Update app.py

app.py

@@ -5,6 +5,12 @@ import pandas as pd
 from groq import Groq
 from PIL import Image
 import io
+import datetime
+import re
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
 
 # Initialize Groq client
 client = Groq(
@@ -17,8 +23,44 @@ def encode_image(image):
     image.save(buffered, format="JPEG")
     return base64.b64encode(buffered.getvalue()).decode('utf-8')
 
+# Process patient history file
+def process_patient_history(file):
+    if file is None:
+        return ""
+    
+    try:
+        # Check file extension
+        file_ext = os.path.splitext(file.name)[1].lower()
+        
+        if file_ext == '.txt':
+            # Read text file
+            content = file.read().decode('utf-8')
+            return content
+        
+        elif file_ext in ['.csv', '.xlsx', '.xls']:
+            # Read spreadsheet file
+            if file_ext == '.csv':
+                df = pd.read_csv(file.name)
+            else:
+                df = pd.read_excel(file.name)
+            
+            # Convert dataframe to formatted string
+            formatted_data = "PATIENT INFORMATION:\n\n"
+            for column in df.columns:
+                formatted_data += f"{column}: {df.iloc[0][column]}\n"
+            
+            return formatted_data
+        
+        else:
+            return "Unsupported file format. Please upload a .txt, .csv, or .xlsx file."
+    
+    except Exception as e:
+        return f"Error processing patient history file: {str(e)}"
+
 # Extract ECG readings from image using Llama Vision model
 def analyze_ecg_image(image, vision_model="llama-3.2-90b-vision-preview"):
+    # Fixed model - always use llama-3.2-90b-vision-preview
+    vision_model = "llama-3.2-90b-vision-preview"
     if image is None:
         return "No image provided."
     
@@ -29,8 +71,32 @@ def analyze_ecg_image(image, vision_model="llama-3.2-90b-vision-preview"):
     # Encode the image
     base64_image = encode_image(image)
     
+    # Get current timestamp
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
     # Create chat completion with vision model
-    vision_prompt = "Analyze this ECG image carefully. Extract and report all visible parameters, intervals, rhythm patterns, and any abnormalities. Report exact numerical values where visible. Format your response as a structured report with clear sections for different measurements and observations."
+    vision_prompt = f"""Analyze this ECG image carefully. You are a cardiologist analyzing an electrocardiogram (ECG).
+    
+    Extract and report all visible parameters, including but not limited to:
+    1. Heart rate
+    2. PR interval
+    3. QRS duration
+    4. QT/QTc interval
+    5. P wave morphology
+    6. ST segment changes
+    7. T wave morphology
+    8. Rhythm classification
+    9. Specific patterns (if any)
+    
+    Report exact numerical values where visible. Format your response as a structured report with clear sections for different measurements and observations. If certain measurements aren't visible in the image, indicate that they cannot be determined.
+    
+    If you notice any abnormalities or concerning patterns, highlight them clearly but avoid making definitive diagnoses.
+    
+    Important formatting instructions:
+    - Use proper HTML/Markdown formatting with <strong> tags for headings or important findings
+    - Do not use asterisks (**) for emphasis - use proper HTML formatting instead
+    - Include this timestamp at the top of your report: {timestamp}
+    """
     
     try:
         vision_completion = client.chat.completions.create(
@@ -54,18 +120,328 @@ def analyze_ecg_image(image, vision_model="llama-3.2-90b-vision-preview"):
         )
         
