Update app.py

app.py

@@ -1,14 +1,18 @@
+
+
+
+
+
 import whisper
 import os
 from gtts import gTTS
 import gradio as gr
 from groq import Groq
-from datetime import datetime  # Import datetime to handle timestamps
-from IPython.display import display, HTML
+from datetime import datetime
 
 # Load a smaller Whisper model for faster processing
 try:
-    model = whisper.load_model("tiny", weights_only=True)  # Set weights_only=True to address the warning
+    model = whisper.load_model("tiny")
 except Exception as e:
     print(f"Error loading Whisper model: {e}")
     model = None
@@ -89,43 +93,6 @@ def chatbot(audio):
         print(f"Error in chatbot function: {e}")
         return "Sorry, there was an error processing your request.", None, chat_history
 
-# Inject custom CSS for background and styling
-def set_background():
-    display(HTML(f'''
-        <style>
-            body {{
-                background-image: url("https://github.com/abdullahzunorain/voice-to-voice-Chatbot/blob/main/image_1");
-                background-size: cover;
-                background-position: center;
-                background-repeat: no-repeat;
-                color: white;
-                font-family: Arial, sans-serif;
-            }}
-            .gradio-container {{
-                background-color: rgba(0, 0, 0, 0.6);
-                padding: 20px;
-                border-radius: 8px;
-                box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
-            }}
-            h1, h2, p, .gradio-label {{
-                color: #FFD700;  /* Gold color for labels and titles */
-            }}
-            .gradio-button {{
-                background-color: #FFD700;
-                color: black;
-                border-radius: 4px;
-                font-weight: bold;
-            }}
-            .gradio-input {{
-                background-color: rgba(255, 255, 255, 0.9);
-                border-radius: 4px;
-            }}
-        </style>
-    '''))
-
-# Display custom background styling
-set_background() 
-
 # Gradio interface for real-time interaction with chat history display
 iface = gr.Interface(
     fn=chatbot,
@@ -137,161 +104,41 @@ iface = gr.Interface(
     live=True,
     title="Stylish Audio Chatbot with Groq API",
     description="Upload your audio, and the chatbot will transcribe and respond to it with a synthesized response.",
-    theme="default"
+    theme="default",
+    css='''
+        body {
+            background-image: url("https://raw.githubusercontent.com/username/repository/main/path/to/your-image.png");
+            background-size: cover;
+            background-position: center;
+            background-repeat: no-repeat;
+            color: white;
+            font-family: Arial, sans-serif;
+        }
+        .gradio-container {
+            background-color: rgba(0, 0, 0, 0.6);
+            padding: 20px;
+            border-radius: 8px;
+            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+        }
+        h1, h2, p, .gradio-label {
+            color: #FFD700;  /* Gold color for labels and titles */
+        }
+        .gradio-button {
+            background-color: #FFD700;
+            color: black;
+            border-radius: 4px;
+            font-weight: bold;
+        }
+        .gradio-input {
+            background-color: rgba(255, 255, 255, 0.9);
+            border-radius: 4px;
+        }
+    '''
 )
 
