Upload app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+
+# Download necessary NLTK data
+nltk.download('punkt')
+nltk.download('stopwords')
+
+
+# Load a pre-trained Hugging Face model
+chatbot = pipeline("text-generation", model="distilgpt2")
+
+
+# Define healthcare-specific response logic (or use a model to generate responses)
+def healthcare_chatbot(user_input):
+    # Simple rule-based keywords to respond
+    if "symptom" in user_input:
+        return "It seems like you're experiencing symptoms. Please consult a doctor for accurate advice."
+    elif "appointment" in user_input:
+        return "Would you like me to schedule an appointment with a doctor?"
+    elif "medication" in user_input:
+        return "It's important to take your prescribed medications regularly. If you have concerns, consult your doctor."
+    else:
+        # For other inputs, use the Hugging Face model to generate a response
+        response = chatbot(user_input, max_length=300, num_return_sequences=1)
+        # Specifies the maximum length of the generated text response, including the input and the generated tokens.
+        # If set to 3, the model generates three different possible responses based on the input.
+        return response[0]['generated_text']
+
+
+# Streamlit web app interface
+def main():
+    # Set up the web app title and input area
+    st.title("Healthcare Assistant Chatbot")
+    
+    # Display a simple text input for user queries
+    user_input = st.text_input("How can I assist you today?", "")
+    
+    # Display chatbot response
+    if st.button("Submit"):
+        if user_input:
+            st.write("User: ", user_input)
+            response = healthcare_chatbot(user_input)
+            st.write("Healthcare Assistant: ", response)
+        else:
+            st.write("Please enter a query.")
+
+if __name__ == "__main__":
+    main()