Update app.py

app.py

@@ -1,205 +1,205 @@
-import streamlit as st
-import google.generativeai as genai
-import os
-import torch
-from dotenv import load_dotenv
-import torch.nn as nn
-from torchvision import models, transforms
-import torch.nn.functional as F
-import matplotlib.pyplot as plt
-import seaborn as sns
-import numpy as np
-from PIL import Image
-from io import BytesIO
-from predictor import *
-from image_processor import *
-import google.generativeai as genai
-import gdown
-load_dotenv()
-api_key = os.getenv("API_KEY")
-genai.configure(api_key=api_key)
-
-gen_model = genai.GenerativeModel('gemini-1.5-flash-latest')
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-@st.cache_resource
-def download_model():
-    file_id = "1Ovlm72q3sa6BxobWb-6QKTAF-uv753kZ"
-    url = f'https://drive.google.com/uc?export=download&id={file_id}'
-    gdown.download(url, 'model.pth', quiet=False)
-    model = models.vgg16(weights = 'VGG16_Weights.DEFAULT')
-    model.classifier[6] = nn.Linear(4096,4)
-    return model
-
-model = download_model()
-
-def createForm(prediction):
-    st.markdown("<h2 style='text-align: center;'>To get the report fill the details</h2>", unsafe_allow_html=True)
-    with st.form(key='user_info_form'):
-        patient_name = st.text_input("Patient Name")
-        patient_age = st.number_input("Age", min_value=0, max_value=120)
-        patient_gender = st.selectbox("Gender", ["Male", "Female", "Other"])
-        patient_symptoms = st.text_area("Other Symptoms", placeholder="Describe the symptoms...")
-        submit_button = st.form_submit_button("Generate Report")
-    if submit_button:
-        if not patient_name:
-            st.error('Patient name is required!!')
-        elif not patient_age:
-            st.error('Patient age is required!!')
-        if patient_symptoms == "":
-            patient_symptoms = 'NIL'
-        user_data = {
-            "Patient Name": patient_name,
-            "Age": patient_age,
-            "Gender": patient_gender,
-            "Symptoms": patient_symptoms
-        }
-        st.markdown(f"### Report for {patient_name}")
-        st.write("(The report generation will be solely based on the symptoms provided and prediction)")
-        generate_and_display_report(prediction, user_data)
-        
-def generate_report(prediction,patient_details):
-    prompt = f"""You have to generate a medical report based on the predicted brain tumor by MRI: {prediction} and the patient details provided below.
-                ### Patient details:
-                1. **Patient Name: {patient_details["Patient Name"]}**
-                2. **Patient Age: {patient_details["Age"]}**
-                3. **Patient Gender: {patient_details["Gender"]}**
-                4. **Patient Symptoms: {patient_details["Symptoms"]}**
-
-                ### Report Instructions:
-                1. **Include the patient details in the header without patient symptoms** as listed above. Each piece of information must be on a separate line (e.g., "Patient Name" on its own line, followed by "Patient Age" on its own line).
-                2. **Do not place any of the details on the same line.** Each detail must appear separately as shown in the list.
-                3. After the patient details, generate a medical report in subsections with each section containing a maximum of 5 lines:
-                    - Diagnosis
-                    - Possible Cause of the condition based on patient details
-                    - Treatment options and recommendations
-                    - Prognosis
-                4. If Patient Symptoms is not empty then analyse those symptoms in diagnosis.
-                5. **Strictly follow these formatting rules**: 
-                    - No bullet points or extra punctuation other than what's necessary for a medical report.
-    """
-
-    response = gen_model.generate_content(
-        contents=prompt
-    )
-    
-    if response._done and response._result and 'candidates' in response._result:
-        report_content = response.text
-        return report_content
-    else:
-        return "Error: Report generation failed."
