modified

app.py

@@ -1,3 +1,188 @@
+# from groq import Groq
+# import gradio as gr
+# from gtts import gTTS
+# import uuid
+# import base64
+# from io import BytesIO
+# import os
+# import logging
+
+# # Set up logger
+# logger = logging.getLogger(__name__)
+# logger.setLevel(logging.DEBUG)
+# console_handler = logging.StreamHandler()
+# file_handler = logging.FileHandler('chatbot_log.log')
+# formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+# console_handler.setFormatter(formatter)
+# file_handler.setFormatter(formatter)
+# logger.addHandler(console_handler)
+# logger.addHandler(file_handler)
+
+# # Initialize Groq Client
+# #client = Groq(api_key=os.getenv("GROQ_API_KEY_2"))
+
+# client = Groq(
+#     api_key="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",
+# )
+
+
+# # Function to encode the image
+# def encode_image(uploaded_image):
+#     try:
+#         logger.debug("Encoding image...")
+#         buffered = BytesIO()
+#         uploaded_image.save(buffered, format="PNG")  # Ensure the correct format
+#         logger.debug("Image encoding complete.")
+#         return base64.b64encode(buffered.getvalue()).decode("utf-8")
+#     except Exception as e:
+#         logger.error(f"Error encoding image: {e}")
+#         raise
+
+# # Function to handle text and image inputs
+# def customLLMBot(user_input, uploaded_image, chat_history):
+#     try:
+#         logger.info("Processing input...")
+
+#         if uploaded_image is not None:
+#             # Encode the image to base64
+#             base64_image = encode_image(uploaded_image)
+#             logger.debug(f"Image received, size: {len(base64_image)} bytes")
+
+#             # Create a message specifically for image prompts
+#             messages = [
+#                 {
+#                     "role": "user",
+#                     "content": [
+#                         {"type": "text", "text": "What's in this image?"},
+#                         {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64_image}"}}]
+#                 }
+#             ]
+
+#             logger.info("Sending image to Groq API for processing...")
+#             # Send the image message to the Groq API
+#             response = client.chat.completions.create(
+#                 model="llama-3.2-11b-vision-preview",
+#                 messages=messages,
+#             )
+#             logger.info("Image processed successfully.")
+#         else:
+#             # Process text input
+#             logger.info("Processing text input...")
+#             messages = [
+#                 {"role": "system", "content": "You are Dr. HealthBuddy, a professional virtual doctor chatbot."},
+#                 {"role": "user", "content": user_input},
+#             ]
+#             response = client.chat.completions.create(
+#                 model="llama-3.2-11b-vision-preview",
+#                 messages=messages,
+#             )
+#             logger.info("Text processed successfully.")
+
+#         # Extract the reply
+#         LLM_reply = response.choices[0].message.content.strip()
+#         logger.debug(f"LLM reply: {LLM_reply}")
+
+#         # Append user input and bot reply as a tuple to the chat history
+#         chat_history.append((user_input, LLM_reply))
+
+#         return chat_history
+
+#     except Exception as e:
+#         logger.error(f"Error in customLLMBot function: {e}")
+#         return chat_history
+    
+#     # Log Viewer Function
+# def get_logs():
+#     try:
+#         with open('/tmp/chatbot_log.log', 'r') as f:
+#             return f.read()[-3000:]  # Return the last 3000 characters of the log
+#     except Exception as e:
+#         return f"Error reading logs: {e}"
+
+
+
+
+# def chatbot_ui():
+
+#     chat_history = []  # Initialize empty chat history
+
+#     with gr.Blocks() as demo:
+#         gr.Markdown("# Healthcare Chatbot Doctor")
+
+#         # Layout for chatbot and input box alignment
+#         with gr.Row():
+#             with gr.Column(scale=3):  # Main column for chatbot
+#                 chatbot = gr.Chatbot(label="Responses", elem_id="chatbot")
+#                 user_input = gr.Textbox(
+#                     label="Ask a health-related question",
+#                     placeholder="Describe your symptoms...",
+#                     elem_id="user-input",
+#                     lines=1,
+#                 )
+#             with gr.Column(scale=1):  # Side column for image and buttons
+#                 uploaded_image = gr.Image(label="Upload an Image", type="pil")
+#                 submit_btn = gr.Button("Submit")
+#                 clear_btn = gr.Button("Clear")
+#                 audio_output = gr.Audio(label="Audio Response")
+#          # Log Viewer
+#         with gr.Row():
+#             log_view = gr.Textbox(label="Logs", lines=10, interactive=False)
+#             view_logs_btn = gr.Button("View Logs")
+#             download_logs_btn = gr.Button("Download Logs")
+
+#         # Define actions
+#         def handle_submit(user_query, image):
+#             response = customLLMBot(user_query, image, chat_history)
+#             return response, None, ""  # Return chat history and reset input
+
+#         # Submit on pressing Enter key
+#         user_input.submit(
+#             handle_submit,
+#             inputs=[user_input, uploaded_image],
+#             outputs=[chatbot, audio_output, user_input],
+#         )
+
+#         # Submit on button click
+#         submit_btn.click(
+#             handle_submit,
+#             inputs=[user_input, uploaded_image],
+#             outputs=[chatbot, audio_output, user_input],
+#         )
+
+#         # Clear button action
+#         clear_btn.click(
+#             lambda: ([], None, ""),
+#             inputs=[],
+#             outputs=[chatbot, uploaded_image, user_input],
+#         )
+
+#         # View Logs Button
+#         view_logs_btn.click(
+#             get_logs,
+#             inputs=[],
+#             outputs=[log_view],
+#         )
+
+#         # Download Logs Button
+#         def download_logs():
+#             return '/tmp/chatbot_log.log'
+
+#         download_logs_btn.click(
+#             download_logs,
+#             inputs=[],
+#             outputs=[gr.File()],
+#         )
+
+#     return demo
+
+
+# # Launch the interface
+# #chatbot_ui().launch(share=True,server_name="0.0.0.0", server_port=7860)
+
+# chatbot_ui().launch(share=True,server_name="localhost", server_port=7860)
+
+
+
 from groq import Groq
 import gradio as gr
 from gtts import gTTS
@@ -21,11 +206,6 @@ logger.addHandler(file_handler)
 # Initialize Groq Client
 client = Groq(api_key=os.getenv("GROQ_API_KEY_2"))
 
