created project

.gitignore

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+.venv

README.md

@@ -1,11 +1,11 @@
 ---
 title: Physiotherapy Assistant
-emoji: 🌍
+emoji: 🏋️‍♂️🧑‍⚕️
 colorFrom: pink
 colorTo: yellow
 sdk: streamlit
 sdk_version: 1.42.1
-app_file: app.py
+app_file: src/app.py
 pinned: false
 ---
 

embedding_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:714cd4a98832cd66bfbd5c191ad1bd834d562adcc4c71b213c06fb746fac0022
+size 556171912

requirements.txt

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+streamlit
+pandas
+groq
+scikit-learn
+sentence_transformers

src/app.py

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+import streamlit as st
+from streamlit_app import model,home_page,embedding_model
+import numpy as np
+
+
+def main():
+   
+    home_page.page_config()
+
+    home_page.page_title()
+
+    username = home_page.get_or_greet_user_name()
+    
+    if username:
+
+        home_page.display_chat()
+
+        home_page.handle_user_input()
+
+if __name__ == "__main__":
+    main()

src/streamlit_app/embedding_model.py

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+from sentence_transformers import SentenceTransformer, util
+from sklearn.linear_model import LogisticRegression
+import pickle
+from sklearn.model_selection import train_test_split
+from sklearn.metrics.pairwise import cosine_similarity
+import torch
+import pandas as pd
+import os
+
+topics = [
+    "Ankle Sprain : Recovery exercises for ankle sprains and rehabilitation tips.",
+    "Back Pain : Best practices for relieving back pain and strengthening exercises.",
+    "Shoulder Injury : Shoulder injury rehabilitation, strengthening, and recovery techniques."
+]
+
+def get_encoding_model():
+    model_encode = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
+    save_model(model_encode,"embedding_model.pkl")
+
+def get_embedding(text):
+    if not os.path.exists("embedding_model.pkl"):
+        loaded_model = get_encoding_model()
+
+    model_encode = load_model("embedding_model.pkl")
+    embedding = model_encode.encode(text)
+    return embedding
+
+def save_model(model, filename):
+    with open(filename, 'wb') as model_file:
+        pickle.dump(model, model_file)
+    print(f"Model saved to {filename}")
+
+
+def load_model(filename):
+    # loaded_model = joblib.load('log_reg_model.pkl')
+    with open(filename, 'rb') as model_file:
+        loaded_model = pickle.load(model_file)
+    print(f"Model loaded from {filename}")
+    return loaded_model
+
+def get_cosine_similarity(prompt):
+    message = "Phsyiotherapy"
+    embendding = get_embedding([message,prompt])
+    similarity = util.cos_sim(embendding[0], embendding[1]).item()
+    return round(similarity, 4)

src/streamlit_app/home_page.py

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+import streamlit as st
+from streamlit_app import model,embedding_model
+from utils import logger
+
+
+def page_config():
+     st.set_page_config(page_title="Physio Assist", layout="wide")
+     logger.log("info","Configuring the page layout and title")
+
+def page_title():
+    st.title("🏋️‍♂️🧑‍⚕️ Welcome to Physio Assist: Your Physical Therapy Companion")
+    logger.log("info", "Setting the title to 'Physio Assist: Your Physical Therapy Companion'")
+
+def get_or_greet_user_name():
+    if 'user_name' not in st.session_state:
+        st.session_state.user_name = None
+        logger.log("info","user_name not found in session_state, setting to None.")
+
+    if st.session_state.user_name is None:
+        logger.log("info","user_name is None, requesting user input.")
+        user_name = st.text_input("Please let me know your name:",
+                              placeholder="Enter your name buddy")
+        if user_name:
+            st.session_state.user_name = user_name
+            logger.log("info", f"User entered name: {user_name}. Setting session_state.user_name.")
+            st.rerun()
+    else:
+        logger.log("info", f"User already entered a name: {st.session_state.user_name}. Displaying greeting.")
+        return st._bottom.subheader(f"Hello {st.session_state.user_name}! How can I assist you today?")
+
+def display_chat():
+    logger.log("info","Displaying the chat history.")
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+        logger.log("info", f"Displayed {len(st.session_state.messages)} messages from the chat history.")
+
+def handle_user_input():
+    logger.log("info", "Waiting for user input...")
+    prompt = st.chat_input("Ask me anything related to physiotherapy. E.g., 'How can I recover from a sprained ankle?'")
+
+    if prompt :
+        with st.chat_message("user"):
+            st.markdown(prompt)
+    if prompt:
+        st.session_state.messages.append({"role": "user", "content": prompt})
+
+        similarity = embedding_model.get_cosine_similarity(prompt)
+        if similarity >= 0.3:
+
+            with st.spinner("Processing your query..."):
+                try:
+                    response = model.get_physiotherapy_assistant_response(prompt)
+                    with st.chat_message("assistant"):
+                        st.markdown(response)
+                    st.session_state.messages.append({"role": "assistant", "content": response})
+                    logger.log("info",f"Assistant response: {response}")
+                except Exception as e:
+                    st.error(f"An error occurred while processing your query: {str(e)}")
+                    logger.log("error", f"Error processing user query: {str(e)}")
+        else:
+            response = "Please ask me anything related to physiotherapy"
+            with st.chat_message("assistant"):
+                st.markdown(response)
+            st.session_state.messages.append({"role": "assistant", "content": response})

src/streamlit_app/model.py

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+from groq import Groq
+
+client = Groq(
+    api_key="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",
+)
+
+def get_physiotherapy_assistant_response(prompt : str):
+    try:
+        system_message = """You are a helpful physiotherapy assistant, trained to provide useful information about exercises, recovery, and treatments. 
+        You can help users with various physical therapy-related queries."""
+
+        if not prompt or len(prompt.strip()) < 5:
+            return "Please provide more details about your physiotherapy question. I need more context to assist you effectively."
+
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {"role": "system", "content": system_message},
+                {"role": "user", "content": prompt},
+            ],
+            model="llama-3.3-70b-versatile",
+        )
+        assistant_response = chat_completion.choices[0].message.content
+        return assistant_response
+    except Exception as e:
+        return f"An error occurred while processing your request: {str(e)}"

src/utils/logger.py

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+import logging
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    datefmt='%Y-%m-%d %H:%M')
+
+def log(log_type,message):
+    try:
+        log_type = log_type.lower()
+        if log_type == "info":
+            return logging.info(message)
+        elif log_type == "debug":
+            return logging.debug(message)
+        elif log_type == "warning":
+            return logging.error(message)
+        elif log_type == "critical":
+            return logging.critical(message)
+        elif log_type == "error":
+            return logging.error(message)
+    except Exception as e:
+            return f"Invalid log type.Error : {e}"