Update app.py

app.py

@@ -7,86 +7,63 @@ from langchain.vectorstores import FAISS
 from langchain.embeddings import HuggingFaceInstructEmbeddings
 from langchain.chains import RetrievalQA
 from langchain import HuggingFaceHub
-import streamlit as st
 from langchain.utilities import GoogleSerperAPIWrapper
+import gradio as gr
+
+import pandas as pd
+df = pd.read_csv('linkss.csv')
+url = []
+for i in df.itertuples():
+  url.append(f"{i[1]}")
+  
 
 class Chatbot:
     def __init__(self):
         os.environ["Hugging_Face_API_KEY"] = "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"
         os.environ["HUGGINGFACEHUB_API_TOKEN"] = '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'
         os.environ["SERPER_API_KEY"] = "a69857e460dd51585e009a43743711b110b6beee"
-        
+
+        self.load_data()
+        self.load_embeddings()
+        self.create_qa_model()
+
     def load_data(self):
-        urls = [
-            'https://zollege.in/exams/bitsat',
-            'https://zollege.in/exams/cat',
-            'https://zollege.in/exams/gate',
-            'https://zollege.in/exams/neet',
-            'https://zollege.in/exams/lsat',
-            'https://zollege.in/exams/jee-advanced',
-            'https://zollege.in/exams/aipmcet'
-        ]
+        urls = url
 
         loaders = UnstructuredURLLoader(urls=urls)
-        data = loaders.load()
-
-        return data
+        self.data = loaders.load()
 
-    def split_documents(self, data):
+    def split_documents(self):
         text_splitter = CharacterTextSplitter(separator='\n', chunk_size=500, chunk_overlap=20)
-        docs = text_splitter.split_documents(data)
+        self.docs = text_splitter.split_documents(self.data)
 
-        return docs
-
-    def create_embeddings(self, docs):
+    def create_embeddings(self):
         instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="sembeddings/model_gpt_trained")
-        db_instructEmbedd = FAISS.from_documents(docs, instructor_embeddings)
-        retriever = db_instructEmbedd.as_retriever(search_kwargs={"k": 3})
+        db_instructEmbedd = FAISS.from_documents(self.docs, instructor_embeddings)
+        self.retriever = db_instructEmbedd.as_retriever(search_kwargs={"k": 3})
 
         with open("db_instructEmbedd.pkl", "wb") as f:
             pickle.dump(db_instructEmbedd, f)
 
-        return retriever
-
     def load_embeddings(self):
         with open("db_instructEmbedd.pkl", "rb") as f:
-            retriever = pickle.load(f)
-        
-        retriever = retriever.as_retriever(search_kwargs={"k": 3})
-        return retriever
+            self.retriever = pickle.load(f)
 
-    def create_qa_model(self, retriever):
-        llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.1})
-        qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
-        return qa
+        self.retriever = self.retriever.as_retriever(search_kwargs={"k": 3})
 
-    def run_chatbot(self):
-        st.title('Chatbot Trained on Indian Exam Articles')
-        st.header("Hi!! How Can I Help You ?")
+    def create_qa_model(self):
+        llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.1})
+        self.qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=self.retriever, return_source_documents=True)
 
-        query = st.text_input('> ')
+    def chat(self, query):
         result = self.qa({'query': query})
-        st.write(result['result'])
-        st.button('Not Satisfied! Talk to our Expert Here..')
+        return result['result']
 
-    def run_google_search(self, query):
-        search = GoogleSerperAPIWrapper()
-        return search.run(query)
-        
-if __name__ == "__main__":
-    chatbot = Chatbot()
-    data = chatbot.load_data()
-    docs = chatbot.split_documents(data)
-    retriever = chatbot.create_embeddings(docs)
-    retrievers = chatbot.load_embeddings()
-    qa = chatbot.create_qa_model(retrievers)
+chatbot = Chatbot()
 
-    st.title('Chatbot Trained on Indian Exam Articles')
-    st.header("Hi!! How Can I Help You ?")
+def chatbot_response(query):
+    response = chatbot.chat(query)
+    return response
 
-    query = st.text_input('ENTER TEXT HERE ')
-    result = qa({'query': query})
-    st.write(result['result'])
-    if st.button('Not Satisfied! Talk to our Expert Here..'):
-        st.write(run_google_search(query))
-    
+iface = gr.Interface(fn=chatbot_response, inputs="text", outputs="text", title="Chatbot Trained on Indian Exam Articles")
+iface.launch()