Upload app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import fitz  # PyMuPDF for PDF handling
+import re
+import tempfile
+
+# Function to clean extracted text
+def clean_text(text):
+    # Replace multiple spaces or newlines with a single space
+    text = re.sub(r'\s+', ' ', text)
+    return text.strip()
+
+# Function to extract text from PDF and clean it
+def extract_text_from_pdf(uploaded_file):
+    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+        tmp_file.write(uploaded_file.read())
+        tmp_file_path = tmp_file.name
+
+    doc = fitz.open(tmp_file_path)
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    doc.close()
+
+    return clean_text(text)
+
+# Function to chunk large text for context
+def chunk_text(text, max_length=1000):
+    # Split the text into chunks with a maximum character length
+    chunks = [text[i:i+max_length] for i in range(0, len(text), max_length)]
+    return chunks
+
+# Initialize Hugging Face model pipeline
+@st.cache_resource
+def load_model():
+    return pipeline("text-generation", model="gpt2", clean_up_tokenization_spaces=True)  # Use GPT-2 for simplicity
+
+model_pipeline = load_model()
+
+# Generate a response
+# Generate a response
+def get_response(prompt, context):
+    combined_prompt = f"Context: {context}\n\nQuestion: {prompt}\nAnswer:"
+    response = model_pipeline(
+        combined_prompt,
+        max_new_tokens=150,  # Ensure concise answers
+        num_return_sequences=1
+    )
+    # Extract only the answer part after the "Answer:" in the generated response
+    raw_response = response[0]["generated_text"]
+    answer_start = raw_response.find("Answer:") + len("Answer:")
+    answer = raw_response[answer_start:].strip()
+    return clean_text(answer)
+
+
+# Streamlit App UI
+st.title("Chat with PDF!!!")
+
+# Sidebar for description
+st.sidebar.title("Instructions")
+st.sidebar.markdown("""
+    ### How to Use This Application:
+    1. **Upload a PDF File**:  
+       Use the file uploader to select and upload the PDF file you wish to analyze. The file should be in `.pdf` format.
+    
+    2. **Text Extraction**:  
+       The application extracts the text from the uploaded PDF using the `PyMuPDF` library (imported as `fitz`).
+
+    3. **Text Chunking**:  
+       Large documents are divided into smaller chunks for better processing. Each chunk contains up to 1000 characters.
+
+    4. **Ask Questions**:  
+       After the text is processed, type your question about the document.
+
+    5. **Model Response**:  
+       The app sends the prompt and the relevant chunk to the model, which generates a response based on the content.
+
+    6. **Receive Insights**:  
+       Get detailed insights and answers related to the PDF content.
+""")
+
+uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf"])
+
+if uploaded_file is not None:
+    # Extract and clean text from the uploaded PDF
+    pdf_text = extract_text_from_pdf(uploaded_file)
+    
+    # Chunk the extracted text
+    text_chunks = chunk_text(pdf_text)
+    
+    # Display the first chunk as a summary
+    st.subheader("PDF Content Summary:")
+    st.write(text_chunks[0])  # Display first chunk
+    
+    # Input for user prompt
+    prompt = st.text_area(label="Ask a question based on the PDF content")
+    button = st.button("Ok")
+    
+    if button:
+        if prompt:
+            # Select relevant chunk based on the question
+            relevant_chunk = None
+            for chunk in text_chunks:
+                if any(keyword.lower() in chunk.lower() for keyword in prompt.split()):
+                    relevant_chunk = chunk
+                    break
+
+            # If no relevant chunk was found, use the first chunk as a fallback
+            if not relevant_chunk:
+                relevant_chunk = text_chunks[0]
+
+            # Get response from the model
+            response = get_response(prompt, relevant_chunk)
+            st.markdown(f"**Answer:** {response}")