Update app.py

app.py

@@ -1,41 +1,20 @@
 # -*- coding: utf-8 -*-
 """saivv_protoype"""
 
-# Install libraries (if not installed by requirements.txt)
-try:
-    import speech_recognition as sr
-except ImportError:
-    pip install speechrecognition
-
-try:
-    import pytesseract
-except ImportError:
-    pip install pytesseract
-
-try:
-    import gradio as gr
-except ImportError:
-    pip install gradio
-
+# Import necessary libraries
 import cv2  # For image processing with OpenCV
 import pytesseract  # For Optical Character Recognition (OCR) on receipts
 import gradio as gr  # For creating the Gradio interface
+import speech_recognition as sr  # For voice recognition
 
-# Only necessary pip installs; Hugging Face Spaces handles requirements.txt installs
-try:
-    from torch import cuda, bfloat16
-    import transformers
-    from transformers import AutoTokenizer
-    import torch
-    from langchain.llms import HuggingFacePipeline
-    from langchain.chains import RetrievalQA
-    from langchain.vectorstores import chroma
-except ImportError:
-    pip install langchain langchain-community langchain-core transformers
-    pip install bitsandbytes accelerate
+# Model setup (using transformers)
+import torch
+from transformers import AutoTokenizer
+from langchain.llms import HuggingFacePipeline
 
+# Initialize device and model config
+from torch import cuda, bfloat16
 model_id = 'HuggingFaceH4/zephyr-7b-beta'
-
 device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
 bnb_config = transformers.BitsAndBytesConfig(
     load_in_4bit=True,
@@ -71,15 +50,9 @@ query_pipeline = transformers.pipeline(
     device_map="auto"
 )
 
-from IPython.display import display, Markdown
-def colorize_text(text):
-    for word, color in zip(["Reasoning", "Question", "Answer", "Total time"], ["blue", "red", "green", "magenta"]):
-        text = text.replace(f"{word}:", f"\n\n**<font color='{color}'>{word}:</font>**")
-    return text
-
 llm = HuggingFacePipeline(pipeline=query_pipeline)
 
-# Define structured user profile and question
+# User profile setup
 user_profile = """
 User Profile:
 Age: 40, Gender: Non-Binary, Marital Status: Divorced, Income Level: Medium ($2733),
@@ -92,14 +65,18 @@ Home Shopping: $235.68, Others: $253.45
 """
 
 question = "Based on this data, can I buy a Lamborghini?"
-
-# Combine structured data into prompt
 prompt = f"{user_profile}\n\nQuestion: {question}"
 
-# Send prompt to LLM
+# Get response from LLM
 response = llm(prompt=prompt)
 
-# Display the result with Markdown formatting
+# Display result
+from IPython.display import display, Markdown
+def colorize_text(text):
+    for word, color in zip(["Reasoning", "Question", "Answer", "Total time"], ["blue", "red", "green", "magenta"]):
+        text = text.replace(f"{word}:", f"\n\n**<font color='{color}'>{word}:</font>**")
+    return text
+
 full_response = f"**Question:** {question}\n\n**Answer:** {response}"
 display(Markdown(colorize_text(full_response)))