Removed Model from root directory

.gitattributes

@@ -35,6 +35,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 frontend/react-code/src/assets/falcondemo.mp4 filter=lfs diff=lfs merge=lfs -text
 frontend/FINAL_MODEL.keras filter=lfs diff=lfs merge=lfs -text
-FINAL_MODEL.keras filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text

FINAL_MODEL.keras

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dff0eafd711bccd941d3cca9bff29202c4b4e40a4ef2d814986137d997ccfd13
-size 228465191

app.py

@@ -1,520 +0,0 @@
-import streamlit as st
-import requests
-from utils.ai71_utils import get_ai71_response
-from datetime import datetime
-import cv2
-import numpy as np
-from PIL import Image
-import supervision as sv
-import matplotlib.pyplot as plt
-import io
-import os
-from inference_sdk import InferenceHTTPClient
-from bs4 import BeautifulSoup
-import tensorflow as tf
-import pandas as pd
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn.metrics import accuracy_score, classification_report
-import nltk
-import re
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-
-# --- Preprocess text function (moved outside session state) ---
-def preprocess_text(text):
-    # Convert to lowercase
-    text = text.lower()
-
-    cleaned_text = re.sub(r'[^a-zA-Z0-9\s\,]', ' ', text)
-    # Tokenize text
-    tokens = word_tokenize(cleaned_text)
-
-    # Remove stop words
-    stop_words = set(stopwords.words('english'))
-    tokens = [word for word in tokens if word not in stop_words]
-
-    # Rejoin tokens into a single string
-    cleaned_text = ' '.join(tokens)
-
-    return cleaned_text
-
-st.title("Medi Scape Dashboard")
-
-# --- Session State Initialization ---
-if 'disease_model' not in st.session_state:
-    try:
-        model_path = 'frontend/FINAL_MODEL.keras'
-        print(f"Attempting to load disease model from: {model_path}")
-        print(f"Model file exists: {os.path.exists(model_path)}") 
-        st.session_state.disease_model = tf.keras.models.load_model(model_path)
-        print("Disease model loaded successfully!")
-    except FileNotFoundError:
-        st.error("Disease classification model not found. Please ensure 'FINAL_MODEL.keras' is in the same directory as this app.")
-        st.session_state.disease_model = None
-
-# Load the vectorizer 
-if 'vectorizer' not in st.session_state:
-    # Always create and save the vectorizer
-    print("Creating and saving vectorizer...")
-    # Load your training data here (replace with your actual data loading)
-    dataset_1 = pd.read_csv("Symptoms_Detection/training_data.csv")
-    dataset_2 = pd.read_csv("Symptoms_Detection/Symptom2Disease.csv")
-
-    # --- Data Preprocessing ---
-    # Create symptoms_text column
-    dataset_1['symptoms_text'] = dataset_1.apply(lambda row: ','.join([col for col in dataset_1.columns if row[col] == 1]), axis=1)
-    final_dataset = pd.DataFrame(dataset_1[["prognosis", "symptoms_text"]])
-    final_dataset.columns = ['label', 'text']
-
-    # Combine datasets
-    df_combined = pd.concat([final_dataset, dataset_2[['label', 'text']]], axis=0, ignore_index=True)
-
-    # Preprocess text (apply the preprocess_text function to the 'text' column)
-    df_combined['text'] = df_combined['text'].apply(preprocess_text)
-
-    # --- End of Data Preprocessing ---
-
-    # Create and fit the vectorizer
-    vectorizer = CountVectorizer()
-    vectorizer.fit(df_combined['text'])
-
-    # Save the vectorizer
-    vectorizer_path = "vectorizer.pkl"
-    pd.to_pickle(vectorizer, vectorizer_path)
-    st.session_state.vectorizer = vectorizer
-    print("Vectorizer saved successfully!")
-
-if 'model_llm' not in st.session_state:
-    # --- Load pre-trained model and vectorizer ---
-    st.session_state.model_llm = LogisticRegression()
-    try:
-        llm_model_path = "logistic_regression_model.pkl"
-        print(f"Attempting to load LLM model from: {llm_model_path}")
-        print(f"LLM Model file exists: {os.path.exists(llm_model_path)}")
-        st.session_state.model_llm = pd.read_pickle(llm_model_path)  
-        print("LLM model loaded successfully!")