         ecg_analysis = vision_completion.choices[0].message.content
+                
+        # Process the response to convert any remaining ** to HTML tags
+        ecg_analysis = re.sub(r'\*\*([^*]+)\*\*', r'<strong>\1</strong>', ecg_analysis)
+        
+        # Make sure all headers are properly formatted
+        ecg_analysis = re.sub(r'^(#+)\s+(.+)
+
+# Generate medical assessment based on ECG readings and patient history
+def generate_assessment(ecg_analysis, patient_history=None, chat_model="llama-3.3-70b-versatile"):
+    # Fixed model - always use llama-3.3-70b-versatile
+    chat_model = "llama-3.3-70b-versatile"
+    if not ecg_analysis or ecg_analysis.startswith("Error"):
+        return "Please analyze an ECG image first."
+    
+    # Get current timestamp
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
+    # Construct prompt based on available information
+    if patient_history and patient_history.strip():
+        prompt = f"""You are a highly trained cardiologist assistant. Based on the ECG analysis below and the patient's history, provide a comprehensive assessment of the patient's cardiac status. Indicate clearly if there are any concerning findings that require immediate medical attention.
+
+ECG ANALYSIS:
+{ecg_analysis}
+
+PATIENT HISTORY:
+{patient_history}
+
+TIMESTAMP: {timestamp}
+
+Provide your assessment with proper formatting:
+<strong>Summary of Findings</strong>
+(Your summary here)
+
+<strong>Key Abnormalities</strong>
+(List any abnormalities here)
+
+<strong>Potential Clinical Implications</strong>
+(Describe implications here)
+
+<strong>Recommendation</strong>
+(Include urgency level)
+
+<strong>Differential Considerations</strong>
+(List differentials here)
+
+Important formatting instructions:
+- Use proper HTML formatting with <strong> tags for headings
+- Do not use asterisks (**) for emphasis - use proper HTML formatting instead
+- For any urgent findings, use <strong style="color:red"> to highlight them
+"""
+    else:
+        prompt = f"""You are a highly trained cardiologist assistant. Based on the ECG analysis below, provide a comprehensive assessment of the patient's cardiac status. Indicate clearly if there are any concerning findings that require immediate medical attention.
+
+ECG ANALYSIS:
+{ecg_analysis}
+
+TIMESTAMP: {timestamp}
+
+Provide your assessment with proper formatting:
+<strong>Summary of Findings</strong>
+(Your summary here)
+
+<strong>Key Abnormalities</strong>
+(List any abnormalities here)
+
+<strong>Potential Clinical Implications</strong>
+(Describe implications here)
+
+<strong>Recommendation</strong>
+(Include urgency level)
+
+<strong>Differential Considerations</strong>
+(List differentials here)
+
+Important formatting instructions:
+- Use proper HTML formatting with <strong> tags for headings
+- Do not use asterisks (**) for emphasis - use proper HTML formatting instead
+- For any urgent findings, use <strong style="color:red"> to highlight them
+"""
+    
+    try:
+        assessment_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are a medical AI assistant specialized in cardiology. Provide accurate, clinically relevant interpretations of ECG data. If there are concerning findings that might indicate a medical emergency, clearly highlight them. Avoid definitive diagnoses but provide reasoned medical assessments based on the data provided."
+                },
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ],
+            model=chat_model,
+            temperature=0.2,  # Lower temperature for more factual responses
+            max_completion_tokens=2048,
+        )
+        
+        assessment_text = assessment_completion.choices[0].message.content
+        
+        # Process the response to convert any remaining ** to HTML tags
+        assessment_text = re.sub(r'\*\*([^*]+)\*\*', r'<strong>\1</strong>', assessment_text)
+        
+        # Make sure all headers are properly formatted
+        assessment_text = re.sub(r'^(#+)\s+(.+)
+
+# Doctor's chat interaction with the model about the patient
+def doctor_chat(message, chat_history, ecg_analysis, patient_history, assessment, chat_model="llama-3.3-70b-versatile"):
+    # Fixed model - always use llama-3.3-70b-versatile
+    chat_model = "llama-3.3-70b-versatile"
+    if not ecg_analysis or ecg_analysis.startswith("Error"):
+        return "Please analyze an ECG image first before starting a chat.", chat_history
+    
+    if not message.strip():
+        return "", chat_history
+    
+    # Get current timestamp
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
+    # Prepare chat context
+    context = f"""ECG ANALYSIS:
+{ecg_analysis}
+
+MEDICAL ASSESSMENT:
+{assessment}
+
+TIMESTAMP: {timestamp}
+
+"""
+    
+    if patient_history and patient_history.strip():
+        context += f"""PATIENT HISTORY:
+{patient_history}
+
+"""
+    
+    # Construct full chat history for context
+    messages = [
+        {
+            "role": "system",
+            "content": f"You are a medical AI assistant specialized in cardiology. You are helping a doctor interpret ECG results and patient data. Answer the doctor's questions based on the following information:\n\n{context}"
+        }
+    ]
+    
+    # Add chat history to the context (limited to last 10 exchanges to avoid token limits)
+    for entry in chat_history[-10:]:
+        messages.append({"role": "user", "content": entry[0]})
+        messages.append({"role": "assistant", "content": entry[1]})
+    
+    # Add the current message
+    messages.append({"role": "user", "content": message})
+    
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=messages,
+            model=chat_model,
+            temperature=0.3,
+            max_completion_tokens=1024,
+        )
+        
+        response = chat_completion.choices[0].message.content
+        chat_history.append((message, response))
+        return "", chat_history
+    
+    except Exception as e:
+        error_message = f"Error in chat: {str(e)}"
+        chat_history.append((message, error_message))
+        return "", chat_history
+
+# Create Gradio interface
+with gr.Blocks(title="Cardiac ECG Analysis System", theme=gr.themes.Soft()) as app:
+    # Session state to store data
+    ecg_analysis_state = gr.State("")
+    
+    gr.Markdown("# 🫀 Cardiac ECG Analysis System")
+    gr.Markdown("Upload an ECG image and optional patient history to get an automated analysis and assessment.")
+    
+    with gr.Tabs():
+        with gr.TabItem("💻 Main Interface"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    # Input components
+                    with gr.Box():
+                        gr.Markdown("### 📊 ECG Image")
+                        ecg_image = gr.Image(type="pil", label="Upload ECG Image")
+                        # Display fixed model info
+                        gr.Markdown("**Vision Model:** llama-3.2-90b-vision-preview")
+                        analyze_button = gr.Button("Analyze ECG Image", variant="primary")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 📋 Patient Information")
+                        patient_history_text = gr.Textbox(
+                            lines=8, 
+                            label="Patient History (Manual Entry)", 
+                            placeholder="Enter patient's medical history, age, sex, symptoms, medications, etc."
+                        )
+                        patient_history_file = gr.File(
+                            label="Upload Patient History File (Optional, .txt, .csv, or .xlsx)",
+                            file_types=[".txt", ".csv", ".xlsx", ".xls"]
+                        )
+                        load_history_button = gr.Button("Load Patient History from File")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 🧠 Assessment Settings")
+                        # Display fixed model info
+                        gr.Markdown("**Chat Model:** llama-3.3-70b-versatile")
+                        assess_button = gr.Button("Generate Assessment", variant="primary")
+                
+                with gr.Column(scale=1):
+                    # Output components
+                    with gr.Box():
+                        gr.Markdown("### 📈 ECG Analysis Results")
+                        ecg_analysis_output = gr.HTML(label="ECG Analysis", elem_id="ecg-analysis")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 📝 Medical Assessment")
+                        assessment_output = gr.HTML(label="Assessment", elem_id="assessment-output")
+            
+            gr.Markdown("## 👨‍⚕️ Doctor's Consultation")
+            gr.Markdown("Ask follow-up questions about the patient's ECG results and medical condition.")
+            
+            with gr.Box():
+                chatbot = gr.Chatbot(label="Consultation", height=400)
+                with gr.Row():
+                    message = gr.Textbox(
+                        lines=2, 
+                        label="Doctor's Question",
+                        placeholder="Ask a question about this patient's cardiac status...",
+                        scale=4
+                    )
+                    chat_button = gr.Button("Send", scale=1, variant="primary")
+        
+        with gr.TabItem("ℹ️ Instructions"):
+            gr.Markdown("""
+            ## How to Use This Application
+            
+            ### Step 1: Upload and Analyze ECG
+            1. Upload an ECG image using the file uploader in the Main Interface tab
+            2. Select the vision model (90b recommended for best results)
+            3. Click "Analyze ECG Image" to extract readings from the image
+            
+            ### Step 2: Add Patient Information (Optional)
+            - Enter patient history directly in the text box, OR
+            - Upload a patient history file (.txt, .csv, or .xlsx) and click "Load Patient History from File"
+            
+            ### Step 3: Generate Assessment
+            1. Select the chat model (70b recommended for detailed analysis)
+            2. Click "Generate Assessment" to get an AI-assisted interpretation
+            
+            ### Step 4: Consultation
+            - Use the chatbot interface to ask follow-up questions
+            - The AI will consider the ECG analysis, patient history, and previous assessment in its responses
+            
+            ### Important Notes
+            - This tool is designed to assist healthcare professionals, not replace clinical judgment
+            - Always validate AI-generated medical interpretations with proper medical expertise
+            - Patient data privacy should be maintained according to relevant regulations
+            """)
+    
+    # Set up event handlers
+    analyze_button.click(
+        analyze_ecg_image, 
+        inputs=[ecg_image], 
+        outputs=ecg_analysis_output
+    ).then(
+        lambda x: x,  # Pass through function to update state
+        inputs=ecg_analysis_output,
+        outputs=ecg_analysis_state
+    )
+    
+    def process_and_update_history(file):
+        if file is None:
+            return "No file uploaded."
+        processed_text = process_patient_history(file)
+        return processed_text
+    
+    load_history_button.click(
+        process_and_update_history,
+        inputs=[patient_history_file],
+        outputs=[patient_history_text]
+    )
+    
+    assess_button.click(
+        generate_assessment, 
+        inputs=[ecg_analysis_output, patient_history_text], 
+        outputs=assessment_output
+    )
+    
+    chat_button.click(
+        doctor_chat, 
+        inputs=[message, chatbot, ecg_analysis_output, patient_history_text, assessment_output], 
+        outputs=[message, chatbot]
+    )
+    
+    # Also trigger chat on Enter key
+    message.submit(
+        doctor_chat,
+        inputs=[message, chatbot, ecg_analysis_output, patient_history_text, assessment_output],
+        outputs=[message, chatbot]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch()
+, r'<strong>\2</strong>', ecg_analysis, flags=re.MULTILINE)
+        
         return ecg_analysis
     
