Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Set up the device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("adi2606/MenstrualQA").to(device)
+tokenizer = AutoTokenizer.from_pretrained("adi2606/MenstrualQA")
+
+# Function to generate a response from the chatbot
+def generate_response(message: str, temperature: float = 0.4, repetition_penalty: float = 1.1) -> str:
+    inputs = tokenizer(message, return_tensors="pt").to(device)
+
+    # Generate the response
+    output = model.generate(
+        inputs['input_ids'],
+        max_length=512,
+        temperature=temperature,
+        repetition_penalty=repetition_penalty,
+        do_sample=True
+    ) 
+
+    # Decode the generated output
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
+
+# Streamlit app layout
+st.title("Menstrual QA Chatbot")
+st.write("Ask any question related to menstrual health.")
+
+# User input
+user_input = st.text_input("You:", "")
+
+if st.button("Send"):
+    if user_input:
+        with st.spinner("Generating response..."):
+            response = generate_response(user_input)
+        st.write(f"Chatbot: {response}")
+    else:
+        st.write("Please enter a question.")