Update app.py

app.py

@@ -2,6 +2,46 @@ import streamlit as st
 from PyPDF2 import PdfReader
 import io
 
+import requests
+import os
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM
+my_token = os.getenv('my_repo_token')
+def find_most_relevant_context(contexts, question, max_features=10000):
+    # Vectorize contexts and question with limited features
+    tfidf_vectorizer = TfidfVectorizer(max_features=max_features)
+    tfidf_matrix = tfidf_vectorizer.fit_transform([question] + contexts)
+    
+    # Compute cosine similarity between question and contexts
+    similarity_scores = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:]).flatten()
+    
+    # Get index of context with highest similarity
+    most_relevant_index = similarity_scores.argmax()
+    
+    return contexts[most_relevant_index]
+
+
+
+
+
+
+
+
+API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
+headers = {"Authorization": f"Bearer {my_token}"}
+
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json()
+	
+output = query({
+	"inputs": instruction,
+})
+
+
 # Mock function for answering questions from the PDF
 # Replace this with your actual backend function
 def answer_question_from_pdf(pdf_text, question):
@@ -13,9 +53,11 @@ def answer_question_from_pdf(pdf_text, question):
 def extract_text_from_pdf(pdf_file):
     pdf_reader = PdfReader(pdf_file)
     pdf_text = ""
+    pdf_arr = []
     for page_num in range(len(pdf_reader.pages)):
-        pdf_text += pdf_reader.pages[page_num].extract_text()
-    return pdf_text
+        pdf_text = pdf_reader.pages[page_num].extract_text()
+        pdf_arr.append(pdf_text)
+    return pdf_arr
 
 # Streamlit app
 st.title("PDF Explorer")
@@ -25,12 +67,13 @@ uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
 
 if uploaded_file is not None:
     # Extract text from uploaded PDF
-    pdf_text = extract_text_from_pdf(uploaded_file)
+    pdf_arr = extract_text_from_pdf(uploaded_file)
     
     st.write("PDF Uploaded Successfully.")
     
     # Text area for entering a question
     question = st.text_input("Ask a question about the PDF")
+    pdf_text = find_most_relevant_context(pdf_arr,question)
     
     if st.button("Get Answer"):
         if question: