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app.py

@@ -1,3 +1,4 @@
+
 import os
 import subprocess
 import sys
@@ -134,23 +135,16 @@ def dummy_analyze(ingredients_list, health_conditions=None):
     return report
 
 # Function to extract text from images using OCR
-# Replace your current extract_text_from_image function with this:
-
 def extract_text_from_image(image):
     try:
         if image is None:
             return "No image captured. Please try again."
 
-        # Print tesseract path for debugging
-        print(f"Using tesseract path: {pytesseract.pytesseract.tesseract_cmd}")
-        
         # Verify Tesseract executable is accessible
         try:
-            result = subprocess.run([pytesseract.pytesseract.tesseract_cmd, "--version"],
+            subprocess.run([pytesseract.pytesseract.tesseract_cmd, "--version"],
                           check=True, capture_output=True, text=True)
-            print(f"Tesseract version check: {result.stdout}")
-        except Exception as e:
-            print(f"Tesseract verification failed: {str(e)}")
+        except (subprocess.SubprocessError, FileNotFoundError):
             return "Tesseract OCR is not installed or not properly configured. Please check installation."
 
         # Import necessary libraries
@@ -433,4 +427,4 @@ with gr.Blocks(title="AI Ingredient Scanner") as app:
 
 # Launch the app
 if __name__ == "__main__":
-    app.launch()
+    app.launch()

requirements.txt

@@ -1,4 +1,9 @@
-google-generativeai
+gradio>=3.50.2
+torch>=2.0.0
+transformers>=4.30.0
+Pillow>=9.0.0
+numpy>=1.24.0
+
 fastapi
 uvicorn
 gradio
@@ -7,10 +12,3 @@ Pillow
 opencv-python-headless
 python-dotenv
 dotenv
-torch>=2.0.0
-transformers>=4.30.0
-numpy>=1.24.0
-pytesseract>=0.3.8
-opencv-python-headless>=4.5.0
-requests>=2.25.0
-

sample_data/README.md

@@ -0,0 +1,19 @@
+This directory includes a few sample datasets to get you started.
+
+*   `california_housing_data*.csv` is California housing data from the 1990 US
+    Census; more information is available at:
+    https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub
+
+*   `mnist_*.csv` is a small sample of the
+    [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
+    described at: http://yann.lecun.com/exdb/mnist/
+
+*   `anscombe.json` contains a copy of
+    [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
+    was originally described in
+
+    Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
+    Statistician. 27 (1): 17-21. JSTOR 2682899.
+
+    and our copy was prepared by the
+    [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).

sample_data/anscombe.json

@@ -0,0 +1,49 @@
+[
+  {"Series":"I", "X":10.0, "Y":8.04},
+  {"Series":"I", "X":8.0, "Y":6.95},
+  {"Series":"I", "X":13.0, "Y":7.58},
+  {"Series":"I", "X":9.0, "Y":8.81},
+  {"Series":"I", "X":11.0, "Y":8.33},
+  {"Series":"I", "X":14.0, "Y":9.96},
+  {"Series":"I", "X":6.0, "Y":7.24},
+  {"Series":"I", "X":4.0, "Y":4.26},
+  {"Series":"I", "X":12.0, "Y":10.84},
+  {"Series":"I", "X":7.0, "Y":4.81},
+  {"Series":"I", "X":5.0, "Y":5.68},
+
+  {"Series":"II", "X":10.0, "Y":9.14},
+  {"Series":"II", "X":8.0, "Y":8.14},
+  {"Series":"II", "X":13.0, "Y":8.74},
+  {"Series":"II", "X":9.0, "Y":8.77},
+  {"Series":"II", "X":11.0, "Y":9.26},
+  {"Series":"II", "X":14.0, "Y":8.10},
+  {"Series":"II", "X":6.0, "Y":6.13},
+  {"Series":"II", "X":4.0, "Y":3.10},
+  {"Series":"II", "X":12.0, "Y":9.13},
+  {"Series":"II", "X":7.0, "Y":7.26},
+  {"Series":"II", "X":5.0, "Y":4.74},
+
+  {"Series":"III", "X":10.0, "Y":7.46},
+  {"Series":"III", "X":8.0, "Y":6.77},
+  {"Series":"III", "X":13.0, "Y":12.74},
+  {"Series":"III", "X":9.0, "Y":7.11},
+  {"Series":"III", "X":11.0, "Y":7.81},
+  {"Series":"III", "X":14.0, "Y":8.84},
+  {"Series":"III", "X":6.0, "Y":6.08},
+  {"Series":"III", "X":4.0, "Y":5.39},
+  {"Series":"III", "X":12.0, "Y":8.15},
+  {"Series":"III", "X":7.0, "Y":6.42},
+  {"Series":"III", "X":5.0, "Y":5.73},
+
+  {"Series":"IV", "X":8.0, "Y":6.58},
+  {"Series":"IV", "X":8.0, "Y":5.76},
+  {"Series":"IV", "X":8.0, "Y":7.71},
+  {"Series":"IV", "X":8.0, "Y":8.84},
+  {"Series":"IV", "X":8.0, "Y":8.47},
+  {"Series":"IV", "X":8.0, "Y":7.04},
+  {"Series":"IV", "X":8.0, "Y":5.25},
+  {"Series":"IV", "X":19.0, "Y":12.50},
+  {"Series":"IV", "X":8.0, "Y":5.56},
+  {"Series":"IV", "X":8.0, "Y":7.91},
+  {"Series":"IV", "X":8.0, "Y":6.89}
+]

sample_data/mnist_test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51c292478d94ec3a01461bdfa82eb0885d262eb09e615679b2d69dedb6ad09e7
+size 18289443

sample_data/mnist_train_small.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ef64781aa03180f4f5ce504314f058f5d0227277df86060473d973cf43b033e
+size 36523880