-
-def generate_and_display_report(prediction,patient_details):
-    report = generate_report(prediction, patient_details)
-    st.markdown("<h2 style='text-align: center; background-color: #17253b'>Generated Report</h2>", unsafe_allow_html=True)
-    st.write(report)
-    st.download_button(
-        label="Download Text Report",
-        data=report,
-        file_name="generated_report.txt",
-        mime="text/plain"
-    )
-
-def stats(logits):
-    
-    probabilities = F.softmax(logits, dim=-1).detach().cpu().numpy()
-    if probabilities.ndim > 1:
-        probabilities = probabilities[0]
-    class_labels = ['glioma', 'meningioma', 'notumor', 'pituitary']
-    probabilities_normalized = probabilities / np.sum(probabilities)
-    percentages = np.round(probabilities_normalized * 100, 2)
-    fig, ax = plt.subplots(figsize=(10, 6))
-    norm = plt.Normalize(vmin=np.min(probabilities_normalized), vmax=np.max(probabilities_normalized))
-    colors = plt.cm.coolwarm(norm(probabilities_normalized))
-    sns.barplot(x=probabilities_normalized, y=class_labels, palette=colors, orient='h', width=0.2, ax=ax)
-    plt.title("Probabilities Window")
-    plt.xlabel("Probability")
-    plt.ylabel("Predictions")
-    for i, percentage in enumerate(percentages):
-        plt.text(probabilities_normalized[i] + 0.02, i, f'{percentage}%', va='center', fontsize=12)
-    st.pyplot(fig)
-st.markdown(
-    """
-    <style>
-    body {
-        background-color: black;
-        color: white;  /* Set text color to white for visibility */
-    }
-    .stApp {
-        background-color: black;
-        color: white;
-    }
-    </style>
-    """,
-    unsafe_allow_html=True
-)
-
-st.markdown("""
-    <style>
-        .stForm {
-            background-color: #ffffff;
-            padding: 20px;
-            border-radius: 10px;
-            box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
-        }
-        .stDownloadButton button{
-            color: #eb4634;
-            border: 1px solid #eb4634
-            border-radius: 8px;      
-        }
-        .stFormSubmitButton button{
-            color: #eb4634;
-            border: 1px solid #eb4634
-            border-radius: 8px;    
-        }
-    </style>
-""", unsafe_allow_html=True)
-
-st.title("Brain Tumor Classification")
-uploaded_file = st.file_uploader("Upload an MRI Image", type=["png", "jpg", "jpeg"])
-if uploaded_file is not None:
-    prediction, outputs = predict(uploaded_file, model, device)
-    image = Image.open(uploaded_file).convert('RGB')
-    saliency_image, blue_image, grad_image = processor(model, uploaded_file, device)
-    # st.image(image, caption="Uploaded Image", width=300)
-    fig, axes = plt.subplots(1,2, figsize=(15,5))
-
-    axes[0].imshow(image)
-    axes[0].set_title("Original Image" , color="white")
-    axes[0].axis('off')
-    axes[1].imshow(blue_image)
-    axes[1].set_title("BW transformation", color="white")
-    axes[1].axis('off')
-    fig.patch.set_facecolor("black")
-    st.pyplot(fig)
-    fig1, axes1 = plt.subplots(1,2, figsize=(15,5))
-
-    axes1[0].imshow(grad_image)
-    axes1[0].set_title("GRAD transformation", color="white")
-    axes1[0].axis('off')
-    axes1[1].imshow(saliency_image)
-    axes1[1].set_title("SALIENT", color="white")
-    axes1[1].axis('off')
-    fig1.patch.set_facecolor("black")
-    st.pyplot(fig1)
-    st.markdown(
-    f"""
-    <div style='
-        margin-top: 20px; 
-        padding: 15px; 
-        background-color: #28292b; 
-        color: white; 
-        border-radius: 5px; 
-        text-align: left; 
-        display: inline-block;
-        width: 100%;'>
-        <h2 style='margin: 0; padding: 0;'>Prediction: <b>{prediction}</b></h2>
-    </div>
-    """,
-    unsafe_allow_html=True
-)
-    # logits = torch.randn(1, 4)  
-    st.markdown("<h1 style='text-align: center;'>Analysis</h1>", unsafe_allow_html=True)
-    stats(outputs)
-    # Report Section
-    createForm(prediction)
-
+import streamlit as st
