Create app.py

app.py

@@ -0,0 +1,95 @@
+pip install streamlit transformers
+import streamlit as st
+from transformers import pipeline
+
+# Load the medical diagnosis model from Hugging Face Transformers
+diagnosis_model = pipeline("text-classification", model="your-medical-diagnosis-model-name")
+
+def main():
+    st.title("Medical Diagnosis Assistant")
+
+    # Text input for user to enter symptoms
+    symptoms = st.text_area("Enter symptoms (separated by commas):")
+
+    if st.button("Diagnose"):
+        if symptoms:
+            # Perform medical diagnosis
+            diagnosis_result = diagnose(symptoms)
+            st.success(f"Diagnosis: {diagnosis_result}")
+        else:
+            st.warning("Please enter symptoms for diagnosis.")
+
+def diagnose(symptoms):
+    # Call the Hugging Face model for medical diagnosis
+    result = diagnosis_model(symptoms)
+
+    # Extract the predicted label
+    label = result[0]["label"]
+
+    return label
+
+if __name__ == "__main__":
+    main()
+import streamlit as st
+from transformers import pipeline
+
+# Load the medical diagnosis model from Hugging Face Transformers
+diagnosis_model = pipeline("text-classification", model="your-medical-diagnosis-model-name")
+
+def main():
+    st.title("Medical Diagnosis Assistant")
+
+    # Text input for user to enter symptoms
+    symptoms = st.text_area("Enter symptoms (separated by commas):")
+
+    if st.button("Diagnose"):
+        if symptoms:
+            # Perform medical diagnosis
+            diagnosis_result = diagnose(symptoms)
+            st.success(f"Diagnosis: {diagnosis_result}")
+        else:
+            st.warning("Please enter symptoms for diagnosis.")
+
+def diagnose(symptoms):
+    # Call the Hugging Face model for medical diagnosis
+    result = diagnosis_model(symptoms)
+
+    # Extract the predicted label
+    label = result[0]["label"]
+
+    return label
+
+if __name__ == "__main__":
+    main()
+import streamlit as st
+from transformers import pipeline
+
+# Load the medical diagnosis model from Hugging Face Transformers
+diagnosis_model = pipeline("text-classification", model="your-medical-diagnosis-model-name")
+
+def main():
+    st.title("Medical Diagnosis Assistant")
+
+    # Text input for user to enter symptoms
+    symptoms = st.text_area("Enter symptoms (separated by commas):")
+
+    if st.button("Diagnose"):
+        if symptoms:
+            # Perform medical diagnosis
+            diagnosis_result = diagnose(symptoms)
+            st.success(f"Diagnosis: {diagnosis_result}")
+        else:
+            st.warning("Please enter symptoms for diagnosis.")
+
+def diagnose(symptoms):
+    # Call the Hugging Face model for medical diagnosis
+    result = diagnosis_model(symptoms)
+
+    # Extract the predicted label
+    label = result[0]["label"]
+
+    return label
+
+if __name__ == "__main__":
+    main()
+streamlit run medical_diagnosis_app.py