS_VOICE_ID,
             model="eleven_monolingual_v1"
         )
 
-        output_path = f"/tmp/output_{abs(hash(text)) % 100000}.mp3"
+        output_path = f"/tmp/audio_{abs(hash(text)) % 100000}.mp3"
         save(audio, output_path)
-
-        return {
-            "type": "SUCCESS",
-            "response": output_path
-        }
+        return output_path
 
     except Exception as e:
-        return {
-            "type": "ERROR",
-            "response": str(e)
-        }
-
-print("✅ ElevenLabs audio function ready!")
-
-# -----------------------------
-# TEXT RESPONSE
-# -----------------------------
-def get_text_response(user_message):
-    try:
-        return llm_chain.predict(user_message=user_message)
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# -----------------------------
-# COMBINED RESPONSE
-# -----------------------------
-def get_text_response_and_audio_response(user_message):
-    text_response = get_text_response(user_message)
-    audio_reply = generate_audio_elevenlabs(text_response)
-
-    return {
-        "text": text_response,
-        "audio_path": audio_reply.get("response", "")
-    }
-
-# -----------------------------
-# MAIN CHATBOT RESPONSE HANDLER
-# -----------------------------
+        print("Audio error:", e)
+        return ""
+
+# ------------------------------------
+# Combined response
+# ------------------------------------
+def get_response_and_audio(message):
+    text = llm_chain.predict(message)
+    audio_path = generate_audio_elevenlabs(text)
+    return text, audio_path
+
+# ------------------------------------
+# Gradio ChatHandler (UI unchanged)
+# ------------------------------------
 def chat_bot_response(message, history):
-    try:
-        result = get_text_response_and_audio_response(message)
-        return result["text"]
-    except Exception as e:
-        return f"Error: {str(e)}"
+    text, audio_path = get_response_and_audio(message)
+    return text
 
-print("✅ Chatbot handler ready!")
-
-# -----------------------------
-# UI (UNCHANGED)
-# -----------------------------
+# ------------------------------------
+# UI (same as your original)
+# ------------------------------------
 demo = gr.ChatInterface(
     fn=chat_bot_response,
     title="🤖 Gemini + ElevenLabs Chatbot",
@@ -158,7 +115,5 @@ demo = gr.ChatInterface(
     theme=gr.themes.Soft()
 )
 
-print("✅ Gradio interface created!")
-
 if __name__ == "__main__":
     demo.launch(debug=True, share=True)