+import google.generativeai as genai
+import os
+import torch
+from dotenv import load_dotenv
+import torch.nn as nn
+from torchvision import models, transforms
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+from PIL import Image
+from io import BytesIO
+from predictor import *
+from image_processor import *
+import google.generativeai as genai
+import gdown
+# load_dotenv()
+api_key = os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=api_key)
+
+gen_model = genai.GenerativeModel('gemini-1.5-flash-latest')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+@st.cache_resource
+def download_model():
+    file_id = "1Ovlm72q3sa6BxobWb-6QKTAF-uv753kZ"
+    url = f'https://drive.google.com/uc?export=download&id={file_id}'
+    gdown.download(url, 'model.pth', quiet=False)
+    model = models.vgg16(weights = 'VGG16_Weights.DEFAULT')
+    model.classifier[6] = nn.Linear(4096,4)
+    return model
+
+model = download_model()
+
+def createForm(prediction):
+    st.markdown("<h2 style='text-align: center;'>To get the report fill the details</h2>", unsafe_allow_html=True)
+    with st.form(key='user_info_form'):
+        patient_name = st.text_input("Patient Name")
+        patient_age = st.number_input("Age", min_value=0, max_value=120)
+        patient_gender = st.selectbox("Gender", ["Male", "Female", "Other"])
+        patient_symptoms = st.text_area("Other Symptoms", placeholder="Describe the symptoms...")
+        submit_button = st.form_submit_button("Generate Report")
+    if submit_button:
+        if not patient_name:
+            st.error('Patient name is required!!')
+        elif not patient_age:
+            st.error('Patient age is required!!')
+        if patient_symptoms == "":
+            patient_symptoms = 'NIL'
+        user_data = {
+            "Patient Name": patient_name,
+            "Age": patient_age,
+            "Gender": patient_gender,
+            "Symptoms": patient_symptoms
+        }
+        st.markdown(f"### Report for {patient_name}")
+        st.write("(The report generation will be solely based on the symptoms provided and prediction)")
+        generate_and_display_report(prediction, user_data)
+        
+def generate_report(prediction,patient_details):
+    prompt = f"""You have to generate a medical report based on the predicted brain tumor by MRI: {prediction} and the patient details provided below.
+                ### Patient details:
+                1. **Patient Name: {patient_details["Patient Name"]}**
+                2. **Patient Age: {patient_details["Age"]}**
+                3. **Patient Gender: {patient_details["Gender"]}**
+                4. **Patient Symptoms: {patient_details["Symptoms"]}**
+
+                ### Report Instructions:
+                1. **Include the patient details in the header without patient symptoms** as listed above. Each piece of information must be on a separate line (e.g., "Patient Name" on its own line, followed by "Patient Age" on its own line).
+                2. **Do not place any of the details on the same line.** Each detail must appear separately as shown in the list.
+                3. After the patient details, generate a medical report in subsections with each section containing a maximum of 5 lines:
+                    - Diagnosis
+                    - Possible Cause of the condition based on patient details
+                    - Treatment options and recommendations
+                    - Prognosis
+                4. If Patient Symptoms is not empty then analyse those symptoms in diagnosis.
+                5. **Strictly follow these formatting rules**: 
+                    - No bullet points or extra punctuation other than what's necessary for a medical report.
+    """
+
+    response = gen_model.generate_content(
+        contents=prompt
+    )
+    
+    if response._done and response._result and 'candidates' in response._result:
+        report_content = response.text
+        return report_content
+    else:
+        return "Error: Report generation failed."
