Create app.py

app.py

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+import openai
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+import joblib
+import gradio as gr
+
+# Load the trained classifier model
+model = joblib.load('model_pkl')
+
+# OpenAI API key (assumed to be set in Hugging Face environment)
+openai.api_key = 'sk-jtt46VTrnym9B57gkkS_QAJ2E0RYodrEDHodAgliVqT3BlbkFJKooyGzBYbWkWkIt4-MG6ASNt4pMguQs2Mob6kpkaEA'
+
+# Function to simulate the medical assistant's interaction
+def medical_assistant_interaction(pulse_rate, blood_pressure_systolic, blood_pressure_diastolic, temperature_celsius, 
+                                  headache, muscle_pain, sore_throat, nausea_vomiting, diarrhea, joint_pains, cough):
+    try:
+        # Prepare symptom responses
+        symptom_responses = {
+            "Headache": headache,
+            "Muscle Pain": muscle_pain,
+            "Sore Throat": sore_throat,
+            "Nausea and Vomiting": nausea_vomiting,
+            "Diarrhea": diarrhea,
+            "Joint Pains": joint_pains,
+            "Dry Cough or Coughing Blood": cough
+        }
+
+        # Combine responses into a single text input
+        patient_responses = ', '.join([f"{symptom}: {response}" for symptom, response in symptom_responses.items()])
+
+        # Compile sensor data for the classifier
+        features = pd.DataFrame({
+            'Pulse_Rate': [pulse_rate],
+            'Blood_Pressure_Systolic': [blood_pressure_systolic],
+            'Blood_Pressure_Diastolic': [blood_pressure_diastolic],
+            'Temperature_Celsius': [temperature_celsius]
+        })
+
+        # Make a prediction using the trained classifier
+        prediction = model.predict(features)[0]
+
+        # Use OpenAI to make a combined inference
+        response_text = f"""
+        A patient has the following sensor readings:
+        - Pulse Rate: {pulse_rate} bpm
+        - Systolic Blood Pressure: {blood_pressure_systolic} mmHg
+        - Diastolic Blood Pressure: {blood_pressure_diastolic} mmHg
+        - Temperature: {temperature_celsius}°C
+
+        The patient reported the following symptoms: {patient_responses}.
+        
+        Based on these readings and symptoms, what is the likelihood of Lassa fever? Provide additional follow-up questions if necessary.
+        """
+
+        # Call OpenAI's API for a response
+        response = openai.Completion.create(
+            engine="text-davinci-003",
+            prompt=response_text,
+            max_tokens=150
+        )
+        assistant_message = response['choices'][0]['text'].strip()
+
+        # Combine the assistant's message with the model's prediction
+        if prediction == 1:
+            model_inference = "Based on the sensor readings, the classifier suggests a high likelihood of Lassa fever."
+        else:
+            model_inference = "Based on the sensor readings, the classifier suggests a low likelihood of Lassa fever."
+
+        final_inference = f"{assistant_message}\n\n{model_inference}"
+        
+        return final_inference
+
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Gradio interface for interaction
+def gradio_interface(pulse_rate, blood_pressure_systolic, blood_pressure_diastolic, temperature_celsius, 
+                     headache, muscle_pain, sore_throat, nausea_vomiting, diarrhea, joint_pains, cough):
+    return medical_assistant_interaction(
+        pulse_rate, blood_pressure_systolic, blood_pressure_diastolic, temperature_celsius, 
+        headache, muscle_pain, sore_throat, nausea_vomiting, diarrhea, joint_pains, cough
+    )
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Number(label="Pulse Rate (bpm)"),
+        gr.Number(label="Systolic Blood Pressure (mmHg)"),
+        gr.Number(label="Diastolic Blood Pressure (mmHg)"),
+        gr.Number(label="Temperature (Celsius)"),
+        gr.Radio(choices=["Yes", "No"], label="Are you experiencing headaches?"),
+        gr.Radio(choices=["Yes", "No"], label="Are you experiencing muscle pain?"),
+        gr.Radio(choices=["Yes", "No"], label="Are you experiencing a sore throat?"),
+        gr.Radio(choices=["Yes", "No"], label="Are you experiencing nausea and vomiting?"),
+        gr.Radio(choices=["Yes", "No"], label="Are you experiencing diarrhea?"),
+        gr.Radio(choices=["Yes", "No"], label="Are you experiencing joint pains?"),
+        gr.Radio(choices=["Yes", "No"], label="Are you experiencing a dry cough or coughing blood?")
+    ],
+    outputs="text",
+    title="Lassa Fever Medical Assistant",
+    description="This assistant uses sensor data and patient symptom responses to infer the likelihood of Lassa fever."
+)
+
+# Launch the Gradio interface
+interface.launch()