fix: that god damn bug

app.py

@@ -56,7 +56,7 @@ exercise_habits_input = gr.Radio(choices=additional_categories["Exercise_Habits"
 diet_input = gr.Radio(choices=additional_categories["Diet"], label="Diet", value="Omnivore")
 condition_severity_input = gr.Slider(minimum=1, maximum=10, label="Condition Severity", step=1, value=5)
 functional_status_input = gr.Radio(choices=additional_categories["Functional_Status"], label="Functional Status", value="Independent")
-previous_trial_participation_input = gr.Radio(choices=additional_categories["Previous_Trial_Participation"], label="Previous Trial Participation", value="No")
+previous_trial_participation_input = gr.Radio(choices=additional_categories["Previous_Trial_Participation"], label="Previous Trial Participation", value="Yes")
 
 
 # def encrypt_array(user_symptoms: np.ndarray, user_id: str) -> bytes:
@@ -132,7 +132,7 @@ def encode_categorical_data(data):
 def clear_data_to_json(data):
     print(data)
     patient_data = {
-        "model_names": ["my_model"],
+        "model_names": ["second_model"],
         "patient": {
             "Age": data.get("age", 30),
             "Blood_Glucose_Level": data.get("blood_glucose_level", 0),
@@ -155,7 +155,7 @@ def clear_data_to_json(data):
             "Previous_Trial_Participation": data.get("Previous_Trial_Participation", 0)
         }
     }
-    return json.dumps(patient_data, indent=4)
+    return patient_data
 
 
 def process_patient_data(age, gender, ethnicity, geographic_location, diagnoses_icd10, medications, allergies, previous_treatments, blood_glucose_level, blood_pressure_systolic, blood_pressure_diastolic, bmi, smoking_status, alcohol_consumption, exercise_habits, diet, condition_severity, functional_status, previous_trial_participation):    
@@ -190,30 +190,42 @@ def process_patient_data(age, gender, ethnicity, geographic_location, diagnoses_
     # encrypted_array = encrypt_array(combined_data, "user_id")
 
     # Send the data to the server 
-    url = SERVER_URL + "clear-match"
-    response = requests.post(url, data=json_data)
+    url = SERVER_URL + "inference/clear-match" 
+    headers = {"Content-Type": "application/json", "X-API-KEY": "secret"}
+    response = requests.post(url, data=json_data, headers=headers)
 
     # Check if the data was sent successfully
     if response.status_code == 200:
         print("Data sent successfully.")
     else:
-        print("Error sending data.")
+        print(f"Error sending data. Status code: {response.status_code}")
     
+    # return f"**[Possible Trial Link]({EXAMPLE_CLINICAL_TRIAL_LINK})**"
     # Decrypt the result
     # decrypted_result = decrypt_result(response.content, USER_ID)
-    print()
-    decrypted_result = response.json()
-    # If the answer is True, return the link
+    # decrypted_result = None
+    try:
+        decrypted_result = response.json()
+        print(f"Decrypted result: {decrypted_result}")
+    except json.JSONDecodeError as e:
+        print(f"Error decrypting result: Invalid JSON. {e}")
+        decrypted_result = False
+        return "There was an error processing the data."
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        decrypted_result = False
+        return "There was an error processing the data."
+    else:
+        print("Json parsed successfully.")
+    
+    
+     # If the answer is True, return the link
     if decrypted_result:
-        return (
-            # f"Encrypted data: {encrypted_array}",
-            f"Decrypted result: {response.json()}"
-        )
+        return f"**[Possible Trial Link]({EXAMPLE_CLINICAL_TRIAL_LINK})**"
     else:
-        return (
-            # f"Encrypted data: {encrypted_array}",
-            f"Decrypted result: {response.json()}"
-        )
+        return "There was an error processing the data."
+    
+
 # Define the function to handle image upload
 def handle_image_upload(image):
     url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
@@ -227,6 +239,7 @@ def handle_image_upload(image):
     pooler_output = outputs.pooler_output[0]
     sclaed_output = 127 + 127 * pooler_output / pooler_output.abs().max()
     sclaed_output = sclaed_output.to(int)
+    url = "/inference/clear-diagnosis"
     return ["Melanoma", "Vascular lesion"]
 
 # Create the Gradio interface
@@ -257,7 +270,7 @@ with gr.Blocks() as demo:
             image_input = gr.Image(label="Upload an Image")
             gr.Button("Upload").click(handle_image_upload, inputs=image_input, outputs=diagnoses_icd10_input)
         with gr.Group():
-            output = gr.JSON(label="Patient Data JSON")
+            output = gr.Markdown("**Server response**")
             gr.Button("Submit").click(process_patient_data, inputs=[
                 age_input, gender_input, ethnicity_input, geographic_location_input, diagnoses_icd10_input, medications_input, allergies_input, previous_treatments_input, blood_glucose_level_input, blood_pressure_systolic_input, blood_pressure_diastolic_input, bmi_input, smoking_status_input, alcohol_consumption_input, exercise_habits_input, diet_input, condition_severity_input, functional_status_input, previous_trial_participation_input
             ], outputs=output)