     except Exception as e:
-        return f"Error analyzing ECG image: {str(e)}"
+        return f"<strong style='color:red'>Error analyzing ECG image:</strong> {str(e)}"
 
 # Generate medical assessment based on ECG readings and patient history
 def generate_assessment(ecg_analysis, patient_history=None, chat_model="llama-3.3-70b-versatile"):
+    # Fixed model - always use llama-3.3-70b-versatile
+    chat_model = "llama-3.3-70b-versatile"
     if not ecg_analysis or ecg_analysis.startswith("Error"):
         return "Please analyze an ECG image first."
     
+    # Get current timestamp
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
     # Construct prompt based on available information
-    if patient_history:
+    if patient_history and patient_history.strip():
         prompt = f"""You are a highly trained cardiologist assistant. Based on the ECG analysis below and the patient's history, provide a comprehensive assessment of the patient's cardiac status. Indicate clearly if there are any concerning findings that require immediate medical attention.
 
 ECG ANALYSIS:
@@ -74,11 +450,28 @@ ECG ANALYSIS:
 PATIENT HISTORY:
 {patient_history}
 
-Provide your assessment in the following format:
-1. Summary of findings
-2. Key abnormalities (if any)
-3. Potential clinical implications
-4. Recommendation (including urgency level)
+TIMESTAMP: {timestamp}
+
+Provide your assessment with proper formatting:
+<strong>Summary of Findings</strong>
+(Your summary here)
+
+<strong>Key Abnormalities</strong>
+(List any abnormalities here)
+
+<strong>Potential Clinical Implications</strong>
+(Describe implications here)
+
+<strong>Recommendation</strong>
+(Include urgency level)
+
+<strong>Differential Considerations</strong>
+(List differentials here)
+
+Important formatting instructions:
+- Use proper HTML formatting with <strong> tags for headings
+- Do not use asterisks (**) for emphasis - use proper HTML formatting instead
+- For any urgent findings, use <strong style="color:red"> to highlight them
 """
     else:
         prompt = f"""You are a highly trained cardiologist assistant. Based on the ECG analysis below, provide a comprehensive assessment of the patient's cardiac status. Indicate clearly if there are any concerning findings that require immediate medical attention.
@@ -86,11 +479,28 @@ Provide your assessment in the following format:
 ECG ANALYSIS:
 {ecg_analysis}
 
