6 update repo description to emphasize prototyping

README.md

@@ -1,3 +1,56 @@
-# harvester-prototype
 
-This repository is for the frontend of the project react
+# Harvester Prototype (FertiScan)
+
+## Overview Description
+
+---
+The FertiScan project, initiated by the AI-Lab, aims to streamline the process
+of capturing and organizing information on fertilizer labels. By leveraging AI
+technology, the mobile application will allow users to scan fertilizer labels,
+extract essential data, and automatically load this data into a database for
+easy access and analysis. This project involves the AI-Lab, the CFIA, and
+clients such as inspectors and teams who will benefit from the streamlined
+process. FertiScan is a web application that facilitates the inspection process
+wherever internet access is available. By automating fertilizer bag inspection,
+the project aims to increase the efficiency and accuracy of the inspection
+process. Through the integration of the Gemini Vertex API, the system can
+analyze fertilizer bag labels more thoroughly, identifying not only basic
+information but also subtle differences that may escape human inspection.
+The Harvester prototype is a crucial step towards realizing a complete
+automated inspection system. With continuous testing and improvements, it has
+the potential to transform the way fertilizer bag inspections are conducted,
+bringing an advanced technological solution to the agricultural sector.
+
+## Key Features
+
+---
+**Gemini Vertex API:** An application programming interface designed to
+simulate human conversation, capable of answering questions and processing
+a large number of images.
+**Testing Process:** Rigorous testing is conducted to demonstrate that
+the Gemini Vertex API, created by Google, is the appropriate method for the
+Harvester project.
+It meets various criteria and achieves two main objectives:
+reading labels during recording and identifying differences with bags analyzed
+by inspectors.
+
+## Potential Advantages
+
+---
+**Increased Efficiency:** By automating fertilizer bag inspection, the project
+aims to increase the efficiency and accuracy of the inspection process.
+**Advanced Analysis:** Through the integration of the Gemini Vertex API, the
+system can analyze fertilizer bag labels more thoroughly, identifying not only
+basic information but also subtle differences that may escape human inspection.
+
+## Future Outlook
+
+The Harvester prototype is a crucial step toward realizing a
+comprehensive automated inspection system. With continuous testing and
+improvements, it has the potential to transform the way fertilizer bag
+inspections are conducted, bringing an advanced technological
+solution to the agricultural sector.
+
+## References
+
+You can find more information on the official site of Google gemini vertex ai: <https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini?hl=fr>

app.py

@@ -20,13 +20,13 @@ def getListImage(image_paths):
         with open(image_data, 'rb') as f:
             image = f.read()
             request.append(Part.from_data(data=image, mime_type="image/jpeg"))
-            
+
     return request
 
 def addText(request, number, baseQuestions=None):
     if baseQuestions is None:
         request.append(create_base_request(read_csv_file("base_composition_questions.csv")))
-    else:  
+    else:
         request.append(create_final_request(read_csv_file("questions_spreadsheet.csv"), baseQuestions))
 
     return request
@@ -53,20 +53,20 @@ def generateRequest(directory, model:GenerativeModel, vertex:vertexai, baseQuest
         }
     ),
         stream=False,
-    )   
+    )
 
     if baseQuestions is None:
         baseQuestions = toDict(responses.text)
     #else:
        # otherQuestion = toDict(responses, baseQuestions)
-       
+
     print(responses.text)
-    
+
     return baseQuestions
 
 def toDict(responses, baseQuestions=None):
     responseAsDict = {}
-    if baseQuestions is None: 
+    if baseQuestions is None:
         for response in responses:
             texte = response
             indice = texte.find(":")
@@ -101,10 +101,10 @@ def  create_final_request(file, baseQuestions, typeOfQuestion):
 
                     if "seed" in categorie and "is_seed" in baseQuestions:
                         request_AsText = addLine(request_AsText, line)
-                    
+
                     if "tank_mixing" in categorie and "is_tank_mixing" in baseQuestions:
                         request_AsText = addLine(request_AsText, line)
-                    
+
                     if "microorganism" in categorie and "contain_microorganism" in baseQuestions:
                         request_AsText = addLine(request_AsText, line)
 
@@ -127,10 +127,10 @@ def  create_final_request(file, baseQuestions, typeOfQuestion):
 
                     if "seed" in categorie & "is_seed" in baseQuestions:
                         request_AsText = addLine(request_AsText, line)
-                    
+
                     if "tank_mixing" in categorie & "is_tank_mixing" in baseQuestions:
                         request_AsText = addLine(request_AsText, line)
-                    
+
                     if "microorganism" in categorie & "contain_microorganism" in baseQuestions:
                         request_AsText = addLine(request_AsText, line)
 
@@ -149,7 +149,7 @@ def  create_final_request(file, baseQuestions, typeOfQuestion):
                 if "mixture_product" in categorie and "is_mixture_product" in baseQuestions:
                     request_AsText = addLine(request_AsText, line)
                 return request_AsText
-                
+
 
 def addLine(requestAsText, line, typeOfQuestion):
     if not line.isnull().all():
@@ -163,24 +163,24 @@ def create_base_request(file):
         if not line.isnull().all():
             specification = line['Specification']
             requestAsText += str(specification) + "\n"
-        
+
         line_text = str(line['Key']) + ":" + str(line['Question']) + "; \n"
         requestAsText += line_text
     return requestAsText
 
-
-
 
-
+
+
+
 model = GenerativeModel("gemini-1.0-pro-vision-001")
 projectinit=vertexai.init(project="test-application-2-416219", location="northamerica-northeast1")
 #Original question
-baseQuestions = generateRequest('company_image_folder\\Acti_Sol1', model, projectinit, None, "Original_question")
-baseQuestions = generateRequest('company_image_folder\\Acti_Sol1', model, projectinit, baseQuestions, "Original_question")
+baseQuestions = generateRequest('company_image_folder\\acti_sol1', model, projectinit, None, "original_question")
+baseQuestions = generateRequest('company_image_folder\\acti_sol1', model, projectinit, baseQuestions, "original_question")
 # Modified question
-baseQuestions = generateRequest('company_image_folder\\Acti_Sol1', model, projectinit, None, "Modified_question")
-baseQuestions = generateRequest('company_image_folder\\Acti_Sol1', model, projectinit, baseQuestions, "Modified_question")
+baseQuestions = generateRequest('company_image_folder\\acti_sol1', model, projectinit, None, "modified_question")
+baseQuestions = generateRequest('company_image_folder\\acti_sol1', model, projectinit, baseQuestions, "modified_question")
 
-#generateRequest('Company_Image_Folder\Bio_Fleur', model, projectinit)
-#generateRequest('Company_Image_Folder\Bio_Fleur', model, projectinit)
-#generateRequest('Company_Image_Folder\Bio_Fleur', model, projectinit)
+#generateRequest('Company_Image_Folder\\Bio_Fleur', model, projectinit)
+#generateRequest('Company_Image_Folder\\Bio_Fleur', model, projectinit)
+#generateRequest('Company_Image_Folder\\Bio_Fleur', model, projectinit)