First commit

app.py

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+import streamlit as st
+from transformers import AutoModel, AutoTokenizer
+
+# Load BioMistral model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B")
+model = AutoModel.from_pretrained("BioMistral/BioMistral-7B")
+
+def generate_response(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=200, do_sample=True, top_p=0.95, top_k=50, num_return_sequences=1)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+def main():
+    st.title("Doctor AI App")
+
+    # Get user input
+    symptoms = st.text_area("Enter your symptoms", height=150)
+    medical_history = st.text_area("Enter your medical history", height=150)
+    allergies = st.text_area("Enter your allergies (if any)", height=150)
+
+    if st.button("Get Diagnosis and Recommendations"):
+        # Prepare input for BioMistral
+        input_text = f"Based on the following information:\n\nSymptoms: {symptoms}\nMedical History: {medical_history}\nAllergies: {allergies}\n\nPlease provide a diagnosis and recommend medications or treatments."
+
+        # Generate response using BioMistral
+        response = generate_response(input_text)
+
+        st.write(response)
+
+if __name__ == "__main__":
+    main()