-# client = Groq(
-#     api_key="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",
-# )
-
-
 # Function to encode the image
 def encode_image(uploaded_image):
     try:
@@ -43,9 +223,14 @@ def customLLMBot(user_input, uploaded_image, chat_history):
     try:
         logger.info("Processing input...")
 
+        # Append user input to the chat history
+        chat_history.append(("User", user_input))
+
         if uploaded_image is not None:
             # Encode the image to base64
             base64_image = encode_image(uploaded_image)
+
+            # Log the image size and type
             logger.debug(f"Image received, size: {len(base64_image)} bytes")
 
             # Create a message specifically for image prompts
@@ -79,24 +264,30 @@ def customLLMBot(user_input, uploaded_image, chat_history):
             logger.info("Text processed successfully.")
 
         # Extract the reply
-        LLM_reply = response.choices[0].message.content.strip()
+        LLM_reply = response.choices[0].message.content
         logger.debug(f"LLM reply: {LLM_reply}")
 
-        # Append user input and bot reply as a tuple to the chat history
-        chat_history.append((user_input, LLM_reply))
+        # Append the bot's response to the chat history
+        chat_history.append(("Bot", LLM_reply))
 
-        return chat_history
+        # Generate audio for response
+        audio_file = f"response_{uuid.uuid4().hex}.mp3"
+        tts = gTTS(LLM_reply, lang='en')
+        tts.save(audio_file)
+        logger.info(f"Audio response saved as {audio_file}")
+
+        # Return the chat history (all Q&A) and the audio file
+        return [(entry[0], entry[1]) for entry in chat_history], audio_file
 
     except Exception as e:
+        # Handle errors gracefully
         logger.error(f"Error in customLLMBot function: {e}")
-        return chat_history
-
-
+        return [("User", f"An error occurred: {e}")], None
 
 
 # Gradio Interface
 def chatbot_ui():
-    chat_history = []  # Initialize empty chat history
+    chat_history = []  # Initialize empty chat history for the session
 
     with gr.Blocks() as demo:
         gr.Markdown("# Healthcare Chatbot Doctor")
@@ -120,36 +311,8 @@ def chatbot_ui():
         # Define actions
         def handle_submit(user_query, image):
             logger.info("User submitted a query.")
-            updated_chat_history = customLLMBot(user_query, image, chat_history)
-            return updated_chat_history, None, ""  # Return the updated chat history
-
-
-def chatbot_ui():
-    chat_history = []  # Initialize empty chat history
-
-    with gr.Blocks() as demo:
-        gr.Markdown("# Healthcare Chatbot Doctor")
-
-        # Layout for chatbot and input box alignment
-        with gr.Row():
-            with gr.Column(scale=3):  # Main column for chatbot
-                chatbot = gr.Chatbot(label="Responses", elem_id="chatbot")
-                user_input = gr.Textbox(
-                    label="Ask a health-related question",
-                    placeholder="Describe your symptoms...",
-                    elem_id="user-input",
-                    lines=1,
-                )
-            with gr.Column(scale=1):  # Side column for image and buttons
-                uploaded_image = gr.Image(label="Upload an Image", type="pil")
-                submit_btn = gr.Button("Submit")
-                clear_btn = gr.Button("Clear")
-                audio_output = gr.Audio(label="Audio Response")
-
-        # Define actions
-        def handle_submit(user_query, image):
-            response = customLLMBot(user_query, image, chat_history)
-            return response, None, ""  # Return chat history and reset input
+            response, audio = customLLMBot(user_query, image, chat_history)
+            return response, audio, ""
 
         # Submit on pressing Enter key
         user_input.submit(
@@ -165,20 +328,16 @@ def chatbot_ui():
             outputs=[chatbot, audio_output, user_input],
         )
 
-        # Clear button action
-        clear_btn.click(
-            lambda: ([], None, ""),
-            inputs=[],
-            outputs=[chatbot, uploaded_image, user_input],
-        )
+        # Action for clearing all fields and resetting chat history
+        def clear_chat():
+            nonlocal chat_history
+            chat_history = []  # Reset chat history for new session
+            return [], "", None, None
+
+        clear_btn.click(clear_chat, inputs=[], outputs=[chatbot, user_input, uploaded_image, audio_output])
 
     return demo
 
 
 # Launch the interface
 chatbot_ui().launch(share=True,server_name="0.0.0.0", server_port=7860)
-
-#chatbot_ui().launch(server_name="localhost", server_port=7860)
-
-
-