tutorial45

ronidas39 · Mar 14, 2024 · 0c96c68 · 0c96c68
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diff --git a/tutorial40/main.py b/tutorial40/main.py
@@ -1,35 +1,75 @@
+import streamlit as st
+import arxiv,os
+from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain.prompts import ChatPromptTemplate
+from pdf2vectore import pdf2VectoreStore
 from langchain_openai import ChatOpenAI
-from langchain.prompts import HumanMessagePromptTemplate,ChatPromptTemplate,PromptTemplate
-from langchain.output_parsers import StructuredOutputParser,ResponseSchema
 from langchain.chains import LLMChain
-
-llm=ChatOpenAI(model="gpt-4",temperature=0.0)
-
-tagline=ResponseSchema(
-    name="tagline",description="generated tagline for the input company description"
-)
-rating=ResponseSchema(
-    name="rating",description="this is a whole number ,generated rating between 1-100 for the input company description"
-)
-rs=[tagline,rating]
-output_parser=StructuredOutputParser.from_response_schemas(rs)
-format_instruction=output_parser.get_format_instructions()
-ts="""
-you are master at suggesting unique tagline for a company based on a input description.
-take the company description below delimited by triple backticks and use it to create the unique tagline.
-input description: ```{input}```
-the based on the input you should create a tagline and popularity score for the generates tagline between 1-100 basaed on your knowledge and analysis.
-{format_instruction}
+template = """Answer the question based only on the following context:
+{context}
+Question: {question}
 """
-prompt=ChatPromptTemplate(
-    messages=[
-        HumanMessagePromptTemplate.from_template(ts)
-    ],
-    input_variables=["input"],
-    partial_variables={
-    "format_instruction":format_instruction},
-    output_parser=output_parser
+prompt = ChatPromptTemplate.from_template(template)
+qa_chain=LLMChain(
+    llm=ChatOpenAI(model="gpt-4",max_tokens=1024),
+    prompt=prompt
 )
-chain=LLMChain(llm=llm,prompt=prompt)
-response=chain.predict_and_parse(input="this is company makes cool and trendy watches for indian youth ")
-print(response["tagline"])
+# Function to handle the logic after submission
+def handle_submission(selected_option, input_text):
+    # Example logic to generate output based on the input text
+    # This can be customized based on your needs
+    output_text = f"You selected '{selected_option}' and submitted: '{input_text}'"
+    return output_text
+
+def download_docs(selected_option):
+    client = arxiv.Client()
+
+# Search for the 10 most recent articles matching the keyword "quantum."
+    search = arxiv.Search(
+    query = "quantum",
+    max_results = 10,
+    sort_by = arxiv.SortCriterion.SubmittedDate
+    )
+
+    results = client.results(search)
+    for result in results:
+        result.download_pdf(dirpath=os.getcwd()+"\\output\\")
+
+
+
+
+
+
+# Setting up the layout
+st.title("Example Streamlit UI")
+
+# Creating two columns for the left and right sides
+col1, col2 = st.columns(2)
+
+# Working on the left side (Column 1)
+with col1:
+    st.header("Input Section")
+    # Dropdown menu for selecting an option
+    options = ["healthcare", "mathematics", "physics","environment","computer science","AI","Quantum computing","space research"]
+    selected_option = st.selectbox("Choose an option", options, index=0, key="select_option")
+
+    # Activating text box and submit button based on dropdown selection
+    if selected_option:
+        download_docs(selected_option)
+        context=pdf2VectoreStore()
+        print("vector store created)")
+        input_text = st.text_area("Text Box", f"Text for {selected_option}", key="input_text")
+        submit_button = st.button("Submit")
+
+# Handling submission and displaying output on the right side (Column 2)
+if submit_button:
+    output = qa_chain.run({"context":context,"question":input_text})
+
+    with col2:
+        st.header("Output Section")
+        st.write(output)
+else:
+    # Showing a message or placeholder in the output section before submission
+    with col2:
+        st.header("Output Section")
+        st.write("Your output will appear here after submission.")
