Create app.py

app.py

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+import os
+import streamlit as st
+from groq import Groq
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+API_KEY = os.getenv("GROQ_API_KEY")
+
+# Initialize Groq client
+client = Groq(api_key=API_KEY)
+
+# Define health topics
+HEALTH_TOPICS = [
+    "Coronary artery disease", "Heart attack", "Hypertension", "Stroke",
+    "Diabetes", "Obesity", "Thyroid disorders", "Cancer", "Depression",
+    "Anxiety", "Asthma", "Pneumonia", "IBS", "Kidney disease", "Arthritis",
+    "HIV/AIDS", "Flu", "COVID-19", "Skin conditions", "Hair loss"
+]
+
+# Function to check if a question is health-related
+def is_health_related(question):
+    return any(topic.lower() in question.lower() for topic in HEALTH_TOPICS)
+
+# Function to get AI response
+def get_response(question):
+    if not is_health_related(question):
+        return "❌ Sorry, I only answer health-related questions."
+
+    response = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=[{"role": "user", "content": question}],
+        temperature=1,
+        max_tokens=1024,
+        top_p=1,
+        stream=False,
+    )
+    return response.choices[0].message.content
+
+# Streamlit UI
+st.set_page_config(page_title="Health Assistant Chatbot", layout="wide")
+st.title("🩺 AI Health Assistant Chatbot")
+
+# User input
+user_input = st.text_area("Ask a health-related question:")
+
+# Process user input
+if st.button("Get Answer"):
+    if user_input:
+        with st.spinner("Thinking..."):
+            answer = get_response(user_input)
+            st.success(answer)
+    else:
+        st.warning("Please enter a health-related question.")
+
+# Upload and transcribe audio
+st.header("🎙️ Ask via Voice")
+audio_file = st.file_uploader("Upload your question (M4A, MP3, WAV)", type=["m4a", "mp3", "wav"])
+
+if audio_file:
+    with open("temp_audio.m4a", "wb") as f:
+        f.write(audio_file.read())
+
+    with st.spinner("Transcribing..."):
+        with open("temp_audio.m4a", "rb") as file:
+            transcription = client.audio.transcriptions.create(
+                file=("temp_audio.m4a", file.read()),
+                model="whisper-large-v3-turbo",
+                response_format="verbose_json",
+            )
+
+        transcript_text = transcription.text
+        st.success(f"Transcription: {transcript_text}")
+
+        # Auto-answer transcribed question
+        if transcript_text:
+            answer = get_response(transcript_text)
+            st.success(f"AI Response: {answer}")