Update app.py

app.py

@@ -50,45 +50,46 @@
 
 
 import streamlit as st
+import sounddevice as sd
+import numpy as np
+import io
+import wavio
 from fpdf import FPDF
-import speech_recognition as sr
-from io import BytesIO
 
-# Function to predict disease based on symptoms
+# Function to handle voice input using sounddevice
+def voice_input():
+    st.write("Listening...")
+    fs = 44100  # Sample rate
+    duration = 5  # Duration in seconds
+
+    # Record audio
+    audio_data = sd.rec(int(duration * fs), samplerate=fs, channels=1, dtype='int16')
+    sd.wait()  # Wait until recording is finished
+
+    # Save to a WAV file
+    wav_buffer = io.BytesIO()
+    wavio.write(wav_buffer, audio_data, fs, sampwidth=2)
+    wav_buffer.seek(0)
+
+    # Convert audio to text (placeholder, you need to use a library/service for actual transcription)
+    return "Symptoms recognized: Placeholder text from audio"
+
+# Function to simulate disease prediction
 def predict_disease(symptoms):
-    # Example logic (replace with actual logic or model predictions)
-    if "fever" in symptoms.lower() and "cough" in symptoms.lower():
-        return "Flu"
-    elif "pain" in symptoms.lower() and "swelling" in symptoms.lower():
-        return "Arthritis"
-    else:
-        return "Unknown disease. Please provide more details."
+    # Placeholder function for predicting disease based on symptoms
+    return "Disease based on symptoms: Placeholder prediction"
 
-# Function to handle voice input
-def voice_input():
-    r = sr.Recognizer()
-    try:
-        with sr.Microphone() as source:
-            r.adjust_for_ambient_noise(source)
-            st.write("Listening...")
-            audio = r.listen(source)
-        symptoms = r.recognize_google(audio)
-        return symptoms
-    except sr.UnknownValueError:
-        return "Sorry, I could not understand the audio."
-    except sr.RequestError:
-        return "Error with the speech recognition service."
-    except Exception as e:
-        return f"Microphone error: {e}"
-
-# Function to save response to a PDF
-def save_to_pdf(response):
+# Function to save results as a PDF
+def save_to_pdf(content):
     pdf = FPDF()
     pdf.add_page()
     pdf.set_font("Arial", size=12)
-    pdf.multi_cell(0, 10, f"Medical Assistant Response:\n\n{response}")
-    pdf_output = BytesIO()
+    pdf.cell(200, 10, txt="Medical Assistance Results", ln=True, align='C')
+    pdf.ln(10)
+    pdf.multi_cell(0, 10, content)
+    pdf_output = io.BytesIO()
     pdf.output(pdf_output)
+    pdf_output.seek(0)
     return pdf_output
 
 # Streamlit app
@@ -108,10 +109,7 @@ save_as_pdf = st.checkbox("Save result as PDF")
 if st.button("Submit"):
     if use_voice_input:
         symptoms = voice_input()  # Get symptoms via voice input
-        if "error" in symptoms.lower():
-            st.error(symptoms)  # Display the error message
-        else:
-            st.write(f"Symptoms recognized: {symptoms}")
+        st.write(f"Symptoms recognized: {symptoms}")
     else:
         symptoms = symptoms_input  # Use keyboard input
 
@@ -121,7 +119,7 @@ if st.button("Submit"):
 
         # Optionally save the response as a PDF
         if save_as_pdf:
-            pdf_output = save_to_pdf(prediction)
+            pdf_output = save_to_pdf(f"Symptoms: {symptoms}\n\nPrediction: {prediction}")
             st.download_button(
                 label="Download PDF",
                 data=pdf_output.getvalue(),