Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the Biomistral 7b model and tokenizer
+model_name = "biomistral/Biomistral-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+# Define the text generation function
+def generate_text(prompt, max_length=500, num_return_sequences=1, temperature=0.7):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+    output = model.generate(
+        input_ids,
+        max_length=max_length,
+        num_return_sequences=num_return_sequences,
+        temperature=temperature,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+    generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+    return generated_text
+
+# Streamlit app
+def main():
+    st.title("Doctor Chatbot (Powered by Biomistral 7b)")
+    st.write("Welcome to the Doctor Chatbot. Please describe your symptoms or ask a medical question, and I'll provide a response.")
+
+    user_input = st.text_area("Enter your symptoms or question:")
+
+    if user_input:
+        with st.spinner("Generating response..."):
+            generated_text = generate_text(user_input)
+            st.write(generated_text[0])
+
+if __name__ == "__main__":
+    main()
+