Update app.py

app.py

@@ -2,10 +2,18 @@
 import streamlit as st
 import requests
 from geopy.geocoders import Nominatim
+import whisper
+import tempfile
+from pydub import AudioSegment
+from io import BytesIO
+from streamlit_js_eval import streamlit_js_eval
 
 # Set your Hugging Face API URL and API key
 API_URL = "https://api-inference.huggingface.co/models/dmis-lab/biobert-base-cased-v1.1"
-headers = {"Authorization": ""}
+headers = {"Authorization": f"secret"}
+
+# Initialize Whisper model
+whisper_model = whisper.load_model("base")
 
 # Function to query the Hugging Face model
 def query(payload):
@@ -13,7 +21,7 @@ def query(payload):
     if response.status_code == 200:
         return response.json()
     else:
-        st.error("Error: Unable to fetch response from model")
+        st.error(f"Error: Unable to fetch response from model (status code: {response.status_code})")
         st.error(response.text)
         return None
 
@@ -27,16 +35,71 @@ def find_nearby_clinics(address):
         st.error("Error: Address not found")
         return None
 
+# Function to transcribe audio to text using Whisper
+def transcribe_audio(audio_bytes):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file:
+        audio = AudioSegment.from_file(BytesIO(audio_bytes), format="wav")
+        audio.export(temp_audio_file.name, format="wav")
+        result = whisper_model.transcribe(temp_audio_file.name)
+        return result["text"]
+
 # Main function to create the Streamlit app
 def main():
     st.title("Healthcare Companion")
     st.write("This app provides healthcare guidance, prescription information, and locates nearby clinics or pharmacies.")
 
-    # User input for medical symptoms
-    symptoms = st.text_area("Enter your symptoms (e.g., 'I am having a cough, weak knee, swollen eyes'):")
-    if symptoms:
+    # JavaScript code to capture audio
+    js_code = """
+    async function recordAudio() {
+        const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
+        const mediaRecorder = new MediaRecorder(stream);
+        let audioChunks = [];
+        
+        mediaRecorder.ondataavailable = event => {
+            audioChunks.push(event.data);
+        };
+        
+        mediaRecorder.onstop = async () => {
+            const audioBlob = new Blob(audioChunks, { type: 'audio/wav' });
+            const audioBuffer = await audioBlob.arrayBuffer();
+            const audioBase64 = arrayBufferToBase64(audioBuffer);
+            document.getElementById('audio_data').value = audioBase64;
+            document.getElementById('audio_form').submit();
+        };
+        
+        mediaRecorder.start();
+        setTimeout(() => mediaRecorder.stop(), 5000);  // Record for 5 seconds
+        
+        function arrayBufferToBase64(buffer) {
+            let binary = '';
+            const bytes = new Uint8Array(buffer);
+            const len = bytes.byteLength;
+            for (let i = 0; i < len; i++) {
+                binary += String.fromCharCode(bytes[i]);
+            }
+            return window.btoa(binary);
+        }
+    }
+    
+    recordAudio();
+    """
+    
+    # Placeholder for audio data
+    st_js_code = streamlit_js_eval(js_code, key="record_audio")
+    
+    # Form to receive audio data from JavaScript
+    with st.form("audio_form", clear_on_submit=True):
+        audio_data = st.text_input("audio_data", type="hidden")
+        submit_button = st.form_submit_button("Submit")
+        
+        if submit_button and audio_data:
+            audio_bytes = BytesIO(base64.b64decode(audio_data))
+            symptoms = transcribe_audio(audio_bytes)
+            st.write(f"Transcribed symptoms: {symptoms}")
+    
+    if 'symptoms' in locals() and symptoms:
         context = """
-        This is a healthcare question and answer platform. The following text contains typical symptoms, treatments, and medical conditions commonly asked about in healthcare settings. 
+        This is a healthcare question and answer platform. The following text contains typical symptoms, treatments, and medical conditions commonly asked about in healthcare settings.
         For example, symptoms of COVID-19 include fever, dry cough, and tiredness. Treatment options for hypertension include lifestyle changes and medications. The platform is designed to assist with general medical inquiries.
         """
         payload = {"inputs": {"question": symptoms, "context": context}}
@@ -45,7 +108,9 @@ def main():
         st.write(f"Debug: Response from model: {result}")  # Debugging: Check response
         if result:
             st.write("**Medical Advice:**")
-            st.write(result.get('answer', "Sorry, I don't have information on that."))
+            # Check the response structure and extract the answer appropriately
+            answer = result.get('answer') if 'answer' in result else "Sorry, I don't have information on that."
+            st.write(answer)
 
     # User input for address to find nearby clinics/pharmacies
     address = st.text_input("Enter your address to find nearby clinics/pharmacies:")
@@ -58,3 +123,4 @@ def main():
 
 if __name__ == "__main__":
     main()
+