-    except FileNotFoundError:
-        st.error("LLM model file not found. Please ensure 'logistic_regression_model.pkl' is in the same directory.")
-        st.session_state.model_llm = None
-
-    # Load datasets (only for reference, not used for training)
-    dataset_1 = pd.read_csv("Symptoms_Detection/training_data.csv")
-    dataset_2 = pd.read_csv("Symptoms_Detection/Symptom2Disease.csv")
-
-    # Create symptoms_text column (only for reference, not used for training)
-    dataset_1['symptoms_text'] = dataset_1.apply(lambda row: ','.join([col for col in dataset_1.columns if row[col] == 1]), axis=1)
-    final_dataset = pd.DataFrame(dataset_1[["prognosis", "symptoms_text"]])
-    final_dataset.columns = ['label', 'text']
-
-    # Combine datasets (only for reference, not used for training)
-    df_combined = pd.concat([final_dataset, dataset_2[['label', 'text']]], axis=0, ignore_index=True)
-
-    # Store in session state (only for reference, not used for training)
-    st.session_state.df_combined = df_combined
-# --- End of Session State Initialization ---
-
-# Load the disease classification model
-try:
-    disease_model = tf.keras.models.load_model('FINAL_MODEL.keras')
-except FileNotFoundError:
-    st.error("Disease classification model not found. Please ensure 'FINAL_MODEL.keras' is in the same directory as this app.")
-    disease_model = None
-
-# Sidebar Navigation
-st.sidebar.title("Navigation")
-page = st.sidebar.radio("Go to", ["Home", "AI Chatbot Diagnosis", "Drug Identification", "Disease Detection", "Outbreak Alert"])
-
-# Access secrets using st.secrets
-if "INFERENCE_API_URL" not in st.secrets or "INFERENCE_API_KEY" not in st.secrets:
-    st.error("Please make sure to set your secrets in the Streamlit secrets settings.")
-else:
-    # Initialize the Inference Client
-    CLIENT = InferenceHTTPClient(
-        api_url=st.secrets["INFERENCE_API_URL"],
-        api_key=st.secrets["INFERENCE_API_KEY"]
-    )
-
-    # Function to preprocess the image
-    def preprocess_image(image_path):
-        # Load the image
-        image = cv2.imread(image_path)
-
-        # Convert to grayscale
-        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-        # Remove noise
-        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
-
-        # Thresholding/Binarization
-        _, binary = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-
-        # Dilation and Erosion
-        kernel = np.ones((1, 1), np.uint8)
-        dilated = cv2.dilate(binary, kernel, iterations=1)
-        eroded = cv2.erode(dilated, kernel, iterations=1)
-
-        # Edge detection
-        edges = cv2.Canny(eroded, 100, 200)
-
-        # Deskewing
-        coords = np.column_stack(np.where(edges > 0))
-        angle = cv2.minAreaRect(coords)[-1]
-        if angle < -45:
-            angle = -(90 + angle)
-        else:
-            angle = -angle
-
-        (h, w) = edges.shape[:2]
-        center = (w // 2, h // 2)
-        M = cv2.getRotationMatrix2D(center, angle, 1.0)
-        deskewed = cv2.warpAffine(edges, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
-
-        # Find contours
-        contours, _ = cv2.findContours(deskewed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-        # Draw contours on the original image
-        contour_image = image.copy()
-        cv2.drawContours(contour_image, contours, -1, (0, 255, 0), 2)
-
-        return contour_image
-
-    def get_x1(detection):
-        return detection.xyxy[0][0]
-
-# Access secrets using st.secrets
-if "INFERENCE_API_URL" not in st.secrets or "INFERENCE_API_KEY" not in st.secrets:
-    st.error("Please make sure to set your secrets in the Streamlit secrets settings.")
