Update app.py

app.py

@@ -8,190 +8,52 @@ import plotly.graph_objs as go
 from typing import List, Dict, Any
 import logging
 
-# Set up logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+# ... (previous code remains unchanged)
 
-# Load environment variables
-load_dotenv()
-
-# Initialize Groq client
-client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-
-def parse_non_json_response(text: str) -> Dict[str, Any]:
-    # Attempt to extract structured information from non-JSON text
-    cancer_types = re.findall(r"(?:cancer type|Cancer Type):\s*(.+?)(?:\n|$)", text, re.IGNORECASE)
-    risk_levels = re.findall(r"(?:risk level|Risk Level):\s*(.+?)(?:\n|$)", text, re.IGNORECASE)
-    descriptions = re.findall(r"(?:description|Description):\s*(.+?)(?:\n|$)", text, re.IGNORECASE)
-    
-    potential_cancer_types = [
-        {"name": ct, "risk_level": rl, "description": desc}
-        for ct, rl, desc in zip(cancer_types, risk_levels, descriptions)
-    ]
-    
-    recommended_tests = re.findall(r"(?:Test Name):\s*(.+?)\n(?:description|Description):\s*(.+?)(?:\n|$)", text, re.IGNORECASE)
-    recommended_tests = [{"name": name, "description": desc} for name, desc in recommended_tests]
-    
-    general_advice = re.search(r"(?:General Advice|general advice):\s*(.+?)(?:\n|$)", text, re.DOTALL | re.IGNORECASE)
-    general_advice = general_advice.group(1) if general_advice else "No general advice provided."
-    
-    disclaimer = re.search(r"(?:DISCLAIMER|Disclaimer):\s*(.+?)(?:\n|$)", text, re.DOTALL | re.IGNORECASE)
-    disclaimer = disclaimer.group(1) if disclaimer else "No disclaimer provided. This tool is for educational purposes only and should not replace professional medical advice."
-    
-    return {
-        "potential_cancer_types": potential_cancer_types,
-        "recommended_tests": recommended_tests,
-        "general_advice": general_advice,
-        "disclaimer": disclaimer
-    }
+def clear_inputs():
+    return gr.Number(value=None), gr.Radio(value=None), gr.Textbox(value=""), gr.Textbox(value="")
 
-def get_diagnosis(age: int, gender: str, symptoms: str, medical_history: str) -> Dict[str, Any]:
-    prompt = f"""
-    Given the following patient information, provide a preliminary analysis of potential cancer risks and recommended tests. 
-    Give professional medical advice to the best of your ability.
-    
-    Patient Information:
-    Age: {age}
-    Gender: {gender}
-    Symptoms: {symptoms}
-    Medical History: {medical_history}
-    
-    Please provide a response with the following structure:
-    Potential Cancer Types:
-    - Cancer Type: [Name]
-      Risk Level: [Low/Medium/High]
-      Description: [Brief description of why this cancer type is considered]
-    Recommended Tests:
-    - Test Name: [Name]
-      Description: [Brief description of why this test is recommended]
-    General Advice: [General health advice for the patient]
-    DISCLAIMER: [A strong disclaimer about the limitations of this assessment]
-    
-    Ensure the response emphasizes the importance of consulting with a medical professional for accurate diagnosis and treatment.
-    """
-    
-    try:
-        chat_completion = client.chat.completions.create(
-            messages=[
-                {
-                    "role": "user",
-                    "content": prompt,
-                }
-            ],
-            model="llama-3.1-8b-instant",
-            temperature=0.5,
-            max_tokens=1500,
-        )
-        
-        response_content = chat_completion.choices[0].message.content
-        
-        try:
-            response = json.loads(response_content)
-        except json.JSONDecodeError:
-            response = parse_non_json_response(response_content)
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as iface:
+    gr.Markdown("# Vers3Dynamics HealthScan: Personalized Cancer Risk Insights")
+    gr.Markdown("This Groq-powered tool provides a preliminary analysis of potential cancer risks based on the information you provide. It is designed to support early awareness and is not a substitute for professional medical advice, diagnosis, or treatment.")
+    
+    with gr.Row():
+        with gr.Column():
+            age_input = gr.Number(label="Age")
+            gender_input = gr.Radio(["Male", "Female", "Other"], label="Gender")
+            symptoms_input = gr.Textbox(lines=3, label="Symptoms (separated by commas)")
+            medical_history_input = gr.Textbox(lines=3, label="Relevant Medical History")
+            submit_button = gr.Button("Submit")
+            clear_button = gr.Button("Clear Inputs")
         