+
+def generate_and_display_report(prediction,patient_details):
+    report = generate_report(prediction, patient_details)
+    st.markdown("<h2 style='text-align: center; background-color: #17253b'>Generated Report</h2>", unsafe_allow_html=True)
+    st.write(report)
+    st.download_button(
+        label="Download Text Report",
+        data=report,
+        file_name="generated_report.txt",
+        mime="text/plain"
+    )
+
+def stats(logits):
+    
+    probabilities = F.softmax(logits, dim=-1).detach().cpu().numpy()
+    if probabilities.ndim > 1:
+        probabilities = probabilities[0]
+    class_labels = ['glioma', 'meningioma', 'notumor', 'pituitary']
+    probabilities_normalized = probabilities / np.sum(probabilities)
+    percentages = np.round(probabilities_normalized * 100, 2)
+    fig, ax = plt.subplots(figsize=(10, 6))
+    norm = plt.Normalize(vmin=np.min(probabilities_normalized), vmax=np.max(probabilities_normalized))
+    colors = plt.cm.coolwarm(norm(probabilities_normalized))
+    sns.barplot(x=probabilities_normalized, y=class_labels, palette=colors, orient='h', width=0.2, ax=ax)
+    plt.title("Probabilities Window")
+    plt.xlabel("Probability")
+    plt.ylabel("Predictions")
+    for i, percentage in enumerate(percentages):
+        plt.text(probabilities_normalized[i] + 0.02, i, f'{percentage}%', va='center', fontsize=12)
+    st.pyplot(fig)
+st.markdown(
+    """
+    <style>
+    body {
+        background-color: black;
+        color: white;  /* Set text color to white for visibility */
+    }
+    .stApp {
+        background-color: black;
+        color: white;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+st.markdown("""
+    <style>
+        .stForm {
+            background-color: #ffffff;
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
+        }
+        .stDownloadButton button{
+            color: #eb4634;
+            border: 1px solid #eb4634
+            border-radius: 8px;      
+        }
+        .stFormSubmitButton button{
+            color: #eb4634;
+            border: 1px solid #eb4634
+            border-radius: 8px;    
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+st.title("Brain Tumor Classification")
+uploaded_file = st.file_uploader("Upload an MRI Image", type=["png", "jpg", "jpeg"])
+if uploaded_file is not None:
+    prediction, outputs = predict(uploaded_file, model, device)
+    image = Image.open(uploaded_file).convert('RGB')
+    saliency_image, blue_image, grad_image = processor(model, uploaded_file, device)
+    # st.image(image, caption="Uploaded Image", width=300)
+    fig, axes = plt.subplots(1,2, figsize=(15,5))
+
+    axes[0].imshow(image)
+    axes[0].set_title("Original Image" , color="white")
+    axes[0].axis('off')
+    axes[1].imshow(blue_image)
+    axes[1].set_title("BW transformation", color="white")
+    axes[1].axis('off')
+    fig.patch.set_facecolor("black")
+    st.pyplot(fig)
+    fig1, axes1 = plt.subplots(1,2, figsize=(15,5))
+
+    axes1[0].imshow(grad_image)
+    axes1[0].set_title("GRAD transformation", color="white")
+    axes1[0].axis('off')
+    axes1[1].imshow(saliency_image)
+    axes1[1].set_title("SALIENT", color="white")
+    axes1[1].axis('off')
+    fig1.patch.set_facecolor("black")
+    st.pyplot(fig1)
+    st.markdown(
+    f"""
+    <div style='
+        margin-top: 20px; 
+        padding: 15px; 
+        background-color: #28292b; 
+        color: white; 
+        border-radius: 5px; 
+        text-align: left; 
+        display: inline-block;
+        width: 100%;'>
+        <h2 style='margin: 0; padding: 0;'>Prediction: <b>{prediction}</b></h2>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+    # logits = torch.randn(1, 4)  
+    st.markdown("<h1 style='text-align: center;'>Analysis</h1>", unsafe_allow_html=True)
+    stats(outputs)
+    # Report Section
+    createForm(prediction)
+