first push

README.md

@@ -0,0 +1,73 @@
+---
+license: mit
+---
+Healthcare Chatbot (FLAN-T5)
+
+📌 Overview
+
+The Healthcare Chatbot is a medical question-answering AI powered by FLAN-T5, a fine-tuned language model. It can provide general guidance on medical topics, symptoms, and treatment suggestions based on a pre-trained dataset.
+
+🚨 Note: This chatbot is for informational purposes only and should not be used as a substitute for professional medical advice. Always consult a doctor for health-related concerns.
+
+📷 Screenshot
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6791349f0df2a77530968217/klDNYjR9JZlRKLmlHHZWP.png)
+
+🚀 How to Install & Run
+
+🔹 Step 1: Download the Project
+
+Option 1: Clone from Hugging Face
+
+git clone https://huggingface.co/alecmoran/Healthcare_Chatbot
+
+cd Healthcare_Chatbot
+
+Option 2: Download as a ZIP
+
+Go to Hugging Face Model Page
+
+Click on "Download"
+
+Extract the ZIP file
+
+🔹 Step 2: Download & Prepare the Model
+
+The chatbot requires FLAN-T5 to be stored locally before running.
+
+For Windows Users 🖥️
+
+Open PowerShell in the project directory.
+
+Run the following command to download the model:
+
+./get_model.ps1
+
+Once the model is downloaded, run the chatbot:
+
+python healthcare_chatbot.py
+
+For macOS/Linux Users 💻
+
+Open Terminal in the project directory.
+
+Run the following command to download the model:
+
+chmod +x get_model.sh && ./get_model.sh
+
+Once the model is downloaded, run the chatbot:
+
+python3 healthcare_chatbot.py
+
+💡 Features
+
+✅ Local Model Loading - Runs FLAN-T5 from your system for faster response times.✅ Medical Q&A Dataset - Includes common questions about symptoms and treatments.✅ Voice Input & Text-to-Speech - Allows users to speak their questions & hear responses.✅ Streamlit UI - Simple and interactive web-based interface.
+
+⚠️ Disclaimer
+
+This chatbot provides general medical information but is not a replacement for professional healthcare advice. Always consult a licensed physician for medical concerns.
+
+📩 Contact & Support
+
+For issues or improvements, open an issue on the Hugging Face repo.
+

getpowershell.ps1

@@ -0,0 +1,20 @@
+# Create directory for the model
+New-Item -ItemType Directory -Path .\flan-t5-small -Force
+# Define the list of model files
+$files = @(
+    "config.json",
+    "pytorch_model.bin",
+    "tokenizer.json",
+    "tokenizer_config.json",
+    "special_tokens_map.json",
+    "vocab.txt"
+)
+# Base URL for the model files
+$base_url = "https://huggingface.co/google/flan-t5-small/resolve/main/"
+# Loop through each file and download it
+foreach ($file in $files) {
+    $url = "$base_url$file"
+    $output = ".\flan-t5-small\$file"
+    Invoke-WebRequest -Uri $url -OutFile $output
+    Write-Host "Downloaded: $file"
+}

