project added

app.py

@@ -1,268 +1,4 @@
-# from groq import Groq
-# import gradio as gr
-# from gtts import gTTS
-# import uuid
-# import base64
-# from io import BytesIO
-# import os
-# import logging
-# import spacy
-# from transformers import pipeline
-
-# # 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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",
-#  )
-
-# # Initialize spaCy NLP model for named entity recognition (NER)
-# nlp = spacy.load("en_core_web_sm")  
-
-# # Initialize sentiment analysis model using Hugging Face
-# sentiment_analyzer = pipeline("sentiment-analysis")
-
-# # Function to preprocess user input for better NLP understanding
-# def preprocess_input(user_input):
-#     # Clean up text (remove unnecessary characters, standardize)
-#     user_input = user_input.strip().lower()
-#     return user_input
-
-# # Function for sentiment analysis (optional)
-# def analyze_sentiment(user_input):
-#     result = sentiment_analyzer(user_input)
-#     return result[0]['label']  # Positive, Negative, or Neutral
-
-# # Function to extract medical entities from input using NER
-
-# symptoms = [
-#     "fever", "cough", "headache", "nausea", "pain", "fatigue", "dizziness",
-#     "shortness of breath", "sore throat", "runny nose", "congestion", "diarrhea",
-#     "vomiting", "chills", "sweating", "loss of appetite", "insomnia", 
-#     "itching", "rash", "swelling", "bleeding", "burning sensation",
-#     "weakness", "tingling", "numbness", "muscle cramps", "joint pain",
-#     "blurred vision", "double vision", "dry eyes", "sensitivity to light",
-#     "difficulty breathing", "palpitations", "chest pain", "back pain",
-#     "stomach ache", "abdominal pain", "weight loss", "weight gain",
-#     "frequent urination", "difficulty urinating", "anxiety", "depression",
-#     "irritability", "confusion", "memory loss", "bruising"
-# ]
-# diseases = [
-#     "diabetes", "cancer", "asthma", "flu", "pneumonia", "hypertension",
-#     "arthritis", "bronchitis", "migraine", "stroke", "heart attack",
-#     "coronary artery disease", "tuberculosis", "malaria", "dengue",
-#     "hepatitis", "anemia", "thyroid disease", "eczema", "psoriasis",
-#     "osteoporosis", "parkinson's", "alzheimer's", "depression",
-#     "anxiety disorder", "schizophrenia", "epilepsy", "bipolar disorder",
-#     "chronic kidney disease", "liver cirrhosis", "HIV", "AIDS",
-#     "covid-19", "cholera", "smallpox", "measles", "mumps", 
-#     "rubella", "whooping cough", "obesity", "GERD", "IBS", 
-#     "celiac disease", "ulcerative colitis", "Crohn's disease", 
-#     "sleep apnea", "hypothyroidism", "hyperthyroidism"
-# ]
-
-
-# # Function to extract medical entities
-# def extract_medical_entities(user_input):
-#     user_input = preprocess_input(user_input)
-#     medical_entities = []
-#     for word in user_input.split():
-#         if word in symptoms or word in diseases:
-#             medical_entities.append(word)
-#     return medical_entities
-# # def extract_medical_entities(user_input):
-# #     doc = nlp(user_input)
-# #     medical_entities = [ent.text for ent in doc.ents if ent.label_ == "SYMPTOM" or ent.label_ == "DISEASE"]
-# #     print(medical_entities)
-# #     print("This is doc",doc)
-# #     return medical_entities
-
-# # Function to encode the image
-# def encode_image(uploaded_image):
-#     try:
-#         logger.debug("Encoding image...")
-#         buffered = BytesIO()
-#         uploaded_image.save(buffered, format="PNG")
-#         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
-
-# # Initialize messages
-# def initialize_messages():
-#      return [{"role": "system",
-#              "content": '''You are Dr. HealthBuddy, a professional, empathetic,
-#               and knowledgeable virtual doctor chatbot. Your purpose is to provide health information,
-#               symptom guidance, and lifestyle tips using the uploaded dataset as a reference for common
-#                symptoms and associated conditions.
-
-#  Utilize the dataset to provide information about symptoms and possible conditions for educational purposes.
-#  If a symptom matches data in the dataset, offer users relevant insights, and suggest general management strategies.
-#  Clearly communicate that you are not a substitute for professional medical advice.
-#  Encourage users to consult a licensed healthcare provider for any severe or persistent health issues.
-#  Maintain a friendly and understanding tone in all responses.