-# Launch the Gradio app with public sharing enabled
+# Launch the Gradio app
 if __name__ == "__main__":
-    iface.launch(share=True)
-
-
-
-
-
-#----------------------------------------------------------------------------------------------------------------------------------------------------------
-
-
-
-
-
-
-
-# import whisper
-# import os
-# from gtts import gTTS
-# import gradio as gr
-# from groq import Groq
-# from datetime import datetime
-
-# # Load a smaller Whisper model for faster processing
-# try:
-#     model = whisper.load_model("tiny")
-# except Exception as e:
-#     print(f"Error loading Whisper model: {e}")
-#     model = None
-
-# # Set up Groq API client using environment variable
-# GROQ_API_TOKEN = os.getenv("GROQ_API")
-# if not GROQ_API_TOKEN:
-#     raise ValueError("Groq API token is missing. Set 'GROQ_API' in your environment variables.")
-# client = Groq(api_key=GROQ_API_TOKEN)
-
-# # Initialize the chat history
-# chat_history = []
-
-# # Function to get the LLM response from Groq with timeout handling
-# def get_llm_response(user_input, role="detailed responder"):
-#     prompt = f"As an expert, provide a detailed and knowledgeable response: {user_input}" if role == "expert" else \
-#              f"As a good assistant, provide a clear, concise, and helpful response: {user_input}" if role == "good assistant" else \
-#              f"Provide a thorough and detailed response: {user_input}"
-
-#     try:
-#         chat_completion = client.chat.completions.create(
-#             messages=[{"role": "user", "content": user_input}],
-#             model="llama3-8b-8192",  # Replace with your desired model
-#             timeout=20  # Increased timeout to 20 seconds
-#         )
-#         return chat_completion.choices[0].message.content
-#     except Exception as e:
-#         print(f"Error during LLM response retrieval: {e}")
-#         return "Sorry, there was an error retrieving the response. Please try again."
-
-# # Function to convert text to speech using gTTS
-# def text_to_speech(text):
-#     try:
-#         tts = gTTS(text)
-#         output_audio = "output_audio.mp3"
-#         tts.save(output_audio)
-#         return output_audio
-#     except Exception as e:
-#         print(f"Error generating TTS: {e}")
-#         return None
-
-# # Main chatbot function to handle audio input and output with chat history
-# def chatbot(audio):
-#     if not model:
-#         return "Error: Whisper model is not available.", None, chat_history
-
-#     if not audio:
-#         return "No audio provided. Please upload a valid audio file.", None, chat_history
-
-#     try:
-#         # Step 1: Transcribe the audio using Whisper
-#         result = model.transcribe(audio)
-#         user_text = result.get("text", "")
-#         if not user_text.strip():
-#             return "Could not understand the audio. Please try speaking more clearly.", None, chat_history
-
-#         # Get current timestamp
-#         timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-
-#         # Display transcription in chat history
-#         chat_history.append((timestamp, "User", user_text))
-
-#         # Step 2: Get LLM response from Groq
-#         response_text = get_llm_response(user_text)
-
-#         # Step 3: Convert the response text to speech
-#         output_audio = text_to_speech(response_text)
-
-#         # Append the latest interaction to the chat history
-#         chat_history.append((timestamp, "Chatbot", response_text))
-
-#         # Format the chat history for display with timestamps and clear labels
-#         formatted_history = "\n".join([f"[{time}] {speaker}: {text}" for time, speaker, text in chat_history])
-        
-#         return formatted_history, output_audio, chat_history
-
-#     except Exception as e:
-#         print(f"Error in chatbot function: {e}")
-#         return "Sorry, there was an error processing your request.", None, chat_history
-
-# # Gradio interface for real-time interaction with chat history display
-# iface = gr.Interface(
-#     fn=chatbot,
-#     inputs=gr.Audio(type="filepath"),
-#     outputs=[
-#         gr.Textbox(label="Chat History"),  # Display chat history
-#         gr.Audio(type="filepath", label="Response Audio"),
-#     ],
-#     live=True,
-#     title="Stylish Audio Chatbot with Groq API",
-#     description="Upload your audio, and the chatbot will transcribe and respond to it with a synthesized response.",
-#     theme="default",
-#     css='''
-#         body {
-#             background-image: url("https://raw.githubusercontent.com/username/repository/main/path/to/your-image.png");
-#             background-size: cover;
-#             background-position: center;
-#             background-repeat: no-repeat;
-#             color: white;
-#             font-family: Arial, sans-serif;
-#         }
-#         .gradio-container {
-#             background-color: rgba(0, 0, 0, 0.6);
-#             padding: 20px;
-#             border-radius: 8px;
-#             box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
-#         }
-#         h1, h2, p, .gradio-label {
-#             color: #FFD700;  /* Gold color for labels and titles */
-#         }
-#         .gradio-button {
-#             background-color: #FFD700;
-#             color: black;
-#             border-radius: 4px;
-#             font-weight: bold;
-#         }
-#         .gradio-input {
-#             background-color: rgba(255, 255, 255, 0.9);
-#             border-radius: 4px;
-#         }
-#     '''
-# )
-
-# # Launch the Gradio app
-# if __name__ == "__main__":
-#     iface.launch()
+    iface.launch()