project added

app.py

@@ -1,247 +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
-# import torch
-# import numpy as np
-# from torchvision import transforms
-# from PIL import Image
-# import pathlib
-
-# # Pathlib adjustment for Windows compatibility
-# temp = pathlib.PosixPath
-# pathlib.PosixPath = pathlib.WindowsPath
-
-# # 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"))
-
-# # 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")
-
-# # Load pre-trained YOLOv5 model
-# def load_yolov5_model():
-#     model = torch.hub.load(
-#         'ultralytics/yolov5', 
-#         'custom', 
-#         path='models/best.pt'
-#     )
-#     model.eval()
-#     return model
-
-# model = load_yolov5_model()
-
-# # Function to preprocess user input for better NLP understanding
-# def preprocess_input(user_input):
-#     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']
-
-# # 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"
-# ]
-
-# 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
-
-# # 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.'''}]
-
-# messages = initialize_messages()
-
-# # Function for image prediction using YOLOv5
-# def predict_image(image):
-#     try:
-#         if image is None:
-#             return "Error: No image uploaded.", "No description available."
-
-#         # Resize the image to match the model's expected input size
-#         image_resized = image.resize((224, 224))
-
-#         # Transform the image for the model
-#         transform = transforms.Compose([
-#             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)
-
-#         # Get predictions
-#         with torch.no_grad():
-#             output = model(im)  # Raw model output (logits)
-
-#         # Apply softmax to get confidence scores
-#         softmax = torch.nn.Softmax(dim=1)
-#         probs = softmax(output)
-
-#         # Get the predicted class and its confidence score
-#         predicted_class_id = torch.argmax(probs, dim=1).item()
-#         confidence_score = probs[0, predicted_class_id].item()
-
-#         # Get predicted class name if available
-#         if hasattr(model, 'names'):
-#             class_name = model.names[predicted_class_id]
-#             prediction_result = f"Predicted Class: {class_name}\nConfidence: {confidence_score:.4f}"
-#             description = get_description(class_name)  # Function to get description
-#         else:
-#             prediction_result = f"Predicted Class ID: {predicted_class_id}\nConfidence: {confidence_score:.4f}"
-#             description = "No description available."
-
-#         return prediction_result, description
-
-#     except Exception as e:
-#         logger.error(f"Error in image prediction: {e}")
-#         return f"An error occurred during image prediction: {e}", "No description available."
-
-# # Function to get description based on predicted class
-# def get_description(class_name):
-#     descriptions = {
-#         "bcc": "Basal cell carcinoma (BCC) is a type of skin cancer that begins in the basal cells. It often appears as a slightly transparent bump on the skin, though it can take other forms. BCC grows slowly and is unlikely to spread to other parts of the body, but early treatment is important to prevent damage to surrounding tissues.",
-#         "atopic": "Atopic dermatitis is a chronic skin condition characterized by itchy, inflamed skin. It is common in individuals with a family history of allergies or asthma.",
-#         "acne": "Acne is a skin condition that occurs when hair follicles become clogged with oil and dead skin cells. It often causes pimples, blackheads, and whiteheads, and is most common among teenagers.",
-#     }
-#     return descriptions.get(class_name.lower(), "No description available.")
-
-# # Gradio Interface
-# def chatbot_ui():
-#     with gr.Blocks() as demo:
-#         gr.Markdown("# Healthcare Chatbot Doctor")
-#         chat_history = gr.State([])
-
-#         with gr.Row():
-#             with gr.Column(scale=3):
-#                 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):
-#                 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")
-
-#         with gr.Row():
-#             with gr.Column():
-#                 gr.Markdown("### Upload Image for Prediction")
-#                 prediction_image = gr.Image(label="Upload Image", type="pil")
-#                 predict_btn = gr.Button("Predict")
-
-#             with gr.Column():
-#                 gr.Markdown("### Prediction Result")
-#                 prediction_output = gr.Textbox(label="Result", interactive=False)
-#                 gr.Markdown("### Description")
-#                 description_output = gr.Textbox(label="Description", interactive=False)
-#                 clear_prediction_btn = gr.Button("Clear Prediction")
-
-#         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
-
-#         def clear_prediction(prediction_image, prediction_output, description_output):
-#             return None, "", ""
-
-#         user_input.submit(
-#             handle_submit,
-#             inputs=[user_input, uploaded_image, chat_history],
-#             outputs=[chatbot, audio_output, uploaded_image, user_input, chat_history],
-#         )
-
-#         submit_btn.click(
-#             handle_submit,
-#             inputs=[user_input, uploaded_image, chat_history],
-#             outputs=[chatbot, audio_output, uploaded_image, user_input, chat_history],
-#         )
-
-#         clear_btn.click(
-#             lambda: ([], "", None, []),
-#             inputs=[],
-#             outputs=[chatbot, user_input, uploaded_image, chat_history],
-#         )
-
-#         predict_btn.click(
-#             predict_image,
-#             inputs=[prediction_image],
-#             outputs=[prediction_output, description_output],
-#         )
-
-#         clear_prediction_btn.click(
-#             clear_prediction,
-#             inputs=[prediction_image, prediction_output, description_output],
-#             outputs=[prediction_image, prediction_output, description_output],
-#         )
-
-#     return demo
-
-# # Launch the interface
-# chatbot_ui().launch(server_name="0.0.0.0", server_port=7860)
-
+# Import necessary libraries
 from groq import Groq
 import gradio as gr
 from gtts import gTTS
@@ -253,11 +10,15 @@ import logging
 import spacy
 from transformers import pipeline
 import torch
+import cv2
 import numpy as np
 from torchvision import transforms
-from PIL import Image
 import pathlib
 
