Updating of the file and adding API logic on 30Dec2023 at 23h19

app.py

@@ -1,39 +1,92 @@
-import streamlit as st
 import pandas as pd
 import numpy as np
 import joblib
-from collections import Counter
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+
+app = FastAPI()
+
+class InputData(BaseModel):
+    wavelengths: list
+    SCAN1: list
+    SCAN2: list
+    # Add more fields as needed
 
 model = joblib.load('random_forest_model.joblib')
 scaler = joblib.load('scaler.joblib')
 
-def preprocess_uploaded_file(uploaded_df, scaler, num_scans=15):
-    uploaded_df.columns = ['wavelengths'] + [f'SCAN{i}' for i in range(1, uploaded_df.shape[1])]
-    template_data = {'wavelengths': uploaded_df['wavelengths']}
-    for i in range(1, num_scans + 1):
+def preprocess_input_data(input_data):
+    template_data = {'wavelengths': input_data['wavelengths']}
+    for i in range(1, 16):  # Assuming you have 15 scans
         template_data[f'SCAN{i}'] = 0
     template_df = pd.DataFrame(template_data)
-    for column in uploaded_df.columns:
-        if column in template_df.columns:
-            template_df[column] = uploaded_df[column]
+    for key, value in input_data.items():
+        if key in template_df.columns:
+            template_df[key] = value
     template_df_cleaned = template_df.dropna(axis=1)
     preprocessed_df = scaler.transform(template_df_cleaned)
     return preprocessed_df
 
-st.image('logo.png', caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
+@app.post("/classify")
+def classify(input_data: InputData):
+    try:
+        preprocessed_input = preprocess_input_data(input_data.dict())
+        predictions = model.predict(preprocessed_input)
+        most_common_class = np.argmax(np.bincount(predictions))
+        
+        if most_common_class == 0:
+            return {"result": 'Resistant'}
+        elif most_common_class == 1:
+            return {"result": 'Medium'}
+        elif most_common_class == 2:
+            return {"result": 'Susceptible'}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# End of Code
+
+
+
+
+
+
+# import streamlit as st
+# import pandas as pd
+# import numpy as np
+# import joblib
+# from collections import Counter
+
+# model = joblib.load('random_forest_model.joblib')
+# scaler = joblib.load('scaler.joblib')
+
+# def preprocess_uploaded_file(uploaded_df, scaler, num_scans=15):
+#     uploaded_df.columns = ['wavelengths'] + [f'SCAN{i}' for i in range(1, uploaded_df.shape[1])]
+#     template_data = {'wavelengths': uploaded_df['wavelengths']}
+#     for i in range(1, num_scans + 1):
+#         template_data[f'SCAN{i}'] = 0
+#     template_df = pd.DataFrame(template_data)
+#     for column in uploaded_df.columns:
+#         if column in template_df.columns:
+#             template_df[column] = uploaded_df[column]
+#     template_df_cleaned = template_df.dropna(axis=1)
+#     preprocessed_df = scaler.transform(template_df_cleaned)
+#     return preprocessed_df
+
+# st.image('logo.png', caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
 
-st.markdown("<h3 style='text-align: center; color: grey;'>Hyperspectral Based System For Identification Of Common Bean Genotypes Resistant To Foliar Diseases</h2>", unsafe_allow_html=True)
+# st.markdown("<h3 style='text-align: center; color: grey;'>Hyperspectral Based System For Identification Of Common Bean Genotypes Resistant To Foliar Diseases</h2>", unsafe_allow_html=True)
 
-uploaded_file = st.file_uploader('Choose a CSV file', type='csv')
+# uploaded_file = st.file_uploader('Choose a CSV file', type='csv')
 
-if uploaded_file is not None:
-    input_df = pd.read_csv(uploaded_file)
-    preprocessed_input = preprocess_uploaded_file(input_df, scaler)
-    predictions = model.predict(preprocessed_input)
-    most_common_class = Counter(predictions).most_common(1)[0][0]
-    if most_common_class == 'Resistant':
-        st.write('The Plant is resistant to foliar diseases.')
-    elif most_common_class == 'Medium':
-        st.write('The Plant shows medium resistance to foliar diseases.')
-    elif most_common_class == 'Susceptible':
-        st.write('The Plant is susceptible to foliar diseases.')
+# if uploaded_file is not None:
+#     input_df = pd.read_csv(uploaded_file)
+#     preprocessed_input = preprocess_uploaded_file(input_df, scaler)
+#     predictions = model.predict(preprocessed_input)
+#     most_common_class = Counter(predictions).most_common(1)[0][0]
+#     if most_common_class == 'Resistant':
+#         st.write('The Plant is resistant to foliar diseases.')
+#     elif most_common_class == 'Medium':
+#         st.write('The Plant shows medium resistance to foliar diseases.')
+#     elif most_common_class == 'Susceptible':
+#         st.write('The Plant is susceptible to foliar diseases.')