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Update app.py
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app.py
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# app.py
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import streamlit as st
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from groq import Groq
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import os
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import requests
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from bs4 import BeautifulSoup
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import re
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from urllib.parse import quote_plus
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from langdetect import detect
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# Groq API setup
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client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
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# Web scraping functions
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def google_search(query):
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
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}
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encoded_query = quote_plus(query)
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url = f"https://www.google.com/search?q={encoded_query}&gl=us&hl=en"
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try:
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response = requests.get(url, headers=headers)
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soup = BeautifulSoup(response.text, 'html.parser')
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results = []
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for g in soup.find_all('div', class_='tF2Cxc'):
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link = g.find('a')['href']
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title = g.find('h3').text
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snippet = g.find('div', class_='VwiC3b')
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if snippet:
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results.append({
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'title': title,
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'link': link,
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'snippet': snippet.text
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})
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return results[:3]
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except Exception as e:
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st.error(f"Search Error: {str(e)}")
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return []
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# Chatbot processing
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def multilingual_chatbot(user_input):
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try:
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# Detect input language
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lang = detect(user_input)
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# Step 1: Symptom extraction
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symptom_prompt = f"""Extract
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{user_input}
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Example Output:
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symptom_response = client.chat.completions.create(
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messages=[{"role": "user", "content": symptom_prompt}],
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model="llama3-70b-8192",
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temperature=0.
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)
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symptoms = symptom_response.choices[0].message.content.split(", ")
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# Step 2:
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search_query =
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results = google_search(search_query)
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#
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final_response = client.chat.completions.create(
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messages=[{"role": "user", "content": response_prompt}],
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return final_response.choices[0].message.content
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except Exception as e:
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return f"Error: {str(e)}"
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# Streamlit UI
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st.set_page_config(page_title="Homeo Assistant", page_icon="🌿")
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st.title("🌐 Multilingual Homeopathy Advisor")
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = [{"role": "assistant", "content": "Describe your symptoms in any language"}]
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# Display chat messages
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Chat input
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if prompt := st.chat_input("Type your symptoms..."):
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st.session_state.messages.append({"role": "user", "content": prompt})
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with st.chat_message("assistant"):
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with st.spinner("Analyzing symptoms..."):
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response = multilingual_chatbot(prompt)
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st.markdown(response)
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st.session_state.messages.append({"role": "assistant", "content": response})
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# Disclaimer
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st.markdown("""
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---
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**⚠️ Disclaimer:**
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This is not medical advice. Always consult a qualified practitioner.
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""")
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# app.py (Updated Version)
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def multilingual_chatbot(user_input):
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try:
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# Detect input language
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lang = detect(user_input)
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# Step 1: Symptom extraction with homeopathy focus
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symptom_prompt = f"""Extract ONLY homeopathic-relevant symptoms from this text as English comma-separated list:
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{user_input}
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Example Output: stinging pain, anxiety worse evenings, thirstlessness"""
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symptom_response = client.chat.completions.create(
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messages=[{"role": "user", "content": symptom_prompt}],
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model="llama3-70b-8192",
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temperature=0.1
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)
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symptoms = symptom_response.choices[0].message.content.split(", ")
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# Step 2: Focused homeopathic web search
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search_query = (
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f"homeopathic remedy for {' '.join(symptoms)} "
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f"(site:.edu OR site:.gov) "
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f"-allopathic -conventional -pharmaceutical"
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)
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results = google_search(search_query)
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# Filter homeopathic-specific results
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homeo_keywords = ['homeopath', 'remedy', 'potency', 'materia medica']
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filtered_results = [
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r for r in results
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if any(kw in r['snippet'].lower() for kw in homeo_keywords)
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]
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# Step 3: Generate strict homeopathic response
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response_prompt = f"""You are a homeopathic doctor. Recommend ONLY homeopathic medicines in {lang} for these symptoms: {', '.join(symptoms)}.
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Use this research data: {[r['snippet'] for r in filtered_results]}
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Include:
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- Medicine name in English
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- Potency (like 30C, 200CK)
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- Administration method
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- Key symptoms it addresses
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- Source reference"""
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final_response = client.chat.completions.create(
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messages=[{"role": "user", "content": response_prompt}],
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return final_response.choices[0].message.content
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except Exception as e:
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return f"Error: {str(e)}"
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