-else:
-    # Initialize the Inference Client
-    CLIENT = InferenceHTTPClient(
-        api_url=st.secrets["INFERENCE_API_URL"],
-        api_key=st.secrets["INFERENCE_API_KEY"]
-    )
-
-    # Function to preprocess the image
-    def preprocess_image(image_path):
-        # Load the image
-        image = cv2.imread(image_path)
-
-        # Convert to grayscale
-        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-        # Remove noise
-        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
-
-        # Thresholding/Binarization
-        _, binary = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-
-        # Dilation and Erosion
-        kernel = np.ones((1, 1), np.uint8)
-        dilated = cv2.dilate(binary, kernel, iterations=1)
-        eroded = cv2.erode(dilated, kernel, iterations=1)
-
-        # Edge detection
-        edges = cv2.Canny(eroded, 100, 200)
-
-        # Deskewing
-        coords = np.column_stack(np.where(edges > 0))
-        angle = cv2.minAreaRect(coords)[-1]
-        if angle < -45:
-            angle = -(90 + angle)
-        else:
-            angle = -angle
-
-        (h, w) = edges.shape[:2]
-        center = (w // 2, h // 2)
-        M = cv2.getRotationMatrix2D(center, angle, 1.0)
-        deskewed = cv2.warpAffine(edges, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
-
-        # Find contours
-        contours, _ = cv2.findContours(deskewed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-        # Draw contours on the original image
-        contour_image = image.copy()
-        cv2.drawContours(contour_image, contours, -1, (0, 255, 0), 2)
-
-        return contour_image
-
-    def get_x1(detection):
-        return detection.xyxy[0][0]
-
-    # --- Prediction function (using session state) ---
-    def predict_disease(symptoms):
-        if st.session_state.vectorizer is not None and st.session_state.model_llm is not None:
-            preprocessed_symptoms = preprocess_text(symptoms)
-            symptoms_vectorized = st.session_state.vectorizer.transform([preprocessed_symptoms])
-            prediction = st.session_state.model_llm.predict(symptoms_vectorized)
-            return prediction[0]
-        else:
-            st.error("Unable to make prediction. Vectorizer or LLM model is not loaded.")
-            return None
-
-    # --- New function to analyze X-ray with LLM ---
-    def analyze_xray_with_llm(predicted_class):
-        prompt = f"""
-        Based on a chest X-ray analysis, the predicted condition is {predicted_class}. 
-        Please provide a concise summary of this condition, including:
-        - A brief description of the condition.
-        - Common symptoms associated with it.
-        - Potential causes.
-        - General treatment approaches.
-        - Any other relevant information for a patient.
-        """
-        llm_response = get_ai71_response(prompt)
-        st.write("## LLM Analysis of X-ray Results:")
-        st.write(llm_response)
-
-    if page == "Home":
-        st.markdown("## Welcome to Medi Scape")
-        st.write("Medi Scape is an AI-powered healthcare application designed to streamline the process of understanding and managing medical information.  It leverages advanced AI models to provide features such as prescription analysis, disease detection from chest X-rays, and symptom-based diagnosis assistance.")
-
-        st.markdown("## Features")
-        st.write("Medi Scape provides various AI-powered tools for remote healthcare, including:")
-        features = [
-            "**AI Chatbot Diagnosis:** Interact with an AI chatbot for preliminary diagnosis and medical information.",
-            "**Drug Identification:** Upload a prescription image to identify medications and access relevant details.",
-            "**Doctor's Handwriting Identification:** Our system can accurately recognize and process doctor's handwriting.",
-            "**Disease Detection:** Upload a chest X-ray image to detect potential diseases.",
-            "**Outbreak Alert:** Stay informed about potential disease outbreaks in your area."
-        ]
-        for feature in features:
-            st.markdown(f"- {feature}")
-
-        st.markdown("## How it Works")
-        steps = [
-            "**Upload:** You can upload a prescription image for drug identification or a chest X-ray image for disease detection.",
-            "**Process:** Our AI models will analyze the image and extract relevant information.",
-            "**Results:** You will receive identified drug names, uses, side effects, and more, or a potential disease diagnosis."
-        ]
-        for i, step in enumerate(steps, 1):
-            st.markdown(f"{i}. {step}")
-
-        st.markdown("## Key Features")
-        key_features = [
-            "**AI-Powered:** Leverages advanced AI models for accurate analysis and diagnosis.",
-            "**User-Friendly:** Simple and intuitive interface for easy navigation and interaction.",
-            "**Secure:** Your data is protected and handled with confidentiality."