-Provide your assessment in the following format:
-1. Summary of findings
-2. Key abnormalities (if any)
-3. Potential clinical implications
-4. Recommendation (including urgency level)
+TIMESTAMP: {timestamp}
+
+Provide your assessment with proper formatting:
+<strong>Summary of Findings</strong>
+(Your summary here)
+
+<strong>Key Abnormalities</strong>
+(List any abnormalities here)
+
+<strong>Potential Clinical Implications</strong>
+(Describe implications here)
+
+<strong>Recommendation</strong>
+(Include urgency level)
+
+<strong>Differential Considerations</strong>
+(List differentials here)
+
+Important formatting instructions:
+- Use proper HTML formatting with <strong> tags for headings
+- Do not use asterisks (**) for emphasis - use proper HTML formatting instead
+- For any urgent findings, use <strong style="color:red"> to highlight them
 """
     
     try:
@@ -116,17 +526,30 @@ Provide your assessment in the following format:
         return f"Error generating assessment: {str(e)}"
 
 # Doctor's chat interaction with the model about the patient
-def doctor_chat(message, chat_history, ecg_analysis, patient_history, chat_model="llama-3.3-70b-versatile"):
+def doctor_chat(message, chat_history, ecg_analysis, patient_history, assessment, chat_model="llama-3.3-70b-versatile"):
+    # Fixed model - always use llama-3.3-70b-versatile
+    chat_model = "llama-3.3-70b-versatile"
     if not ecg_analysis or ecg_analysis.startswith("Error"):
         return "Please analyze an ECG image first before starting a chat.", chat_history
     
+    if not message.strip():
+        return "", chat_history
+    
+    # Get current timestamp
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
     # Prepare chat context
     context = f"""ECG ANALYSIS:
 {ecg_analysis}
 
+MEDICAL ASSESSMENT:
+{assessment}
+
+TIMESTAMP: {timestamp}
+
 """
     
-    if patient_history:
+    if patient_history and patient_history.strip():
         context += f"""PATIENT HISTORY:
 {patient_history}
 
@@ -140,8 +563,8 @@ def doctor_chat(message, chat_history, ecg_analysis, patient_history, chat_model
         }
     ]
     
-    # Add chat history to the context
-    for entry in chat_history:
+    # Add chat history to the context (limited to last 10 exchanges to avoid token limits)
+    for entry in chat_history[-10:]:
         messages.append({"role": "user", "content": entry[0]})
         messages.append({"role": "assistant", "content": entry[1]})
     
@@ -166,66 +589,639 @@ def doctor_chat(message, chat_history, ecg_analysis, patient_history, chat_model
         return "", chat_history
 
 # Create Gradio interface
-with gr.Blocks(title="Cardiac ECG Analysis System") as app:
-    gr.Markdown("# Cardiac ECG Analysis System")
+with gr.Blocks(title="Cardiac ECG Analysis System", theme=gr.themes.Soft()) as app:
+    # Session state to store data
+    ecg_analysis_state = gr.State("")
+    
+    gr.Markdown("# 🫀 Cardiac ECG Analysis System")
     gr.Markdown("Upload an ECG image and optional patient history to get an automated analysis and assessment.")
     
