Update app.py

app.py

@@ -5,6 +5,11 @@ import json
 from dotenv import load_dotenv
 import re
 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')
 
 # Load environment variables
 load_dotenv()
@@ -12,7 +17,7 @@ load_dotenv()
 # Initialize Groq client
 client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
 
-def parse_non_json_response(text):
+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)
@@ -39,7 +44,7 @@ def parse_non_json_response(text):
         "disclaimer": disclaimer
     }
 
-def get_diagnosis(age, gender, symptoms, medical_history):
+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.
@@ -72,7 +77,7 @@ def get_diagnosis(age, gender, symptoms, medical_history):
                     "content": prompt,
                 }
             ],
-            model="llama-3.1-8b-instant",  # Updated to a valid Groq model
+            model="llama-3.1-8b-instant",
             temperature=0.5,
             max_tokens=1500,
         )
@@ -86,9 +91,10 @@ def get_diagnosis(age, gender, symptoms, medical_history):
         
         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):
+def plot_risk(potential_cancer_types: List[Dict[str, str]]) -> go.Figure:
     if not potential_cancer_types:
         return None
 
@@ -99,17 +105,24 @@ def plot_risk(potential_cancer_types):
     fig = go.Figure(data=[go.Bar(
         x=names,
         y=risk_levels,
-        marker_color=colors
+        marker_color=colors,
+        text=risk_levels,
+        textposition='auto',
     )])
     fig.update_layout(
         title="Cancer Risk Levels",
-        yaxis_title="Risk Level (1: Low, 2: Medium, 3: High)",
-        xaxis_tickangle=-45
+        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):
+def format_output(response: Dict[str, Any]) -> str:
     if "error" in response:
         return f"Error: {response['error']}"
 
@@ -129,7 +142,7 @@ def format_output(response):
     
     return output
 
-def validate_input(age, gender, symptoms, medical_history):
+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.")
@@ -137,7 +150,7 @@ def validate_input(age, gender, symptoms, medical_history):
         errors.append("Please enter at least one symptom.")
     return errors
 
-def process_input(age, gender, symptoms, medical_history):
+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
@@ -149,6 +162,9 @@ def process_input(age, gender, symptoms, medical_history):
     
     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,
@@ -164,10 +180,19 @@ iface = gr.Interface(
     ],
     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."
+    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]])
+
 # Launch the app
 if __name__ == "__main__":
-    iface.launch()
-    
+    iface.launch()