-        ]
-        for feature in key_features:
-            st.markdown(f"- {feature}")
-
-        st.markdown("Please use the sidebar to navigate to different features.")
-
-    elif page == "AI Chatbot Diagnosis":
-        st.write("Enter your symptoms separated by commas:")
-        symptoms_input = st.text_area("Symptoms:")
-        if st.button("Diagnose"):
-            if symptoms_input:
-                # --- Pipeline 1 Implementation ---
-                # 1. Symptom Input (already done with st.text_area)
-                # 2. Regression Prediction
-                regression_prediction = predict_disease(symptoms_input)
-
-                if regression_prediction is not None:
-                    # 3. LLM Prompt Enhancement
-                    prompt = f"""The predicted condition based on a symptom analysis is {regression_prediction}. 
-                    Provide a detailed explanation of this condition, including possible causes, common symptoms, 
-                    and general treatment approaches. Also, suggest when a patient should consult a doctor."""
-
-                    # 4. LLM Output
-                    llm_response = get_ai71_response(prompt)
-
-                    # 5. Combined Output
-                    st.write("## Logistic Regression Prediction:")
-                    st.write(regression_prediction)
-
-                    st.write("## LLM Explanation:")
-                    st.write(llm_response)
-                # --- End of Pipeline 1 Implementation ---
-
-            else:
-                st.write("Please enter your symptoms.")
-
-    elif page == "Drug Identification":
-        st.write("Upload a prescription image for drug identification.")
-        uploaded_file = st.file_uploader("Upload prescription", type=["png", "jpg", "jpeg"])
-
-        if uploaded_file is not None:
-            # Display the uploaded image
-            image = Image.open(uploaded_file)
-            st.image(image, caption="Uploaded Prescription", use_column_width=True)
-
-            if st.button("Process Prescription"):
-                # Save the image to a temporary file
-                temp_image_path = "temp_image.jpg"
-                image.save(temp_image_path)
-
-                # Preprocess the image
-                preprocessed_image = preprocess_image(temp_image_path)
-
-                # Perform inference
-                result_doch1 = CLIENT.infer(preprocessed_image, model_id="doctor-s-handwriting/1")
-
-                # Extract labels and detections
-                labels = [item["class"] for item in result_doch1["predictions"]]
-                detections = sv.Detections.from_inference(result_doch1)
-
-                # Sort detections and labels
-                sorted_indices = sorted(range(len(detections)), key=lambda i: get_x1(detections[i]))
-                sorted_detections = [detections[i] for i in sorted_indices]
-                sorted_labels = [labels[i] for i in sorted_indices]
-
-                # Convert list to string
-                resulting_string = ''.join(sorted_labels)
-
-                # Display results
-                st.subheader("Processed Prescription")
-                fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))
-
-                # Plot bounding boxes
-                image_with_boxes = preprocessed_image.copy()
-                for detection in sorted_detections:
-                    x1, y1, x2, y2 = detection.xyxy[0]
-                    cv2.rectangle(image_with_boxes, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2)      
-                ax1.imshow(cv2.cvtColor(image_with_boxes, cv2.COLOR_BGR2RGB))
-                ax1.set_title("Bounding Boxes")
-                ax1.axis('off')
-
-                # Plot labels
-                image_with_labels = preprocessed_image.copy()
-                for i, detection in enumerate(sorted_detections):
-                    x1, y1, x2, y2 = detection.xyxy[0]
-                    label = sorted_labels[i]
-                    cv2.putText(image_with_labels, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
-                ax2.imshow(cv2.cvtColor(image_with_labels, cv2.COLOR_BGR2RGB))
-                ax2.set_title("Labels")
-                ax2.axis('off')
-
-                st.pyplot(fig)
-
-                st.write("Extracted Text from Prescription:", resulting_string)
-
-                # Prepare prompt for LLM
-                prompt = f"""Analyze the following prescription text:
-                {resulting_string}
-
-                Please provide:
-                1. Identified drug name(s)
-                2. Full name of each identified drug
-                3. Primary uses of each drug
-                4. Common side effects
-                5. Recommended dosage (if identifiable from the text)
-                6. Any warnings or precautions
-                7. Potential interactions with other medications (if multiple drugs are identified)
-                8. Any additional relevant information for the patient
-
-                If any part of the prescription is unclear or seems incomplete, please mention that and provide information about possible interpretations or matches. Always emphasize the importance of consulting a healthcare professional for accurate interpretation and advice."""