-    with gr.Row():
-        with gr.Column(scale=1):
-            # Input components
-            ecg_image = gr.Image(type="pil", label="Upload ECG Image")
-            vision_model = gr.Dropdown(
-                choices=["llama-3.2-90b-vision-preview", "llama-3.2-11b-vision-preview"], 
-                value="llama-3.2-90b-vision-preview", 
-                label="Vision Model"
-            )
-            analyze_button = gr.Button("Analyze ECG Image")
-            
-            patient_history = gr.Textbox(
-                lines=10, 
-                label="Patient History (optional)", 
-                placeholder="Enter patient's medical history, age, sex, symptoms, medications, etc."
-            )
-            
-            chat_model = gr.Dropdown(
-                choices=["llama-3.3-70b-versatile", "llama-3.3-8b-versatile"], 
-                value="llama-3.3-70b-versatile", 
-                label="Chat Model"
-            )
-            assess_button = gr.Button("Generate Assessment")
-        
-        with gr.Column(scale=1):
-            # Output components
-            ecg_analysis_output = gr.Textbox(label="ECG Analysis", lines=15)
-            assessment_output = gr.Textbox(label="Medical Assessment", lines=15)
-    
-    gr.Markdown("## Doctor's Consultation")
-    gr.Markdown("Ask follow-up questions about the patient's ECG results and medical condition.")
-    
-    chatbot = gr.Chatbot(label="Consultation")
-    message = gr.Textbox(
-        lines=2, 
-        label="Doctor's Question",
-        placeholder="Ask a question about this patient's cardiac status..."
-    )
-    chat_button = gr.Button("Send")
+    with gr.Tabs():
+        with gr.TabItem("💻 Main Interface"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    # Input components
+                    with gr.Box():
+                        gr.Markdown("### 📊 ECG Image")
+                        ecg_image = gr.Image(type="pil", label="Upload ECG Image")
+                        # Display fixed model info
+                        gr.Markdown("**Vision Model:** llama-3.2-90b-vision-preview")
+                        analyze_button = gr.Button("Analyze ECG Image", variant="primary")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 📋 Patient Information")
+                        patient_history_text = gr.Textbox(
+                            lines=8, 
+                            label="Patient History (Manual Entry)", 
+                            placeholder="Enter patient's medical history, age, sex, symptoms, medications, etc."
+                        )
+                        patient_history_file = gr.File(
+                            label="Upload Patient History File (Optional, .txt, .csv, or .xlsx)",
+                            file_types=[".txt", ".csv", ".xlsx", ".xls"]
+                        )
+                        load_history_button = gr.Button("Load Patient History from File")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 🧠 Assessment Settings")
+                        # Display fixed model info
+                        gr.Markdown("**Chat Model:** llama-3.3-70b-versatile")
+                        assess_button = gr.Button("Generate Assessment", variant="primary")
+                
+                with gr.Column(scale=1):
+                    # Output components
+                    with gr.Box():
+                        gr.Markdown("### 📈 ECG Analysis Results")
+                        ecg_analysis_output = gr.HTML(label="ECG Analysis", elem_id="ecg-analysis")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 📝 Medical Assessment")
+                        assessment_output = gr.HTML(label="Assessment", elem_id="assessment-output")
+            
+            gr.Markdown("## 👨‍⚕️ Doctor's Consultation")
+            gr.Markdown("Ask follow-up questions about the patient's ECG results and medical condition.")
+            
+            with gr.Box():
+                chatbot = gr.Chatbot(label="Consultation", height=400)
+                with gr.Row():
+                    message = gr.Textbox(
+                        lines=2, 
+                        label="Doctor's Question",
+                        placeholder="Ask a question about this patient's cardiac status...",
+                        scale=4
+                    )
+                    chat_button = gr.Button("Send", scale=1, variant="primary")
+        
+        with gr.TabItem("ℹ️ Instructions"):
+            gr.Markdown("""
+            ## How to Use This Application
+            
+            ### Step 1: Upload and Analyze ECG
+            1. Upload an ECG image using the file uploader in the Main Interface tab
+            2. Select the vision model (90b recommended for best results)
+            3. Click "Analyze ECG Image" to extract readings from the image
+            
+            ### Step 2: Add Patient Information (Optional)
+            - Enter patient history directly in the text box, OR
+            - Upload a patient history file (.txt, .csv, or .xlsx) and click "Load Patient History from File"
+            
+            ### Step 3: Generate Assessment
+            1. Select the chat model (70b recommended for detailed analysis)
+            2. Click "Generate Assessment" to get an AI-assisted interpretation
+            
+            ### Step 4: Consultation
+            - Use the chatbot interface to ask follow-up questions
+            - The AI will consider the ECG analysis, patient history, and previous assessment in its responses
+            
+            ### Important Notes
+            - This tool is designed to assist healthcare professionals, not replace clinical judgment
+            - Always validate AI-generated medical interpretations with proper medical expertise
+            - Patient data privacy should be maintained according to relevant regulations
+            """)
     
     # Set up event handlers
     analyze_button.click(
         analyze_ecg_image, 
-        inputs=[ecg_image, vision_model], 
+        inputs=[ecg_image], 
         outputs=ecg_analysis_output
+    ).then(
+        lambda x: x,  # Pass through function to update state
+        inputs=ecg_analysis_output,
+        outputs=ecg_analysis_state
+    )
+    
+    def process_and_update_history(file):
+        if file is None:
+            return "No file uploaded."
+        processed_text = process_patient_history(file)
+        return processed_text
+    
+    load_history_button.click(
+        process_and_update_history,
+        inputs=[patient_history_file],
+        outputs=[patient_history_text]
     )
     
     assess_button.click(
         generate_assessment, 
-        inputs=[ecg_analysis_output, patient_history, chat_model], 
+        inputs=[ecg_analysis_output, patient_history_text], 
         outputs=assessment_output
     )
     
