Update app.py

app.py

@@ -61,8 +61,9 @@ def process_patient_history(file):
 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."
+        return "<strong style='color:red'>No image provided.</strong>"
     
     # Convert to PIL Image if needed
     if not isinstance(image, Image.Image):
@@ -120,19 +121,25 @@ 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+(.+)
+        # Make sure all headers are properly formatted (with complete pattern)
+        ecg_analysis = re.sub(r'^(#+)\s+(.+)$', 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."
+    
+    if not ecg_analysis or ecg_analysis.startswith("<strong style='color:red'>Error"):
+        return "<strong style='color:red'>Please analyze an ECG image first.</strong>"
     
     # Get current timestamp
     timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -222,14 +229,20 @@ Important formatting instructions:
         # 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+(.+)
+        # Make sure all headers are properly formatted (with complete pattern)
+        assessment_text = re.sub(r'^(#+)\s+(.+)$', 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"):
+    
+    if not ecg_analysis or ecg_analysis.startswith("<strong style='color:red'>Error"):
         return "Please analyze an ECG image first before starting a chat.", chat_history
     
     if not message.strip():
@@ -280,816 +293,15 @@ TIMESTAMP: {timestamp}
         )
         
         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,
-        )
+        # Process any remaining asterisks to HTML tags in the response
+        response = re.sub(r'\*\*([^*]+)\*\*', r'<strong>\1</strong>', response)
         
-        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]
-    )
-
-# 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)}"
+        error_message = f"<strong style='color:red'>Error in chat:</strong> {str(e)}"
         chat_history.append((message, error_message))
         return "", chat_history
 
@@ -1162,16 +374,14 @@ with gr.Blocks(title="Cardiac ECG Analysis System", theme=gr.themes.Soft()) as a
             
             ### 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
+            2. 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
+            - Click "Generate Assessment" to get an AI-assisted interpretation
             
             ### Step 4: Consultation
             - Use the chatbot interface to ask follow-up questions