-#  Examples:
-
-#  User: "I have skin rash and itching. What could it be?"
-#  Response: "According to the data, skin rash and itching are common symptoms of conditions like fungal infections.
-#  You can try keeping the affected area dry and clean, and using over-the-counter antifungal creams.
-# If the rash persists or worsens, please consult a dermatologist."
-
-#  User: "What might cause nodal skin eruptions?"
-#  Response: "Nodal skin eruptions could be linked to conditions such as fungal infections.
-# It's best to monitor the symptoms and avoid scratching.
-#  For a proper diagnosis, consider visiting a healthcare provider.'''}]
-
-
-# messages = initialize_messages()
-
-# def customLLMBot(user_input, uploaded_image, chat_history):
-#     try:
-#         global messages
-#         logger.info("Processing input...")
-
-#         # Preprocess the user input
-#         user_input = preprocess_input(user_input)
-        
-#         # Analyze sentiment (Optional)
-#         sentiment = analyze_sentiment(user_input)
-#         logger.info(f"Sentiment detected: {sentiment}")
-
-#         # Extract medical entities (Optional)
-#         medical_entities = extract_medical_entities(user_input)
-#         logger.info(f"Extracted medical entities: {medical_entities}")
-
-#         # 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)
-
-#             logger.debug(f"Image received, size: {len(base64_image)} bytes")
-
-#             # Create a message for the image prompt
-#             messages_image = [
-#                 {
-#                     "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...")
-#             response = client.chat.completions.create(
-#                 model="llama-3.2-11b-vision-preview",
-#                 messages=messages_image,
-#             )
-#             logger.info("Image processed successfully.")
-#         else:
-#             # Process text input
-#             logger.info("Processing text input...")
-#             messages.append({
-#                 "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
-#         logger.debug(f"LLM reply: {LLM_reply}")
-
-#         # Append the bot's response to the chat history
-#         chat_history.append(("bot", LLM_reply))
-#         messages.append({"role": "assistant", "content": LLM_reply})
-
-#         # 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 chat history and audio file
-#         return chat_history, audio_file
-
-#     except Exception as e:
-#         logger.error(f"Error in customLLMBot function: {e}")
-#         return [("user", user_input or "Image uploaded"), ("bot", f"An error occurred: {e}")], None
-
-# # Gradio Interface
-# def chatbot_ui():
-#     with gr.Blocks() as demo:
-#         gr.Markdown("# Healthcare Chatbot Doctor")
-
-#         # State for user chat history
-#         chat_history = gr.State([])
-
-#         # 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, history):
-#             logger.info("User submitted a query.")
-#             response, audio = customLLMBot(user_query, image, history)
-#             return response, audio, None, "", history  # Clear the image after submission
-
-#         # Submit on pressing Enter key
-#         user_input.submit(
-#             handle_submit,
-#             inputs=[user_input, uploaded_image, chat_history],
-#             outputs=[chatbot, audio_output, uploaded_image, user_input, chat_history],
-#         )
-
-#         # Submit on button click
-#         submit_btn.click(
-#             handle_submit,
-#             inputs=[user_input, uploaded_image, chat_history],
-#             outputs=[chatbot, audio_output, uploaded_image, user_input, chat_history],
-#         )
-
-#         # Action for clearing all fields
-#         clear_btn.click(
-#             lambda: ([], "", None, []),
-#             inputs=[],
-#             outputs=[chatbot, user_input, uploaded_image, chat_history],
-#         )
-
-#     return demo
-
-# # Launch the interface
-# #chatbot_ui().launch(server_name="0.0.0.0", server_port=7860)
-
-# chatbot_ui().launch(server_name="localhost", server_port=7860)
-
-
+# Import necessary libraries
 from groq import Groq
 import gradio as gr
 from gtts import gTTS
@@ -274,15 +10,14 @@ import logging
 import spacy
 from transformers import pipeline
 import torch
-from PIL import Image
+import cv2
+import numpy as np
 from torchvision import transforms
 import pathlib
-import cv2  # Import OpenCV
-import numpy as np
 