     chat_button.click(
         doctor_chat, 
-        inputs=[message, chatbot, ecg_analysis_output, patient_history, chat_model], 
+        inputs=[message, chatbot, ecg_analysis_output, patient_history_text, assessment_output], 
+        outputs=[message, chatbot]
+    )
+    
+    # Also trigger chat on Enter key
+    message.submit(
+        doctor_chat,
+        inputs=[message, chatbot, ecg_analysis_output, patient_history_text, assessment_output],
+        outputs=[message, chatbot]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch()
+, r'<strong>\2</strong>', assessment_text, flags=re.MULTILINE)
+        
+        return assessment_text
+    
+    except Exception as e:
+        return f"<strong style='color:red'>Error generating assessment:</strong> {str(e)}"
+
+# Doctor's chat interaction with the model about the patient
+def doctor_chat(message, chat_history, ecg_analysis, patient_history, assessment, chat_model="llama-3.3-70b-versatile"):
+    # Fixed model - always use llama-3.3-70b-versatile
+    chat_model = "llama-3.3-70b-versatile"
+    if not ecg_analysis or ecg_analysis.startswith("Error"):
+        return "Please analyze an ECG image first before starting a chat.", chat_history
+    
+    if not message.strip():
+        return "", chat_history
+    
+    # Get current timestamp
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
+    # Prepare chat context
+    context = f"""ECG ANALYSIS:
+{ecg_analysis}
+
+MEDICAL ASSESSMENT:
+{assessment}
+
+TIMESTAMP: {timestamp}
+
+"""
+    
+    if patient_history and patient_history.strip():
+        context += f"""PATIENT HISTORY:
+{patient_history}
+
+"""
+    
+    # Construct full chat history for context
+    messages = [
+        {
+            "role": "system",
+            "content": f"You are a medical AI assistant specialized in cardiology. You are helping a doctor interpret ECG results and patient data. Answer the doctor's questions based on the following information:\n\n{context}"
+        }
+    ]
+    
+    # Add chat history to the context (limited to last 10 exchanges to avoid token limits)
+    for entry in chat_history[-10:]:
+        messages.append({"role": "user", "content": entry[0]})
+        messages.append({"role": "assistant", "content": entry[1]})
+    
+    # Add the current message
+    messages.append({"role": "user", "content": message})
+    
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=messages,
+            model=chat_model,
+            temperature=0.3,
+            max_completion_tokens=1024,
+        )
+        
+        response = chat_completion.choices[0].message.content
+        chat_history.append((message, response))
+        return "", chat_history
+    
+    except Exception as e:
+        error_message = f"Error in chat: {str(e)}"
+        chat_history.append((message, error_message))
+        return "", chat_history
+
+# Create Gradio interface
+with gr.Blocks(title="Cardiac ECG Analysis System", theme=gr.themes.Soft()) as app:
+    # Session state to store data
+    ecg_analysis_state = gr.State("")
+    
+    gr.Markdown("# 🫀 Cardiac ECG Analysis System")
+    gr.Markdown("Upload an ECG image and optional patient history to get an automated analysis and assessment.")
+    
+    with gr.Tabs():
+        with gr.TabItem("💻 Main Interface"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    # Input components
+                    with gr.Box():
+                        gr.Markdown("### 📊 ECG Image")
+                        ecg_image = gr.Image(type="pil", label="Upload ECG Image")
+                        # Display fixed model info
+                        gr.Markdown("**Vision Model:** llama-3.2-90b-vision-preview")
+                        analyze_button = gr.Button("Analyze ECG Image", variant="primary")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 📋 Patient Information")
+                        patient_history_text = gr.Textbox(
+                            lines=8, 
+                            label="Patient History (Manual Entry)", 
+                            placeholder="Enter patient's medical history, age, sex, symptoms, medications, etc."
+                        )
+                        patient_history_file = gr.File(
+                            label="Upload Patient History File (Optional, .txt, .csv, or .xlsx)",
+                            file_types=[".txt", ".csv", ".xlsx", ".xls"]
+                        )
+                        load_history_button = gr.Button("Load Patient History from File")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 🧠 Assessment Settings")
+                        # Display fixed model info
+                        gr.Markdown("**Chat Model:** llama-3.3-70b-versatile")
+                        assess_button = gr.Button("Generate Assessment", variant="primary")
+                
+                with gr.Column(scale=1):
+                    # Output components
+                    with gr.Box():
+                        gr.Markdown("### 📈 ECG Analysis Results")
+                        ecg_analysis_output = gr.HTML(label="ECG Analysis", elem_id="ecg-analysis")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 📝 Medical Assessment")
+                        assessment_output = gr.HTML(label="Assessment", elem_id="assessment-output")
+            
+            gr.Markdown("## 👨‍⚕️ Doctor's Consultation")
+            gr.Markdown("Ask follow-up questions about the patient's ECG results and medical condition.")
+            
+            with gr.Box():
+                chatbot = gr.Chatbot(label="Consultation", height=400)
+                with gr.Row():
+                    message = gr.Textbox(
+                        lines=2, 
+                        label="Doctor's Question",
+                        placeholder="Ask a question about this patient's cardiac status...",
+                        scale=4
+                    )
+                    chat_button = gr.Button("Send", scale=1, variant="primary")
+        
+        with gr.TabItem("ℹ️ Instructions"):
+            gr.Markdown("""
+            ## How to Use This Application
+            
+            ### Step 1: Upload and Analyze ECG
+            1. Upload an ECG image using the file uploader in the Main Interface tab
+            2. Select the vision model (90b recommended for best results)
+            3. Click "Analyze ECG Image" to extract readings from the image
+            
+            ### Step 2: Add Patient Information (Optional)
+            - Enter patient history directly in the text box, OR
+            - Upload a patient history file (.txt, .csv, or .xlsx) and click "Load Patient History from File"
+            
+            ### Step 3: Generate Assessment
+            1. Select the chat model (70b recommended for detailed analysis)
