Upload 4 files

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+# Function to load and preprocess the data
+def load_data(file):
+    df = pd.read_csv(file, delimiter=";")
+    return df
+
+# Function to process the input and get the most similar question
+def get_most_similar_question(new_sentence, questions, answers, vectorizer, tfidf_matrix):
+    new_tfidf = vectorizer.transform([new_sentence])
+
+    similarities = cosine_similarity(new_tfidf, tfidf_matrix)
+
+    most_similar_index = np.argmax(similarities)
+
+    similarity_percentage = similarities[0, most_similar_index] * 100
+
+    return answers[most_similar_index], similarity_percentage
+
+# Function to generate response
+def answer_the_question(new_sentence, questions, answers, vectorizer, tfidf_matrix):
+    most_similar_answer, similarity_percentage = get_most_similar_question(new_sentence, questions, answers, vectorizer, tfidf_matrix)
+    if similarity_percentage > 70:
+        response = {
+            'answer': most_similar_answer
+        }
+    else:
+        response = {
+            'answer': 'Sorry, I am not aware of this information :('
+        }
+
+    return response
+
+# Streamlit app
+def main():
+    st.title("Q&A Chatbot")
+    st.write("Upload a CSV file with questions and answers.")
+
+    # Upload CSV file
+    uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
+
+    if uploaded_file is not None:
+        df = load_data(uploaded_file)
+        questions = df['question'].tolist()
+        answers = df['answer'].tolist()
+        vectorizer = TfidfVectorizer()
+        tfidf_matrix = vectorizer.fit_transform(questions)
+
+        # Ask question
+        user_question = st.text_input("Ask your question here:")
+        if st.button("Ask"):
+            if user_question:
+                response = answer_the_question(user_question, questions, answers, vectorizer, tfidf_matrix)
+                st.write("Answer:", response['answer'])
+
+if __name__ == "__main__":
+    main()

qna.csv

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+question;answer
+who is pm of India;Modi
+Who is Indian prime minister;Modi
+who is the leader of BJP;Modi
+Who is Indian pm;Modi

train.py

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+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+df = pd.read_csv('qna.csv',encoding = 'utf-8',delimiter=';')
+print(df)
+questions = df['question'].tolist()
+print(questions)
+answers = df['answer'].tolist()
+vectorizer = TfidfVectorizer()
+tfidf_matrix = vectorizer.fit_transform(questions)
+
+def get_most_similar_question(new_sentence):
+    new_tfidf = vectorizer.transform([new_sentence])
+
+    similarities = cosine_similarity(new_tfidf,tfidf_matrix)
+
+    most_similar_index = np.argmax(similarities)
+
+    similarity_percentage = similarities[0, most_similar_index]*100
+
+    return answers[most_similar_index], similarity_percentage
+
+def AnswertheQuestion(new_sentence):
+    most_similar_answer, similarity_percentage = get_most_similar_question(new_sentence)
+    if similarity_percentage > 70:
+        response = {
+            'answer': most_similar_answer
+        
+        }
+    else:
+        response = {
+            'answer': 'Sorry, I am not aware of this information :('
+        }
+
+    return response
+
+print(AnswertheQuestion('Who is the Ninad'))
+