Update app.py

app.py

@@ -48,135 +48,81 @@
 # if __name__ == "__main__":
 #     main()
 import streamlit as st
-import speech_recognition as sr
-# import pyttsx3
 from fpdf import FPDF
-#!sudo apt-get install espeak
-import pyttsx3
-#engine = pyttsx3.init('espeak') 
-engine = pyttsx3.init('espeak')  
-
-# Get a list of available voices
-voices = engine.getProperty('voices')
-
-# Choose a voice (adjust the index as needed)
-engine.setProperty('voice', voices[0].id)
-# Initialize speech recognition and TTS engine
-recognizer = sr.Recognizer()
-# engine = pyttsx3.init()
-
-# Function to listen to voice input
-def listen_to_voice():
-    """Listen to audio and convert to text."""
+import speech_recognition as sr
+from io import BytesIO
+
+# Function to predict disease based on symptoms
+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."
+
+# Function to handle voice input
+def voice_input():
+    r = sr.Recognizer()
     try:
         with sr.Microphone() as source:
-            st.write("Listening for symptoms (speak now)...")
-            audio = recognizer.listen(source, timeout=5, phrase_time_limit=10)
-            text = recognizer.recognize_google(audio)
-            st.write(f"You said: {text}")
-            return text
+            r.adjust_for_ambient_noise(source)
+            st.write("Listening...")
+            audio = r.listen(source)
+        symptoms = r.recognize_google(audio)
+        return symptoms
     except sr.UnknownValueError:
-        st.write("Sorry, I did not understand the audio.")
-        return ""
-    except sr.RequestError as e:
-        st.write(f"Could not request results from Google Speech Recognition service; {e}")
-        return ""
+        return "Sorry, I could not understand the audio."
+    except sr.RequestError:
+        return "Error with the speech recognition service."
     except Exception as e:
-        st.write(f"Error: {str(e)}")
-        return ""
+        return f"Microphone error: {e}"
 
-# Text-to-speech function
-def tts(text):
-    """Convert text to speech."""
-    engine.say(text)
-    engine.runAndWait()
-
-# Dummy disease prediction based on symptoms
-def predict_disease(symptoms):
-    """Mock prediction of disease based on symptoms."""
-    if 'fever' in symptoms.lower():
-        return "You might have the flu."
-    else:
-        return "No prediction available."
-
-# Function to generate PDF
-def generate_pdf(content):
-    """Generate PDF with the given content."""
+# Function to save response to a PDF
+def save_to_pdf(response):
     pdf = FPDF()
     pdf.add_page()
     pdf.set_font("Arial", size=12)
-    pdf.multi_cell(200, 10, content)
-    # Save the PDF to a file
-    pdf_file = 'medical_report.pdf'
-    pdf.output(pdf_file)
-    return pdf_file
+    pdf.multi_cell(0, 10, f"Medical Assistant Response:\n\n{response}")
+    pdf_output = BytesIO()
+    pdf.output(pdf_output)
+    return pdf_output
 
-# Streamlit App Interface
+# Streamlit app
 st.title("Medical Assistance for Doctors")
+st.write("Enter symptoms either by typing or using voice input.")
 
-# Step 1: Input Symptoms via Text Box with a voice input icon
-st.header("Enter Symptoms")
-
-# Display a text input box with a microphone button next to it
-col1, col2 = st.columns([5, 1])  # Create a layout with 5:1 ratio for text input and button
+# Input field for entering symptoms manually
+symptoms_input = st.text_area("Enter symptoms here:")
 
-with col1:
-    symptoms = st.text_area("Enter Symptoms Using Keyboard:")
+# Toggle for voice input
+use_voice_input = st.checkbox("Use Voice Input")
 
-with col2:
-    if st.button("🎤"):
-        symptoms = listen_to_voice()  # Capture voice input when the mic icon is pressed
+# Checkbox to save the result as PDF
+save_as_pdf = st.checkbox("Save result as PDF")
 
-# Display entered symptoms
-if symptoms:
-    st.write(f"Entered Symptoms: {symptoms}")
-
-# Step 2: Predict disease based on symptoms
-if st.button("Predict Disease"):
-    prediction = predict_disease(symptoms)
-    st.write(f"Prediction: {prediction}")
-    
-    # Step 3: Edit Response via Voice or Text
-    st.header("Edit Response (Optional)")
-    
-    # Mic button for editing response through voice
-    col3, col4 = st.columns([5, 1])
-    
-    with col3:
-        edited_response = st.text_area("Or Edit Response Using Keyboard:", prediction)
-    
-    with col4:
-        if st.button("🎤 Edit by Voice"):
-            edited_response = listen_to_voice()  # Voice editing
-            
-    if edited_response:
-        prediction = edited_response
-
-    # Display the final response
-    st.write(f"Final Response: {prediction}")
-    
-    # Text-to-speech option for the final response
-    if st.button("Play Final Response"):
-        tts(prediction)
-
-    # Step 4: Generate and Download PDF
-    st.header("Generate and Download PDF Report")
-    content = f"Symptoms: {symptoms}\nPrediction: {prediction}"
+# Button to trigger prediction
+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}")
+    else:
+        symptoms = symptoms_input  # Use keyboard input
 
-    if st.button("Generate PDF"):
-        pdf_file = generate_pdf(content)
-        st.success(f"PDF report generated successfully: {pdf_file}")
+    if symptoms:
+        prediction = predict_disease(symptoms)  # Predict disease based on symptoms
+        st.write(f"Predicted Disease: {prediction}")
 
-        # Option to download the PDF file
-        with open(pdf_file, "rb") as file:
+        # Optionally save the response as a PDF
+        if save_as_pdf:
+            pdf_output = save_to_pdf(prediction)
             st.download_button(
-                label="Download Medical Report",
-                data=file,
-                file_name="medical_report.pdf",
+                label="Download PDF",
+                data=pdf_output.getvalue(),
+                file_name="medical_assistance.pdf",
                 mime="application/pdf"
             )
-
-# Run Streamlit App
-if __name__ == "__main__":
-    st.write("Welcome to the medical assistance system.")
-