Update app.py

app.py

@@ -1,49 +1,156 @@
+# import streamlit as st
+# from fpdf import FPDF
+# import os
+
+# llm_result = """
+# Diagnosis: Pneumonia
+
+# Prescription:
+# - Amoxicillin 500 mg, twice daily for 7 days
+# - Paracetamol 500 mg, every 6 hours for fever
+# - Rest and hydration
+# - Follow-up in 7 days if symptoms persist
+# """
+
+# def save_pdf(content):
+#     pdf = FPDF()
+#     pdf.add_page()
+#     pdf.set_font("Arial", size=12)
+#     pdf.multi_cell(0, 10, txt=content)
+
+#     pdf_output_path = "prescription.pdf"
+#     pdf.output(pdf_output_path)
+    
+#     # Return the path to download
+#     return pdf_output_path
+
+# # Streamlit app
+# def main():
+#     st.title("Doctor's Assistance: Review and Edit Prescription")
+    
+#     st.write("## Review the LLM-generated prescription and make edits if necessary.")
+#     edited_text = st.text_area("Edit Prescription", value=llm_result, height=300)
+
+#     if st.button("Save Prescription"):
+#         if edited_text.strip():
+#             pdf_file_path = save_pdf(edited_text)
+#             st.success("Prescription saved!")
+#             with open(pdf_file_path, "rb") as file:
+#                 st.download_button(
+#                     label="Download Prescription as PDF",
+#                     data=file,
+#                     file_name="prescription.pdf",
+#                     mime="application/pdf"
+#                 )
+#         else:
+#             st.error("Prescription content is empty. Please add details.")
+
+# if __name__ == "__main__":
+#     main()
 import streamlit as st
+import speech_recognition as sr
+import pyttsx3
 from fpdf import FPDF
-import os
 
-llm_result = """
-Diagnosis: Pneumonia
+# Initialize speech recognition and TTS engine
+recognizer = sr.Recognizer()
+engine = pyttsx3.init()
 
-Prescription:
-- Amoxicillin 500 mg, twice daily for 7 days
-- Paracetamol 500 mg, every 6 hours for fever
-- Rest and hydration
-- Follow-up in 7 days if symptoms persist
-"""
+def listen_to_voice():
+    """Listen to audio and convert to text."""
+    with sr.Microphone() as source:
+        st.write("Listening for symptoms (speak now)...")
+        audio = recognizer.listen(source)
+        try:
+            text = recognizer.recognize_google(audio)
+            st.write(f"You said: {text}")
+            return text
+        except sr.UnknownValueError:
+            st.write("Sorry, I did not understand the audio.")
+        except sr.RequestError:
+            st.write("Sorry, there was an issue with the speech recognition service.")
+    return ""
 
-def save_pdf(content):
+def tts(text):
+    """Convert text to speech."""
+    engine.say(text)
+    engine.runAndWait()
+
+def predict_disease(symptoms):
+    """Mock prediction of disease based on symptoms."""
+    # Dummy response for demo purposes
+    if 'fever' in symptoms.lower():
+        return "You might have the flu."
+    else:
+        return "No prediction available."
+
+def generate_pdf(content):
+    """Generate PDF with the given content."""
     pdf = FPDF()
     pdf.add_page()
     pdf.set_font("Arial", size=12)
-    pdf.multi_cell(0, 10, txt=content)
+    pdf.multi_cell(200, 10, content)
+    # Save the PDF to a file
+    pdf_file = 'medical_report.pdf'
+    pdf.output(pdf_file)
+    return pdf_file
+
+# Streamlit App
+st.title("Medical Assistance for Doctors (Voice and Text)")
+
+# Step 1: Doctor inputs symptoms through voice or typing
+st.header("Enter Symptoms")
+symptoms = ""
+if st.button("Use Voice Input for Symptoms"):
+    symptoms = listen_to_voice()
+
+typed_symptoms = st.text_area("Or Enter Symptoms Using Keyboard:", symptoms)
+if typed_symptoms:
+    symptoms = typed_symptoms
 
-    pdf_output_path = "prescription.pdf"
-    pdf.output(pdf_output_path)
+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}")
     
-    # Return the path to download
-    return pdf_output_path
+    # Step 3: Allow editing through text or voice
+    st.header("Edit Response (Optional)")
+    if st.button("Edit Response Using Voice"):
+        edited_response = listen_to_voice()
+        if edited_response:
+            prediction = edited_response
+    else:
+        edited_response = st.text_area("Or Edit Response Using Keyboard:", prediction)
+        if edited_response:
+            prediction = edited_response
 
-# Streamlit app
-def main():
-    st.title("Doctor's Assistance: Review and Edit Prescription")
+    # Final Response
+    st.write(f"Final Response: {prediction}")
     
-    st.write("## Review the LLM-generated prescription and make edits if necessary.")
-    edited_text = st.text_area("Edit Prescription", value=llm_result, height=300)
-
-    if st.button("Save Prescription"):
-        if edited_text.strip():
-            pdf_file_path = save_pdf(edited_text)
-            st.success("Prescription saved!")
-            with open(pdf_file_path, "rb") as file:
-                st.download_button(
-                    label="Download Prescription as PDF",
-                    data=file,
-                    file_name="prescription.pdf",
-                    mime="application/pdf"
-                )
-        else:
-            st.error("Prescription content is empty. Please add details.")
+    # Option to listen to the final response
+    if st.button("Play Final Response"):
+        tts(prediction)
+
+    # Step 4: Generate PDF
+    st.header("Generate PDF Report")
+    content = f"Symptoms: {symptoms}\nPrediction: {prediction}"
+    if st.button("Generate PDF"):
+        pdf_file = generate_pdf(content)
+        st.success(f"PDF report generated successfully: {pdf_file}")
 
+    # Step 5: Download PDF
+    with open(pdf_file, "rb") as file:
+        st.download_button(
+            label="Download Medical Report",
+            data=file,
+            file_name="medical_report.pdf",
+            mime="application/pdf"
+        )
+
+# Run Streamlit App
 if __name__ == "__main__":
-    main()
+    st.write("Welcome to the medical assistance system.")
+