-# # Pathlib adjustment for Windows compatibility
-# temp = pathlib.PosixPath
-# pathlib.PosixPath = pathlib.WindowsPath
+# Pathlib adjustment for Windows compatibility
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
 
 # Set up logger
 logger = logging.getLogger(__name__)
@@ -295,24 +30,14 @@ file_handler.setFormatter(formatter)
 logger.addHandler(console_handler)
 logger.addHandler(file_handler)
 
-#Initialize Groq Client
+# Initialize Groq Client
 client = Groq(api_key=os.getenv("GROQ_API_KEY_2"))
 
-# # Initialize Groq Client
-# client = Groq(api_key="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")
-
-# # Initialize spaCy NLP model for named entity recognition (NER)
-# nlp = spacy.load("en_core_web_sm")
-
+# Initialize Groq Client
+#client = Groq(api_key="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")
 
-
-# Download the spaCy model if it doesn't exist
-try:
-    nlp = spacy.load("en_core_web_sm")
-except OSError:
-    print("Downloading 'en_core_web_sm' model...")
-    spacy.cli.download("en_core_web_sm")
-    nlp = spacy.load("en_core_web_sm")
+# Initialize spaCy NLP model for named entity recognition (NER)
+nlp = spacy.load("en_core_web_sm")
 
 # Initialize sentiment analysis model using Hugging Face
 sentiment_analyzer = pipeline("sentiment-analysis")
@@ -389,36 +114,8 @@ def encode_image(uploaded_image):
 
 # Initialize messages
 def initialize_messages():
-    return [{"role": "system",
-             "content": '''You are Dr. SkinCare, a highly experienced and professional virtual dermatologist chatbot with over 40 years of expertise in diagnosing and managing skin conditions. You provide accurate, empathetic, and actionable advice on skin-related concerns, including rashes, acne, infections, and chronic skin diseases. Your goal is to offer clear explanations, practical solutions, and guidance on when to seek in-person care from a dermatologist.
-
-You only respond to skin-related inquiries and strive to provide the best possible guidance. Your responses should include:
-1. A clear explanation of the possible condition(s) based on the symptoms described.
-2. Practical, actionable steps for managing the issue at home (if applicable).
-3. A recommendation to consult a dermatologist for persistent, severe, or unclear symptoms.
-
-Maintain a friendly, professional, and empathetic tone in all interactions. Always emphasize that you are not a substitute for professional medical advice, diagnosis, or treatment.
-
-**Prompt Template:**
-- Input: Patient’s skin-related concerns, including symptoms, questions, or specific issues they mention.
-- Response: Start with a polite acknowledgment of the patient’s concern. Provide a clear, concise explanation of the possible condition(s) and suggest practical, actionable steps. If needed, advise on when to consult a dermatologist.
-
-**Examples:**
+    return [{"role": "system", "content": '''You are Dr. HealthBuddy, a professional, empathetic, and knowledgeable virtual doctor chatbot.'''}]
 
-- User: "I have a red, itchy rash on my arm. What could it be?"
-  Response: "Hello! A red, itchy rash on your arm could be caused by several conditions, such as eczema, contact dermatitis, or a fungal infection. Try keeping the area clean and moisturized, and avoid scratching. Over-the-counter hydrocortisone cream or antihistamines may help. If the rash persists or worsens, please consult a dermatologist for a proper diagnosis."
-
-- User: "I have small, raised bumps on my face. What might this be?"
-  Response: "Hi there! Small, raised bumps on your face could be due to acne, closed comedones, or even a mild allergic reaction. Avoid using harsh skincare products and consider using a gentle cleanser and non-comedogenic moisturizer. If the bumps don’t improve or spread, it’s best to see a dermatologist for further evaluation."
-
-- User: "I’ve noticed dark patches on my skin. What could cause this?"
-  Response: "Hello! Dark patches on the skin, known as hyperpigmentation, can be caused by sun exposure, hormonal changes, or conditions like melasma. Use a broad-spectrum sunscreen daily and consider skincare products with ingredients like vitamin C or niacinamide. For persistent or concerning patches, consult a dermatologist to rule out underlying conditions."
-
-- User: "I have a mole that looks irregular. Should I be worried?"
-  Response: "Hi. Irregular moles can sometimes be a sign of skin changes that need attention. Keep an eye on the mole for changes in size, shape, or color, and avoid exposing it to excessive sunlight. It’s important to have it checked by a dermatologist to rule out any serious concerns, such as skin cancer."
-
-Always maintain a compassionate tone, provide educational insights, and stress that you are not a substitute for professional medical advice. Encourage users to consult a dermatologist for any serious, persistent, or unclear skin concerns.'''
-    }]
 messages = initialize_messages()
 
