Create app.py

app.py

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+import os
+import fitz  # PyMuPDF for PDF processing
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import streamlit as st
+from groq import Groq
+from tempfile import NamedTemporaryFile
+
+# Set up the Groq client with your API key
+client = Groq(api_key="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")
+
+# Step 1: Function to extract text from PDF
+def extract_text_from_pdf(pdf_path):
+    doc = fitz.open(pdf_path)
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    doc.close()
+    return text
+
+# Step 2: Function to split extracted text into chunks for retrieval
+def chunk_text(text, chunk_size=1000):
+    words = text.split()
+    chunks = []
+    for i in range(0, len(words), chunk_size):
+        chunk = " ".join(words[i:i+chunk_size])
+        chunks.append(chunk)
+    return chunks
+
+# Step 3: Retrieve the most relevant chunk using TF-IDF and cosine similarity
+def retrieve_chunk(question, chunks):
+    vectorizer = TfidfVectorizer().fit_transform([question] + chunks)
+    question_vector = vectorizer[0]
+    chunk_vectors = vectorizer[1:]
+    similarities = cosine_similarity(question_vector, chunk_vectors).flatten()
+    best_chunk_index = np.argmax(similarities)
+    return chunks[best_chunk_index]
+
+# Step 4: Generate an answer using the Groq API's language model
+def generate_answer(retrieved_text, question):
+    prompt = f"Based on the following text, answer the question:\n\nText: {retrieved_text}\n\nQuestion: {question}"
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": prompt}],
+        model="llama3-8b-8192"
+    )
+    return chat_completion.choices[0].message.content
+
+# Step 5: Streamlit UI for PDF upload and Q&A
+def main():
+    st.title("PDF Question-Answer Chatbot")
+
+    uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
+
+    if uploaded_file:
+        with NamedTemporaryFile(delete=False) as tmp_file:
+            tmp_file.write(uploaded_file.getvalue())
+            pdf_path = tmp_file.name
+
+        # Extract text from the uploaded PDF and chunk it
+        text = extract_text_from_pdf(pdf_path)
+        chunks = chunk_text(text)
+
+        question = st.text_input("Ask a question:")
+        if st.button("Get Answer"):
+            if question:
+                retrieved_text = retrieve_chunk(question, chunks)
+                answer = generate_answer(retrieved_text, question)
+                st.write("Answer:", answer)
+            else:
+                st.write("Please enter a question.")
+
+if __name__ == "__main__":
+    main()