Update app.py

app.py

@@ -1,161 +1,3 @@
-import whisper
-import os
-from gtts import gTTS
-import gradio as gr
-from groq import Groq
-from datetime import datetime
-import tempfile
-
-# 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 and handle temporary files
-def text_to_speech(text):
-    try:
-        tts = gTTS(text)
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_file:
-            output_audio = temp_file.name
-            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", lines=10, interactive=False),  # Display chat history
-        gr.Audio(type="filepath", label="Response Audio"),
-    ],
-    live=True,
-    title="Voice to Voice Chatbot",
-    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://huggingface.co/spaces/abdullahzunorain/voice-to-voice-Chatbot/resolve/main/BG_1.jpg");
-            background-size: cover;
-            background-position: center;
-            background-repeat: no-repeat;
-            color: white;
-            font-family: 'Helvetica Neue', sans-serif;
-        }
-        .gradio-container {
-            background-color: rgba(0, 0, 0, 0.7);
-            padding: 20px;
-            border-radius: 10px;
-            box-shadow: 0 4px 20px rgba(0, 0, 0, 0.5);
-        }
-        h1, h2, p, .gradio-label {
-            color: #FFD700;  /* Gold color for labels and titles */
-            text-align: center;
-        }
-        .gradio-button {
-            background-color: #FFD700;
-            color: black;
-            border-radius: 5px;
-            font-weight: bold;
-            transition: background-color 0.3s, transform 0.2s;
-        }
-        .gradio-button:hover {
-            background-color: #FFC107;  /* Lighter gold on hover */
-            transform: scale(1.05);
-        }
-        .gradio-input {
-            background-color: rgba(255, 255, 255, 0.9);
-            border-radius: 4px;
-            border: 2px solid #FFD700; /* Gold border */
-        }
-        .gradio-audio {
-            border: 2px solid #FFD700; /* Gold border for audio */
-        }
-    '''
-)
-
-# Launch the Gradio app
-if __name__ == "__main__":
-    iface.launch()
-
-
-
-
-
-
-
-
-
-
 # import whisper
 # import os
 # from gtts import gTTS
@@ -253,7 +95,7 @@ if __name__ == "__main__":
 #     fn=chatbot,
 #     inputs=gr.Audio(type="filepath"),
 #     outputs=[
-#         gr.Textbox(label="Chat History"),  # Display chat history
+#         gr.Textbox(label="Chat History", lines=10, interactive=False),  # Display chat history
 #         gr.Audio(type="filepath", label="Response Audio"),
 #     ],
 #     live=True,
@@ -267,27 +109,37 @@ if __name__ == "__main__":
 #             background-position: center;
 #             background-repeat: no-repeat;
 #             color: white;
-#             font-family: Arial, sans-serif;
+#             font-family: 'Helvetica Neue', sans-serif;
+#         }
+#         .gradio-container {
+#             background-color: rgba(0, 0, 0, 0.7);
+#             padding: 20px;
+#             border-radius: 10px;
+#             box-shadow: 0 4px 20px rgba(0, 0, 0, 0.5);
+#         }
+#         h1, h2, p, .gradio-label {
+#             color: #FFD700;  /* Gold color for labels and titles */
+#             text-align: center;
+#         }
+#         .gradio-button {
+#             background-color: #FFD700;
+#             color: black;
+#             border-radius: 5px;
+#             font-weight: bold;
+#             transition: background-color 0.3s, transform 0.2s;
+#         }
+#         .gradio-button:hover {
+#             background-color: #FFC107;  /* Lighter gold on hover */
+#             transform: scale(1.05);
+#         }
+#         .gradio-input {
+#             background-color: rgba(255, 255, 255, 0.9);
+#             border-radius: 4px;
+#             border: 2px solid #FFD700; /* Gold border */
+#         }
+#         .gradio-audio {
+#             border: 2px solid #FFD700; /* Gold border for audio */
 #         }
-#         # .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;
-#         # }
 #     '''
 # )
 
@@ -296,3 +148,151 @@ if __name__ == "__main__":
 #     iface.launch()
 
 
+
+
+
+
+
+
+
+
+import whisper
+import os
+from gtts import gTTS
+import gradio as gr
+from groq import Groq
+from datetime import datetime
+import tempfile
+
+# 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 and handle temporary files
+def text_to_speech(text):
+    try:
+        tts = gTTS(text)
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_file:
+            output_audio = temp_file.name
+            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="Voice to Voice Chatbot",
+    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://huggingface.co/spaces/abdullahzunorain/voice-to-voice-Chatbot/resolve/main/BG_1.jpg");
+            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()
+
+