-        return response
-    except Exception as e:
-        logging.error(f"Error in get_diagnosis: {str(e)}")
-        return {"error": f"An error occurred while communicating with the API: {str(e)}"}
-
-def plot_risk(potential_cancer_types: List[Dict[str, str]]) -> go.Figure:
-    if not potential_cancer_types:
-        return None
-
-    names = [c["name"] for c in potential_cancer_types]
-    risk_levels = [1 if c["risk_level"].lower() == "low" else 2 if c["risk_level"].lower() == "medium" else 3 for c in potential_cancer_types]
-    colors = ["green" if rl == 1 else "yellow" if rl == 2 else "red" for rl in risk_levels]
-
-    fig = go.Figure(data=[go.Bar(
-        x=names,
-        y=risk_levels,
-        marker_color=colors,
-        text=risk_levels,
-        textposition='auto',
-    )])
-    fig.update_layout(
-        title="Cancer Risk Levels",
-        yaxis_title="Risk Level",
-        xaxis_tickangle=-45,
-        yaxis=dict(
-            tickmode='array',
-            tickvals=[1, 2, 3],
-            ticktext=['Low', 'Medium', 'High']
-        )
-    )
-
-    return fig
-
-def format_output(response: Dict[str, Any]) -> str:
-    if "error" in response:
-        return f"Error: {response['error']}"
-
-    output = "HealthScan AI: Personalized Cancer Risk Insights\n\n"
-    
-    output += "Potential Cancer Types:\n"
-    for cancer in response.get("potential_cancer_types", []):
-        output += f"- {cancer.get('name', 'N/A')} (Risk Level: {cancer.get('risk_level', 'N/A')})\n"
-        output += f"  {cancer.get('description', 'No description provided.')}\n\n"
+        with gr.Column():
+            output_text = gr.Textbox(label="Assessment Results", lines=10)
+            output_plot = gr.Plot(label="Cancer Risk Levels")
     
-    output += "Recommended Tests:\n"
-    for test in response.get("recommended_tests", []):
-        output += f"- {test.get('name', 'N/A')}: {test.get('description', 'No description provided.')}\n\n"
+    gr.Markdown("## IMPORTANT")
+    gr.Markdown("HealthScan AI is for educational and informational purposes only. Always consult with a qualified healthcare provider for medical concerns. The insights provided by this tool should not be used for self-diagnosis or treatment. Early detection and regular check-ups with healthcare professionals are crucial for managing your health effectively.")
     
-    output += f"General Advice:\n{response.get('general_advice', 'No general advice provided.')}\n\n"
-    output += f"DISCLAIMER:\n{response.get('disclaimer', 'No disclaimer provided. This tool is for educational purposes only and should not replace professional medical advice.')}"
-    
-    return output
-
-def validate_input(age: int, gender: str, symptoms: str, medical_history: str) -> List[str]:
-    errors = []
-    if not (0 < age < 120):
-        errors.append("Please enter a valid age between 1 and 120.")
-    if not symptoms.strip():
-        errors.append("Please enter at least one symptom.")
-    return errors
-
-def process_input(age: int, gender: str, symptoms: str, medical_history: str) -> tuple:
-    errors = validate_input(age, gender, symptoms, medical_history)
-    if errors:
-        return "\n".join(errors), None
-
-    diagnosis = get_diagnosis(age, gender, symptoms, medical_history)
-    output = format_output(diagnosis)
+    submit_button.click(
+        fn=process_input,
+        inputs=[age_input, gender_input, symptoms_input, medical_history_input],
+        outputs=[output_text, output_plot]
+    )
     
-    risk_plot = plot_risk(diagnosis.get("potential_cancer_types", []))
+    clear_button.click(
+        fn=clear_inputs,
+        inputs=[],
+        outputs=[age_input, gender_input, symptoms_input, medical_history_input]
+    )
     
-    return output, risk_plot
-
-def clear_inputs():
-    return gr.Number(value=None), gr.Radio(value=None), gr.Textbox(value=""), gr.Textbox(value="")
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=process_input,
-    inputs=[
-        gr.Number(label="Age"),
-        gr.Radio(["Male", "Female", "Other"], label="Gender"),
-        gr.Textbox(lines=3, label="Symptoms (separated by commas)"),
-        gr.Textbox(lines=3, label="Relevant Medical History")
-    ],
-    outputs=[
-        gr.Textbox(label="Assessment Results", lines=10),
-        gr.Plot(label="Cancer Risk Levels")
-    ],
-    title="Vers3Dynamics HealthScan: Personalized Cancer Risk Insights",
-    description="This Groq-powered tool provides a preliminary analysis of potential cancer risks based on the information you provide. It is designed to support early awareness and is not a substitute for professional medical advice, diagnosis, or treatment.",
-    article="IMPORTANT: HealthScan AI is for educational and informational purposes only. Always consult with a qualified healthcare provider for medical concerns. The insights provided by this tool should not be used for self-diagnosis or treatment. Early detection and regular check-ups with healthcare professionals are crucial for managing your health effectively.",
-    examples=[
-        [45, "Male", "Persistent cough, weight loss", "Family history of lung cancer"],
-        [35, "Female", "Unexplained fatigue, bruising easily", "No significant medical history"],
-        [60, "Other", "Blood in stool, abdominal pain", "History of inflammatory bowel disease"]
-    ],
-    theme=gr.themes.Soft()
-)
-
-# Add a clear button
-clear_button = gr.Button("Clear Inputs")
-clear_button.click(fn=clear_inputs, inputs=[], outputs=[iface.inputs[0], iface.inputs[1], iface.inputs[2], iface.inputs[3]])
+    gr.Examples(
+        examples=[
+            [45, "Male", "Persistent cough, weight loss", "Family history of lung cancer"],
+            [35, "Female", "Unexplained fatigue, bruising easily", "No significant medical history"],
+            [60, "Other", "Blood in stool, abdominal pain", "History of inflammatory bowel disease"]
+        ],
+        inputs=[age_input, gender_input, symptoms_input, medical_history_input]
+    )
 
 # Launch the app
 if __name__ == "__main__":