+# Pathlib adjustment for Windows compatibility
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+
 # Set up logger
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
@@ -272,31 +33,45 @@ logger.addHandler(file_handler)
 # Initialize Groq Client
 client = Groq(api_key=os.getenv("GROQ_API_KEY_2"))
 
-# Initialize spaCy NLP model for named entity recognition (NER)
-import spacy
-from spacy.util import get_package_path
-
-try:
-    model_path = get_package_path("en_core_web_sm")
-    nlp = spacy.load(model_path)
-    print("Model loaded using absolute path!")
-except Exception as e:
-    print(f"Error: {e}")
+# 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 sentiment analysis model using Hugging Face
 sentiment_analyzer = pipeline("sentiment-analysis")
 
-# Load pre-trained YOLOv5 model
+import torch
+import os
+
 def load_yolov5_model():
+    # Load model from Hugging Face Hub or local path
     model = torch.hub.load(
-        'ultralytics/yolov5', 
-        'custom', 
-        path=r'C:\Users\RESHMA R B\OneDrive\Documents\Desktop\project_without_malayalam\chatbot2\models\best.pt'
+        'ultralytics/yolov5',  # Use the official YOLOv5 repo
+        'custom',
+        path='models/best.pt',  # Relative path to the model file
+        source='local'  # Change to 'github' if loading from the official repo
     )
-    model.eval()
     return model
 
+# Example usage
+if __name__ == "__main__":
+    model = load_yolov5_model()
+    print("Model loaded successfully!")
+
+
+# Load pre-trained YOLOv5 model
+# def load_yolov5_model():
+#     model = torch.hub.load(
+#         r'C:\Users\RESHMA R B\OneDrive\Documents\Desktop\project_without_malayalam\chatbot2\yolov5',
+#         'custom',
+#         path=r"C:\Users\RESHMA R B\OneDrive\Documents\Desktop\project_without_malayalam\chatbot2\models\best.pt",
+#         source="local"
+#     )
+#     model.eval()
+#     return model
+
 model = load_yolov5_model()
 
 # Function to preprocess user input for better NLP understanding
@@ -365,11 +140,22 @@ messages = initialize_messages()
 # Function for image prediction using YOLOv5
 def predict_image(image):
     try:
+        # Debug: Check if the image is None
         if image is None:
             return "Error: No image uploaded.", "No description available."
 
+        # 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)
+
         # Resize the image to match the model's expected input size
-        image_resized = image.resize((224, 224))
+        image_resized = cv2.resize(image_np, (224, 224))
 
         # Transform the image for the model
         transform = transforms.Compose([
@@ -411,6 +197,7 @@ def get_description(class_name):
         "bcc": "Basal cell carcinoma (BCC) is a type of skin cancer that begins in the basal cells. It often appears as a slightly transparent bump on the skin, though it can take other forms. BCC grows slowly and is unlikely to spread to other parts of the body, but early treatment is important to prevent damage to surrounding tissues.",
         "atopic": "Atopic dermatitis is a chronic skin condition characterized by itchy, inflamed skin. It is common in individuals with a family history of allergies or asthma.",
         "acne": "Acne is a skin condition that occurs when hair follicles become clogged with oil and dead skin cells. It often causes pimples, blackheads, and whiteheads, and is most common among teenagers.",
+        # Add more descriptions as needed
     }
     return descriptions.get(class_name.lower(), "No description available.")
 
@@ -418,10 +205,13 @@ def get_description(class_name):
 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):
+            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",
@@ -429,57 +219,71 @@ def chatbot_ui():
                     elem_id="user-input",
                     lines=1,
                 )
-            with gr.Column(scale=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")
 
+        # New section for image prediction (left and right layout)
         with gr.Row():
+            # Left side: Upload image
             with gr.Column():
                 gr.Markdown("### Upload Image for Prediction")
                 prediction_image = gr.Image(label="Upload Image", type="pil")
                 predict_btn = gr.Button("Predict")
 
+            # Right side: Prediction result and description
             with gr.Column():
                 gr.Markdown("### Prediction Result")
                 prediction_output = gr.Textbox(label="Result", interactive=False)
+
+                # Description column
                 gr.Markdown("### Description")
                 description_output = gr.Textbox(label="Description", interactive=False)
+
+                # Clear button for prediction result (below description box)
                 clear_prediction_btn = gr.Button("Clear Prediction")
 
+        # 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 prediction result and image
         def clear_prediction(prediction_image, prediction_output, description_output):
             return None, "", ""
 
+        # 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],
         )
 
+        # Action for image prediction
         predict_btn.click(
             predict_image,
             inputs=[prediction_image],
-            outputs=[prediction_output, description_output],
+            outputs=[prediction_output, description_output],  # Update both outputs
         )
 
+        # Action for clearing prediction result and image
         clear_prediction_btn.click(
             clear_prediction,
             inputs=[prediction_image, prediction_output, description_output],
@@ -489,4 +293,7 @@ def chatbot_ui():
     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)
+
+# Launch the interface
+chatbot_ui().launch(server_name="0.0.0.0", server_port=7860)