helathcare_chatbot.py

@@ -0,0 +1,141 @@
+import os
+import pickle
+import streamlit as st
+import speech_recognition as sr
+import pyttsx3
+
+try:
+    import torch
+    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+except ModuleNotFoundError as e:
+    st.error(f"❌ Missing dependency: {e}. Please install required packages.")
+    st.stop()
+
+# ==============================
+# 1) Model Configuration
+# ==============================
+MODEL_DIR = "flan-t5-small"
+
+torch_dtype = torch.float32 if torch.cuda.is_available() else torch.float32
+
+def load_model():
+    """Load the FLAN-T5 model from local storage."""
+    st.write("🚀 Loading FLAN-T5 model from local storage...")
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR, local_files_only=True)
+        model = AutoModelForSeq2SeqLM.from_pretrained(
+            MODEL_DIR,
+            torch_dtype=torch_dtype,
+        )
+        st.write("✅ Model loaded successfully from local storage!")
+        return tokenizer, model
+    except Exception as e:
+        st.error(f"❌ Model failed to load: {e}")
+        st.stop()
+
+# ==============================
+# 2) Initialize Streamlit UI
+# ==============================
+st.title("🩺 Healthcare Chatbot (FLAN-T5)")
+
+# Load model
+try:
+    tokenizer, model = load_model()
+except Exception as e:
+    st.error(f"❌ Model load error: {e}")
+    st.stop()
+
+# ==============================
+# 3) Load Medical Q&A Data
+# ==============================
+try:
+    st.write("📂 Loading medical Q&A data...")
+    with open("train_data.pkl", "rb") as file:
+        medical_qna = pickle.load(file)
+    st.write("✅ Q&A data loaded!")
+except FileNotFoundError:
+    st.error("❌ 'train_data.pkl' not found. Please ensure it exists.")
+    st.stop()
+except Exception as e:
+    st.error(f"❌ Failed to load Q&A data: {e}")
+    st.stop()
+
+# ==============================
+# 4) Chatbot Response Logic
+# ==============================
+def chatbot_response(user_input: str) -> str:
+    """
+    1. Check if user_input matches any question in medical_qna.
+    2. If so, return that pre-written answer.
+    3. Otherwise, feed FLAN-T5 a prompt to generate a medical response.
+    """
+
+    # Check Q&A first
+    for qa in medical_qna:
+        if user_input.lower() in qa["question"].lower():
+            return qa["answer"]
+
+    # System-style prompt for medical answers
+    prompt = (
+        "You are a helpful medical assistant. The user asked:\n"
+        f"Question: {user_input}\n\n"
+        "Answer in a concise, accurate way. If you're unsure, advise seeing a doctor."
+    )
+
+    # Encode
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
+    # Generate
+    outputs = model.generate(
+        **inputs,
+        max_length=256,
+        num_beams=2,  # for more deterministic, coherent responses
+        no_repeat_ngram_size=2
+    )
+    # Decode
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return answer
+
+# ==============================
+# 5) Text-to-Speech
+# ==============================
+tts_engine = pyttsx3.init()
+
+def speak(text: str):
+    """Speak text aloud."""
+    tts_engine.say(text)
+    tts_engine.runAndWait()
+
+# ==============================
+# 6) Streamlit UI Logic
+# ==============================
+if st.button("What can you help me with?"):
+    st.write(
+        "I can provide general information about medical symptoms, treatments, "
+        "and offer guidance. If you have serious concerns, please contact a doctor."
+    )
+
+user_input = st.text_input("Ask me a medical question:")
+if st.button("Get Answer"):
+    if user_input.strip():
+        response = chatbot_response(user_input)
+        st.write(f"**Bot:** {response}")
+        speak(response)
+    else:
+        st.warning("Please enter a question.")
+
+if st.button("🎙 Use Voice Input"):
+    recognizer = sr.Recognizer()
+    with sr.Microphone() as source:
+        st.write("🎤 Listening...")
+        audio = recognizer.listen(source)
+    try:
+        voice_input = recognizer.recognize_google(audio)
+        st.write(f"🗣 You said: {voice_input}")
+        response = chatbot_response(voice_input)
+        st.write(f"**Bot:** {response}")
+        speak(response)
+    except sr.UnknownValueError:
+        st.write("❌ Could not understand audio.")
+    except sr.RequestError:
+        st.write("❌ Speech recognition service error.")

requirements.txt

@@ -0,0 +1,8 @@
+torch
+transformers
+accelerate
+bitsandbytes
+streamlit
+speechrecognition
+pyttsx3
+huggingface_hub

train_data.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:003162f9e8e8a6050d79e427ec97880cfcad0bc2531e4c7768a28000278c4953
+size 291