diff --git a/tutorial40/pdf2vectore.py b/tutorial40/pdf2vectore.py
@@ -0,0 +1,31 @@
+from langchain_community.document_loaders import DirectoryLoader,PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import GPT4AllEmbeddings
+from langchain_community.vectorstores import Qdrant
+import os
+
+def pdf2VectoreStore():
+    loader = DirectoryLoader(path=os.getcwd()+"//output/", glob="./*.pdf", loader_cls=PyPDFLoader)
+    docs=loader.load()
+    full_text = ''
+    for doc in docs:
+        full_text += doc.page_content
+    # Split the original text into lines
+    lines = full_text.splitlines()
+    non_empty_lines = []
+    for line in lines:
+        if line:
+            non_empty_lines.append(line)
+    full_text = " ".join(non_empty_lines)
+
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    doc_chunks = text_splitter.create_documents([full_text])
+    qdrant = Qdrant.from_documents(
+    documents=doc_chunks,
+    embedding=GPT4AllEmbeddings(),
+    path="./tmp/local_qdrant",
+    collection_name="arxiv_papers",
+    )
+    retriever = qdrant.as_retriever()
+    return retriever
diff --git a/tutorial44/.env.txt b/tutorial44/.env.txt
diff --git a/tutorial45/.DS_Store b/tutorial45/.DS_Store
diff --git a/tutorial45/main.py b/tutorial45/main.py
@@ -0,0 +1,107 @@
+import requests,io
+
+cookies = {
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+}
+
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+    'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8',
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+    'currency': 'INR',
+    'language': 'eng',
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+    'origin': 'https://www.makemytrip.com',
+    'os': 'desktop',
+    'referer': 'https://www.makemytrip.com/',
+    'region': 'in',
+    'sec-ch-ua': '"Chromium";v="122", "Not(A:Brand";v="24", "Google Chrome";v="122"',
+    'sec-ch-ua-mobile': '?0',
+    'sec-ch-ua-platform': '"Windows"',
+    'sec-fetch-dest': 'empty',
+    'sec-fetch-mode': 'cors',
+    'sec-fetch-site': 'same-site',
+    'server': 'b2c',
+    'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36',
+    'usr-mcid': '12697088725315095381106968647157822291',
+    'vid': '60bfdc9b-b566-4935-8a89-60a727cfffd8',
+    'visitor-id': '60bfdc9b-b566-4935-8a89-60a727cfffd8',
+}
+
+params = {
+    'srcClient': 'DESKTOP',
+    'contextType': 'null',
+    'language': 'eng',
+    'region': 'in',
+    'currency': 'INR',
+    'idContext': 'B2C',
+    'countryCode': 'IN',
+}
+
+json_data = {
+    'filter': {
+        'ota': 'MMT',
+        'travelTagTypes': [],
+    },
+    'sortCriteria': {
+        'sortBy': 'Latest first',
+    },
+    'start': 1,
+    'limit': 1830,
+    'bookingDevice': 'DESKTOP',
+}
+
+response = requests.post(
+    'https://mapi.makemytrip.com/clientbackend/entity/api/hotel/200709211000561392/flyfishReviews',
+    params=params,
+    cookies=cookies,
+    headers=headers,
+    json=json_data,
+)
+
+# Note: json_data will not be serialized by requests
+# exactly as it was in the original request.
+#data = '{"filter":{"ota":"MMT","travelTagTypes":[]},"sortCriteria":{"sortBy":"Latest first"},"start":5,"limit":5,"bookingDevice":"DESKTOP"}'
+#response = requests.post(
+#    'https://mapi.makemytrip.com/clientbackend/entity/api/hotel/200709211000561392/flyfishReviews',
+#    params=params,
+#    cookies=cookies,
+#    headers=headers,
+#    data=data,
+#)
+
+data=response.json()
+data=data["payload"]
+data=data["response"]
+rows=data["MMT"]
+print(len(rows))
+with io.open("review.txt","a",encoding="utf-8")as f1:
+    for row in rows:
+        f1.write(row["reviewText"]+"\n\n")
+f1.close()