Delete app.py

app.py

@@ -1,215 +0,0 @@
-import gradio as gr
-from groq import Groq, RateLimitError
-import pandas as pd
-from PIL import Image
-import pytesseract
-import pdfplumber
-from pdf2image import convert_from_path
-import os
-import time
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-load_dotenv()
-
-# Set the path to Tesseract executable
-pytesseract.pytesseract.tesseract_cmd = os.getenv("TESSERACT_CMD")
-
-# Set the path to Poppler for PDF image extraction
-poppler_path = os.getenv("POPPLER_PATH")
-
-# Your Groq API key
-YOUR_GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-
-# Initialize Groq client
-client = Groq(api_key=YOUR_GROQ_API_KEY)
-
-# Global variable to store extracted text
-extracted_text = ""
-
-def extract_text_from_image(image):
-    return pytesseract.image_to_string(image)
-
-def remove_header_footer(image, header_height=3.9, footer_height=2.27):
-    width, height = image.size
-    header_height_pixels = int(header_height * 96)  # Convert inches to pixels (assuming 96 DPI)
-    footer_height_pixels = int(footer_height * 96)
-    cropping_box = (0, header_height_pixels, width, height - footer_height_pixels)
-    return image.crop(cropping_box)
-
-def handle_file(file, page_range=None):
-    global extracted_text
-    extracted_text = ""
-
-    if file is None:
-        return None, "No file uploaded"
-
-    file_name = file.name.lower()
-
-    if file_name.endswith(('png', 'jpg', 'jpeg')):
-        image = Image.open(file)
-        extracted_text = extract_text_from_image(image)
-        return image, extracted_text
-
-    elif file_name.endswith('pdf'):
-        text = ""
-        pdf_images = []
-        start_page = 1
-        end_page = None
-
-        if page_range:
-            try:
-                start_page, end_page = map(int, page_range.split('-'))
-            except ValueError:
-                start_page = int(page_range)
-                end_page = start_page
-
-        with pdfplumber.open(file) as pdf_file:
-            total_pages = len(pdf_file.pages)
-            end_page = end_page or total_pages
-
-            for page_number in range(start_page - 1, end_page):
-                page = pdf_file.pages[page_number]
-                page_text = page.extract_text() or ""
-                text += f"Page {page_number + 1}:\n{page_text}\n"
-
-                try:
-                    page_images = convert_from_path(file.name, first_page=page_number + 1, last_page=page_number + 1, poppler_path=poppler_path)
-                    page_images = [remove_header_footer(img) for img in page_images]
-                    pdf_images.extend(page_images)
-                    for img in page_images:
-                        image_text = extract_text_from_image(img)
-                        text += f"Page {page_number + 1} (Image):\n{image_text}\n"
-                except Exception as e:
-                    text += f"Error processing images on page {page_number + 1}: {e}\n"
-
-        extracted_text = text
-        if pdf_images:
-            return pdf_images[0], extracted_text
-        else:
-            return None, extracted_text
-
-    elif file_name.endswith(('xls', 'xlsx')):
-        df = pd.read_excel(file)
-        extracted_text = df.to_string()
-        return None, extracted_text
-
-    elif file_name.endswith('csv'):
-        df = pd.read_csv(file)
-        extracted_text = df.to_string()
-        return None, extracted_text
-
-    else:
-        return None, "Unsupported file type"
-
-def split_text(text, max_length=2000):
-    words = text.split()
-    chunks = []
-    current_chunk = []
-    current_length = 0
-    for word in words:
-        word_length = len(word) + 1  # +1 for the space or punctuation
-        if current_length + word_length > max_length:
-            chunks.append(" ".join(current_chunk))
-            current_chunk = [word]
-            current_length = word_length
-        else:
-            current_chunk.append(word)
-            current_length += word_length
-    if current_chunk:
-        chunks.append(" ".join(current_chunk))
-    return chunks
-
-def is_rate_limited():
-    # Implement a method to check rate limit status if needed
-    return False
-
-def chat_groq_sync(user_input, history, extracted_text):
-    retries = 5
-    while retries > 0:
-        rate_limit_status = is_rate_limited()
-        if rate_limit_status:
-            return f"{rate_limit_status} Please try again later."
-
-        messages = [{"role": "system", "content": "The following text is extracted from the uploaded file:\n" + extracted_text}]
-        for msg in history:
-            messages.append({"role": "user", "content": msg[0]})
-            messages.append({"role": "assistant", "content": msg[1]})
-        messages.append({"role": "user", "content": user_input})
-
-        try:
-            response = client.chat.completions.create(
-                model="llama3-70b-8192",
-                messages=messages,
-                max_tokens=1000,
-                temperature=0.4
-            )
-
-            response_content = response.choices[0].message.content
-            return response_content
-        except RateLimitError as e:
-            error_info = e.args[0] if e.args else {}
-            error_message = error_info.get('error', {}).get('message', '') if isinstance(error_info, dict) else str(error_info)
-
-            wait_time = 60
-            if 'try again in' in error_message:
-                try:
-                    wait_time = float(error_message.split('try again in ')[-1].split('s')[0])
-                except ValueError:
-                    pass
-
-            print(f"Rate limit error: {error_message}")
-            print(f"Retrying in {wait_time:.2f} seconds...")
-            retries -= 1
-            if retries > 0:
-                time.sleep(wait_time)
-            else:
-                return "Rate limit exceeded. Please try again later."
-        except Exception as e:
-            print(f"An unexpected error occurred: {e}")
-            return "An unexpected error occurred. Please try again later."
-
-def update_chat(user_input, history):
-    global extracted_text
-    response = chat_groq_sync(user_input, history, extracted_text)
-    history.append((user_input, response))
-    return history, history, ""
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("# RAG Chatbot")
-            gr.Markdown("Check out the [GitHub](https://github.com/anshh-arora?tab=repositories) for more information.")
-
-            file = gr.File(label="Upload your file")
-            page_range = gr.Textbox(label="If the uploaded document is a PDF and has more than 10 pages, enter the page range (e.g., 1-3) or specific page number (e.g., 2):", lines=1, visible=False, interactive=True)
-            file_upload_button = gr.Button("Upload File")
-            image_display = gr.Image(label="Uploaded Image", visible=False)
-            extracted_text_display = gr.Textbox(label="Extracted Text", interactive=False)
-
-        with gr.Column(scale=3):
-            gr.Markdown("# Chat with your file")
-            history = gr.State([])
-            with gr.Column():
-                chatbot = gr.Chatbot(height=500, bubble_full_width=False)
-                user_input = gr.Textbox(placeholder="Enter Your Query", visible=True, scale=7, interactive=True)
-
-                clear_btn = gr.Button("Clear")
-                undo_btn = gr.Button("Undo")
-
-                user_input.submit(update_chat, [user_input, history], [chatbot, history, user_input])
-                clear_btn.click(lambda: ([], []), None, [chatbot, history])
-                undo_btn.click(lambda h: h[:-2], history, history)
-
-    def show_page_range_input(file):
-        if file and file.name.lower().endswith('pdf'):
-            with pdfplumber.open(file) as pdf_file:
-                if len(pdf_file.pages) > 10:
-                    return gr.update(visible=True)
-        return gr.update(visible=False)
-
-    file.change(show_page_range_input, inputs=file, outputs=page_range)
-    file_upload_button.click(handle_file, [file, page_range], [image_display, extracted_text_display])
-
-if __name__ == "__main__":
-    demo.launch(share=True)