Upload new.py

new.py

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+# -*- coding: utf-8 -*-
+"""new.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/10l5M_bqlqlmx8isz58qrPCp9YwDOKjiC
+"""
+
+from re import S
+!pip install Streamlit
+!pip install whisper
+!pip install pydub
+!pip install mapbox
+!pip install openai-whisper pydub streamlit requests geopy
+!pip install streamlit-webrtc
+
+from google.colab import userdata
+userdata.get('secret')
+
+import streamlit as st
+import requests
+from geopy.geocoders import Nominatim
+import whisper
+import tempfile
+from pydub import AudioSegment
+from io import BytesIO
+import base64
+import folium
+import requests
+from mapbox import Geocoder
+import folium
+
+
+# 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")
+
+# Initialize Mapbox Geocoder with your API key
+MAPBOX_API_KEY = "sk.eyJ1IjoibWFyaW9jYWxlYiIsImEiOiJjbHdkZWV3b2wwdnlxMmtwbnhqaHdnNzA3In0.iOhyIXlUFztYtc1iZhSMng"
+geocoder = Geocoder(access_token=MAPBOX_API_KEY)
+
+# 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 Mapbox
+def find_nearby_clinics(address):
+    response = geocoder.forward(address)
+    if response.status_code == 200:
+        features = response.json()['features']
+        if features:
+            location = features[0]['center']
+            return location
+        else:
+            st.error("No locations found")
+            return None
+    else:
+        st.error("Error: Unable to fetch location data")
+        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("Welcome to Healthcare Companion! Your personal healthcare guide.")
+
+    st.header("Speak Your Symptoms")
+    st.write("Press the button and speak your symptoms clearly.")
+
+    # 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="default")
+        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:
+        st.header("Symptom Checker")
+        st.write("Enter your symptoms below for advice.")
+
+        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}}
+        result = query(payload)
+        if result:
+            st.write("**Medical Advice:**")
+            answer = result.get('answer', "Sorry, I don't have information on that.")
+            st.write(answer)
+
+    st.header("Locate Nearest Pharmacy/Clinic")
+    st.write("Enter your address to find the nearest pharmacy or clinic.")
+
+    address = st.text_input("Enter your address here:")
+    if address:
+        location = find_nearby_clinics(address)
+        if location:
+            map_ = folium.Map(location=[location[1], location[0]], zoom_start=15)
+            folium.Marker([location[1], location[0]], popup="Your Location").add_to(map_)
+            st_folium(map_, width=700, height=500)
+            st.write(f"**Nearby Clinics/Pharmacies (Coordinates):** {location}")
+
+    st.write("Providing reliable healthcare guidance and information. Please consult a healthcare professional for medical emergencies.")
+
+if __name__ == "__main__":
+    main()
+