Delete app.py

app.py

@@ -1,126 +0,0 @@
-
-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": f"secret"}
-
-# Initialize Whisper model
-whisper_model = whisper.load_model("base")
-
-# Function to query the Hugging Face model
-def query(payload):
-    response = requests.post(API_URL, headers=headers, json=payload)
-    if response.status_code == 200:
-        return response.json()
-    else:
-        st.error(f"Error: Unable to fetch response from model (status code: {response.status_code})")
-        st.error(response.text)
-        return None
-
-# Function to find nearby clinics/pharmacies using geopy
-def find_nearby_clinics(address):
-    geolocator = Nominatim(user_agent="healthcare_companion")
-    location = geolocator.geocode(address)
-    if location:
-        return (location.latitude, location.longitude)
-    else:
-        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.")
-
-    # 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.
-        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}}
-        st.write(f"Debug: Payload sent to model: {payload}")  # Debugging: Check payload
-        result = query(payload)
-        st.write(f"Debug: Response from model: {result}")  # Debugging: Check response
-        if result:
-            st.write("**Medical Advice:**")
-            # Check the response structure and extract the answer appropriately
-            answer = result.get('answer') if 'answer' in result else "Sorry, how about i contact a doctor."
-            st.write(answer)
-
-    # User input for address to find nearby clinics/pharmacies
-    address = st.text_input("Enter your address to find nearby clinics/pharmacies:")
-    if address:
-        location = find_nearby_clinics(address)
-        if location:
-            st.write(f"**Nearby Clinics/Pharmacies (Coordinates):** {location}")
-
-    # Additional features like prescription info can be added similarly
-
-if __name__ == "__main__":
-    main()
-