 # Function for image prediction using YOLOv5
@@ -428,9 +125,13 @@ def predict_image(image):
         if image is None:
             return "Error: No image uploaded.", "No description available."
 
-        # Convert PIL image to NumPy array (OpenCV format)
+        # Convert PIL image to NumPy array
         image_np = np.array(image)  # Convert PIL image to NumPy array
 
+        # Handle grayscale images
+        if len(image_np.shape) == 2:  # Grayscale image
+            image_np = cv2.cvtColor(image_np, cv2.COLOR_GRAY2RGB)
+
         # Convert RGB to BGR (OpenCV uses BGR by default)
         image_np = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
 
@@ -439,8 +140,8 @@ def predict_image(image):
 
         # Transform the image for the model
         transform = transforms.Compose([
-            transforms.ToTensor(),  # Convert image to tensor
-            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # Normalize
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
         ])
         im = transform(image_resized).unsqueeze(0)  # Add batch dimension (BCHW)
 
@@ -465,10 +166,6 @@ def predict_image(image):
             prediction_result = f"Predicted Class ID: {predicted_class_id}\nConfidence: {confidence_score:.4f}"
             description = "No description available."
 
-        # Display the image with OpenCV (optional)
-        cv2.imshow("Processed Image", image_resized)
-        cv2.waitKey(1)  # Wait for 1 ms to display the image
-
         return prediction_result, description
 
     except Exception as e:
@@ -485,83 +182,6 @@ def get_description(class_name):
     }
     return descriptions.get(class_name.lower(), "No description available.")
 
-# Custom LLM Bot Function
-def customLLMBot(user_input, uploaded_image, chat_history):
-    try:
-        global messages
-        logger.info("Processing input...")
-
-        # Preprocess the user input
-        user_input = preprocess_input(user_input)
-        
-        # Analyze sentiment (Optional)
-        sentiment = analyze_sentiment(user_input)
-        logger.info(f"Sentiment detected: {sentiment}")
-
-        # Extract medical entities (Optional)
-        medical_entities = extract_medical_entities(user_input)
-        logger.info(f"Extracted medical entities: {medical_entities}")
-
-        # 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)
-
-            logger.debug(f"Image received, size: {len(base64_image)} bytes")
-
-            # Create a message for the image prompt
-            messages_image = [
-                {
-                    "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...")
-            response = client.chat.completions.create(
-                model="llama-3.2-11b-vision-preview",
-                messages=messages_image,
-            )
-            logger.info("Image processed successfully.")
-        else:
-            # Process text input
-            logger.info("Processing text input...")
-            messages.append({
-                "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
-        logger.debug(f"LLM reply: {LLM_reply}")
-
-        # Append the bot's response to the chat history
-        chat_history.append(("bot", LLM_reply))
-        messages.append({"role": "assistant", "content": LLM_reply})
-
-        # 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 chat history and audio file
-        return chat_history, audio_file
-
-    except Exception as e:
-        logger.error(f"Error in customLLMBot function: {e}")
-        return [("user", user_input or "Image uploaded"), ("bot", f"An error occurred: {e}")], None
-
 # Gradio Interface
 def chatbot_ui():
     with gr.Blocks() as demo:
@@ -654,7 +274,7 @@ def chatbot_ui():
     return demo
 
 # Launch the interface
-# chatbot_ui().launch(server_name="localhost", server_port=7860)
+#chatbot_ui().launch(server_name="localhost", server_port=7860)
 
 # Launch the interface
 chatbot_ui().launch(server_name="0.0.0.0", server_port=7860)