+            2. Click "Generate Assessment" to get an AI-assisted interpretation
+            
+            ### Step 4: Consultation
+            - Use the chatbot interface to ask follow-up questions
+            - The AI will consider the ECG analysis, patient history, and previous assessment in its responses
+            
+            ### Important Notes
+            - This tool is designed to assist healthcare professionals, not replace clinical judgment
+            - Always validate AI-generated medical interpretations with proper medical expertise
+            - Patient data privacy should be maintained according to relevant regulations
+            """)
+    
+    # Set up event handlers
+    analyze_button.click(
+        analyze_ecg_image, 
+        inputs=[ecg_image], 
+        outputs=ecg_analysis_output
+    ).then(
+        lambda x: x,  # Pass through function to update state
+        inputs=ecg_analysis_output,
+        outputs=ecg_analysis_state
+    )
+    
+    def process_and_update_history(file):
+        if file is None:
+            return "No file uploaded."
+        processed_text = process_patient_history(file)
+        return processed_text
+    
+    load_history_button.click(
+        process_and_update_history,
+        inputs=[patient_history_file],
+        outputs=[patient_history_text]
+    )
+    
+    assess_button.click(
+        generate_assessment, 
+        inputs=[ecg_analysis_output, patient_history_text], 
+        outputs=assessment_output
+    )
+    
+    chat_button.click(
+        doctor_chat, 
+        inputs=[message, chatbot, ecg_analysis_output, patient_history_text, assessment_output], 
+        outputs=[message, chatbot]
+    )
+    
+    # Also trigger chat on Enter key
+    message.submit(
+        doctor_chat,
+        inputs=[message, chatbot, ecg_analysis_output, patient_history_text, assessment_output],
+        outputs=[message, chatbot]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch()
+, r'<strong>\2</strong>', ecg_analysis, flags=re.MULTILINE)
+        
+        return ecg_analysis
+    
+    except Exception as e:
+        return f"<strong style='color:red'>Error analyzing ECG image:</strong> {str(e)}"
+
+# Generate medical assessment based on ECG readings and patient history
+def generate_assessment(ecg_analysis, patient_history=None, chat_model="llama-3.3-70b-versatile"):
+    # Fixed model - always use llama-3.3-70b-versatile
+    chat_model = "llama-3.3-70b-versatile"
+    if not ecg_analysis or ecg_analysis.startswith("Error"):
+        return "Please analyze an ECG image first."
+    
+    # Get current timestamp
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
+    # Construct prompt based on available information
+    if patient_history and patient_history.strip():
+        prompt = f"""You are a highly trained cardiologist assistant. Based on the ECG analysis below and the patient's history, provide a comprehensive assessment of the patient's cardiac status. Indicate clearly if there are any concerning findings that require immediate medical attention.
+
+ECG ANALYSIS:
+{ecg_analysis}
+
+PATIENT HISTORY:
+{patient_history}
+
+TIMESTAMP: {timestamp}
+
+Provide your assessment with proper formatting:
+<strong>Summary of Findings</strong>
+(Your summary here)
+
+<strong>Key Abnormalities</strong>
+(List any abnormalities here)
+
+<strong>Potential Clinical Implications</strong>
+(Describe implications here)
+
+<strong>Recommendation</strong>
+(Include urgency level)
+
+<strong>Differential Considerations</strong>
+(List differentials here)
+
+Important formatting instructions:
+- Use proper HTML formatting with <strong> tags for headings
+- Do not use asterisks (**) for emphasis - use proper HTML formatting instead
+- For any urgent findings, use <strong style="color:red"> to highlight them
+"""
+    else:
+        prompt = f"""You are a highly trained cardiologist assistant. Based on the ECG analysis below, provide a comprehensive assessment of the patient's cardiac status. Indicate clearly if there are any concerning findings that require immediate medical attention.
+
+ECG ANALYSIS:
+{ecg_analysis}
+
+TIMESTAMP: {timestamp}
+
+Provide your assessment with proper formatting:
+<strong>Summary of Findings</strong>
+(Your summary here)
+
+<strong>Key Abnormalities</strong>
+(List any abnormalities here)
+
+<strong>Potential Clinical Implications</strong>
+(Describe implications here)
+
+<strong>Recommendation</strong>
+(Include urgency level)
+
+<strong>Differential Considerations</strong>
+(List differentials here)
+
+Important formatting instructions:
+- Use proper HTML formatting with <strong> tags for headings
+- Do not use asterisks (**) for emphasis - use proper HTML formatting instead
+- For any urgent findings, use <strong style="color:red"> to highlight them
+"""
+    
+    try:
+        assessment_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are a medical AI assistant specialized in cardiology. Provide accurate, clinically relevant interpretations of ECG data. If there are concerning findings that might indicate a medical emergency, clearly highlight them. Avoid definitive diagnoses but provide reasoned medical assessments based on the data provided."
+                },
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ],
+            model=chat_model,
+            temperature=0.2,  # Lower temperature for more factual responses
+            max_completion_tokens=2048,
+        )
+        
+        return assessment_completion.choices[0].message.content
+    
+    except Exception as e:
+        return f"Error generating assessment: {str(e)}"
+
+# Doctor's chat interaction with the model about the patient
+def doctor_chat(message, chat_history, ecg_analysis, patient_history, assessment, chat_model="llama-3.3-70b-versatile"):
+    # Fixed model - always use llama-3.3-70b-versatile
+    chat_model = "llama-3.3-70b-versatile"
+    if not ecg_analysis or ecg_analysis.startswith("Error"):
+        return "Please analyze an ECG image first before starting a chat.", chat_history
+    
+    if not message.strip():
+        return "", chat_history
+    
+    # Get current timestamp
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
+    # Prepare chat context
+    context = f"""ECG ANALYSIS:
+{ecg_analysis}
+
+MEDICAL ASSESSMENT:
+{assessment}
+
+TIMESTAMP: {timestamp}
+
+"""
+    
+    if patient_history and patient_history.strip():
+        context += f"""PATIENT HISTORY:
+{patient_history}
+
+"""
+    
+    # Construct full chat history for context
+    messages = [
+        {
+            "role": "system",
+            "content": f"You are a medical AI assistant specialized in cardiology. You are helping a doctor interpret ECG results and patient data. Answer the doctor's questions based on the following information:\n\n{context}"