-
-                # Get LLM response
-                llm_response = get_ai71_response(prompt)
-
-                st.subheader("AI Analysis of the Prescription")
-                st.write(llm_response)
-
-                # Remove the temporary image file
-                os.remove(temp_image_path)
-
-        else:
-            st.info("Please upload a prescription image to proceed.")
-
-    elif page == "Disease Detection":
-        st.write("Upload a chest X-ray image for disease detection.")
-        uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-        if uploaded_image is not None and st.session_state.disease_model is not None:
-            # Display the image
-            img_opened = Image.open(uploaded_image).convert('RGB')
-            image_pred = np.array(img_opened)
-            image_pred = cv2.resize(image_pred, (150, 150))
-
-            # Convert the image to a numpy array
-            image_pred = np.array(image_pred)
-
-            # Rescale the image (if the model was trained with rescaling)
-            image_pred = image_pred / 255.0
-
-            # Add an extra dimension to match the input shape (1, 150, 150, 3)
-            image_pred = np.expand_dims(image_pred, axis=0)
-
-            # Predict using the model
-            prediction = st.session_state.disease_model.predict(image_pred)
-
-            # Get the predicted class
-            predicted_ = np.argmax(prediction)
-
-            # Decode the prediction
-            if predicted_ == 0:
-                predicted_class = "Covid"
-            elif predicted_ == 1:
-                predicted_class = "Normal Chest X-ray"
-            else:
-                predicted_class = "Pneumonia"
-
-            st.image(image_pred, caption='Input image by user', use_column_width=True)
-            st.write("Prediction Classes for different types:")
-            st.write("COVID: 0")
-            st.write("Normal Chest X-ray: 1")
-            st.write("Pneumonia: 2")
-            st.write("\n")
-            st.write("DETECTED DISEASE DISPLAY")
-            st.write(f"Predicted Class : {predicted_}")
-            st.write(predicted_class)
-
-            # Analyze X-ray results with LLM
-            analyze_xray_with_llm(predicted_class)
-        else:
-            st.write("Please upload an image file or ensure the disease model is loaded.")
-
-    elif page == "Outbreak Alert":
-        st.markdown("## **Disease Outbreak News (from WHO)**")
-
-        # Fetch WHO news page
-        url = "https://www.who.int/news-room/events"
-        response = requests.get(url)
-        response.raise_for_status()  # Raise an exception for bad status codes
-
-        soup = BeautifulSoup(response.content, 'html.parser')
-
-        # Find news articles (adjust selectors if WHO website changes)
-        articles = soup.find_all('div', class_='list-view--item')
-
-        for article in articles[:5]:  # Display the top 5 news articles
-            title_element = article.find('a', class_='link-container')
-            if title_element:
-                title = title_element.text.strip()
-                link = title_element['href']
-                date_element = article.find('span', class_='date')
-                date = date_element.text.strip() if date_element else "Date not found"
-                
-                # Format date
-                date_parts = date.split()
-                if len(date_parts) >= 3:
-                    try:
-                        formatted_date = datetime.strptime(date, "%d %B %Y").strftime("%Y-%m-%d")
-                    except ValueError:
-                        formatted_date = date  # Keep the original date if formatting fails
-                else:
-                    formatted_date = date
-                
-                # Display news item in a card-like container
-                with st.container():
-                    st.markdown(f"**{formatted_date}**")
-                    st.markdown(f"[{title}]({link})")
-                    st.markdown("---")
-            else:
-                st.write("Could not find article details.")
-
-# Auto-scroll to the bottom of the chat container
-st.markdown(
-    """
-    <script>
-    const chatContainer = document.querySelector('.st-chat-container');
-    if (chatContainer) {
-        chatContainer.scrollTop = chatContainer.scrollHeight;
-    }
-    </script>
-    """,
-    unsafe_allow_html=True,
-)

logistic_regression_model.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cfd5b38cded9c2b4426bb8cc9f337cd078973f5488e2692f3ecd21db3cc41c96
-size 601655