/home/adminuser/venv/lib/python3.10/site-packages/streamlit/runtime/scriptru
nner/script_runner.py:565 in _run_script
/mount/src/optimization-of-npk-anlaysis-using-deep-learning/app.py:267 in
<module>
264 if a == "Home":
265 │ display_Home()
266 elif a == "Analysis":
❱ 267 │ display_Analysis()
268 elif a == "Contact Us":
269 │ display_Contact_us()
270
/mount/src/optimization-of-npk-anlaysis-using-deep-learning/app.py:160 in
display_Analysis
157 │ if st.button("Submit"):
158 │ │ tabs = st.tabs(['Result', 'Visual Representation'])
159 │ │ with tabs[0]:
❱ 160 │ │ │ process_analysis(nitrogen, phosphorus, potassium, Temperat
161 │ │ with tabs[1]:
162 │ │ │ st.subheader('Visual Representation')
163 │ │ │ if tabs[1].is_active:
/mount/src/optimization-of-npk-anlaysis-using-deep-learning/app.py:194 in
process_analysis
191 │ │ None
192 │ """
193 │ l = [n, p, k, T, H, aph]
❱ 194 │ crop = solve(l)
195 │ crop = ''.join(crop)
196 │ display_result(crop, l)
197
/mount/src/optimization-of-npk-anlaysis-using-deep-learning/app.py:237 in
solve
234 │ label_encoder = LabelEncoder()
235 │ y_encoded = label_encoder.fit_transform(y)
236 │ rf_classifier = joblib.load(rf_model_path)
❱ 237 │ model = tf.keras.models.load_model(rnn_model_path)
238 │ rf_prediction = rf_classifier.predict(testing_data)
239 │ rnn_prediction = np.argmax(model.predict(np.expand_dims(testing_da
240 │ cmb_predc = (rf_prediction + rnn_prediction) / 2
/home/adminuser/venv/lib/python3.10/site-packages/keras/utils/traceback_util
s.py:70 in error_handler
67 │ │ │ filtered_tb = _process_traceback_frames(e.__traceback__)
68 │ │ │ # To get the full stack trace, call:
69 │ │ │ # `tf.debugging.disable_traceback_filtering()`
❱ 70 │ │ │ raise e.with_traceback(filtered_tb) from None
71 │ │ finally:
72 │ │ │ del filtered_tb
73
/home/adminuser/venv/lib/python3.10/site-packages/keras/engine/input_layer.p
y:152 in __init__
149 │ │ │ │ batch_size = batch_input_shape[0]
150 │ │ │ │ input_shape = batch_input_shape[1:]
151 │ │ if kwargs:
❱ 152 │ │ │ raise ValueError(
153 │ │ │ │ f"Unrecognized keyword arguments: {list(kwargs.keys())
154 │ │ │ )
155
────────────────────────────────────────────────────────────────────────────────
ValueError: Unrecognized keyword arguments: ['batch_shape']
I’m not able to find any errors while running it in my local machine but I’m getting this errors after deploying it into the streamlit cloud. app is deployed but to get the output we have to click the submit button after clicking it got the above error.
Can anyone able to look into this once.
Here is my Repo Link - Github Repo
Below error is the onscreen output
Traceback:
File "/home/adminuser/venv/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 565, in _run_script
exec(code, module.__dict__)File "/mount/src/optimization-of-npk-anlaysis-using-deep-learning/app.py", line 267, in <module>
display_Analysis()File "/mount/src/optimization-of-npk-anlaysis-using-deep-learning/app.py", line 160, in display_Analysis
process_analysis(nitrogen, phosphorus, potassium, Temperature, Humidity, PH_Value)File "/mount/src/optimization-of-npk-anlaysis-using-deep-learning/app.py", line 194, in process_analysis
crop = solve(l)File "/mount/src/optimization-of-npk-anlaysis-using-deep-learning/app.py", line 237, in solve
model = tf.keras.models.load_model(rnn_model_path)File "/home/adminuser/venv/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from NoneFile "/home/adminuser/venv/lib/python3.10/site-packages/keras/engine/input_layer.py", line 152, in __init__
raise ValueError(