+        }
+    ]
+    
+    # Add chat history to the context (limited to last 10 exchanges to avoid token limits)
+    for entry in chat_history[-10:]:
+        messages.append({"role": "user", "content": entry[0]})
+        messages.append({"role": "assistant", "content": entry[1]})
+    
+    # Add the current message
+    messages.append({"role": "user", "content": message})
+    
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=messages,
+            model=chat_model,
+            temperature=0.3,
+            max_completion_tokens=1024,
+        )
+        
+        response = chat_completion.choices[0].message.content
+        chat_history.append((message, response))
+        return "", chat_history
+    
+    except Exception as e:
+        error_message = f"Error in chat: {str(e)}"
+        chat_history.append((message, error_message))
+        return "", chat_history
+
+# Create Gradio interface
+with gr.Blocks(title="Cardiac ECG Analysis System", theme=gr.themes.Soft()) as app:
+    # Session state to store data
+    ecg_analysis_state = gr.State("")
+    
+    gr.Markdown("# 🫀 Cardiac ECG Analysis System")
+    gr.Markdown("Upload an ECG image and optional patient history to get an automated analysis and assessment.")
+    
+    with gr.Tabs():
+        with gr.TabItem("💻 Main Interface"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    # Input components
+                    with gr.Box():
+                        gr.Markdown("### 📊 ECG Image")
+                        ecg_image = gr.Image(type="pil", label="Upload ECG Image")
+                        # Display fixed model info
+                        gr.Markdown("**Vision Model:** llama-3.2-90b-vision-preview")
+                        analyze_button = gr.Button("Analyze ECG Image", variant="primary")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 📋 Patient Information")
+                        patient_history_text = gr.Textbox(
+                            lines=8, 
+                            label="Patient History (Manual Entry)", 
+                            placeholder="Enter patient's medical history, age, sex, symptoms, medications, etc."
+                        )
+                        patient_history_file = gr.File(
+                            label="Upload Patient History File (Optional, .txt, .csv, or .xlsx)",
+                            file_types=[".txt", ".csv", ".xlsx", ".xls"]
+                        )
+                        load_history_button = gr.Button("Load Patient History from File")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 🧠 Assessment Settings")
+                        # Display fixed model info
+                        gr.Markdown("**Chat Model:** llama-3.3-70b-versatile")
+                        assess_button = gr.Button("Generate Assessment", variant="primary")
+                
+                with gr.Column(scale=1):
+                    # Output components
+                    with gr.Box():
+                        gr.Markdown("### 📈 ECG Analysis Results")
+                        ecg_analysis_output = gr.HTML(label="ECG Analysis", elem_id="ecg-analysis")
+                    
+                    with gr.Box():
+                        gr.Markdown("### 📝 Medical Assessment")
+                        assessment_output = gr.HTML(label="Assessment", elem_id="assessment-output")
+            
+            gr.Markdown("## 👨‍⚕️ Doctor's Consultation")
+            gr.Markdown("Ask follow-up questions about the patient's ECG results and medical condition.")
+            
+            with gr.Box():
+                chatbot = gr.Chatbot(label="Consultation", height=400)
+                with gr.Row():
+                    message = gr.Textbox(
+                        lines=2, 
+                        label="Doctor's Question",
+                        placeholder="Ask a question about this patient's cardiac status...",
+                        scale=4
+                    )
+                    chat_button = gr.Button("Send", scale=1, variant="primary")
+        
+        with gr.TabItem("ℹ️ Instructions"):
+            gr.Markdown("""
+            ## How to Use This Application
+            
+            ### Step 1: Upload and Analyze ECG
+            1. Upload an ECG image using the file uploader in the Main Interface tab
+            2. Select the vision model (90b recommended for best results)
+            3. Click "Analyze ECG Image" to extract readings from the image
+            
+            ### Step 2: Add Patient Information (Optional)
+            - Enter patient history directly in the text box, OR
+            - Upload a patient history file (.txt, .csv, or .xlsx) and click "Load Patient History from File"
+            
+            ### Step 3: Generate Assessment
+            1. Select the chat model (70b recommended for detailed analysis)
+            2. Click "Generate Assessment" to get an AI-assisted interpretation
+            
+            ### Step 4: Consultation
+            - Use the chatbot interface to ask follow-up questions
+            - The AI will consider the ECG analysis, patient history, and previous assessment in its responses
+            
+            ### Important Notes
+            - This tool is designed to assist healthcare professionals, not replace clinical judgment
+            - Always validate AI-generated medical interpretations with proper medical expertise
+            - Patient data privacy should be maintained according to relevant regulations
+            """)
+    
+    # Set up event handlers
+    analyze_button.click(
+        analyze_ecg_image, 
+        inputs=[ecg_image], 
+        outputs=ecg_analysis_output
+    ).then(
+        lambda x: x,  # Pass through function to update state
+        inputs=ecg_analysis_output,
+        outputs=ecg_analysis_state
+    )
+    
+    def process_and_update_history(file):
+        if file is None:
+            return "No file uploaded."
+        processed_text = process_patient_history(file)
+        return processed_text
+    
+    load_history_button.click(
+        process_and_update_history,
+        inputs=[patient_history_file],
+        outputs=[patient_history_text]
+    )
+    
+    assess_button.click(
+        generate_assessment, 
+        inputs=[ecg_analysis_output, patient_history_text], 
+        outputs=assessment_output
+    )
+    
+    chat_button.click(
+        doctor_chat, 
+        inputs=[message, chatbot, ecg_analysis_output, patient_history_text, assessment_output], 
+        outputs=[message, chatbot]
+    )
+    
+    # Also trigger chat on Enter key
+    message.submit(
+        doctor_chat,
+        inputs=[message, chatbot, ecg_analysis_output, patient_history_text, assessment_output],
         outputs=[message, chatbot]
     )