Update app.py

app.py

@@ -53,24 +53,23 @@ def parse_ticket_image(image, question):
     
     # Define questions you want to ask the model
     
-    questions = ["What is the ticket number?"]
-        #{"question": "What is the type of grain (For example: corn, soy, wheat)?", "context": image},
-        #{"question": "What is the date?", "context": image},
-        #{"question": "What is the time?", "context": image},
-        #{"question": "What is the gross weight?", "context": image},
-        #{"question": "What is the tare weight?", "context": image},
-        #{"question": "What is the net weight?", "context": image},
-        #{"question": "What is the moisture (moist) percentage?", "context": image},
-        #{"question": "What is the damage percentage?", "context": image},
-        #{"question": "What is the gross units?", "context": image},
-        #{"question": "What is the dock units?", "context": image},
-        #{"question": "What is the comment?", "context": image},
-        #{"question": "What is the assembly number?", "context": image},
-    #]
+    questions = [
+        "What is the ticket number?", 
+        "What is the type of grain (For example: corn, soy, wheat)?", 
+        "What is the date?", 
+        "What is the time?", 
+        "What is the gross weight?",  
+        "What is the tare weight?", 
+        "What is the net weight?", 
+        "What is the moisture (moist) percentage?", 
+        "What is the damage percentage?",
+        "What is the gross units?",
+        "What is the dock units?", 
+        "What is the comment?", 
+        "What is the assembly number?",
+    ]
     
     # Use the model to answer each question
-    #results = [model(q["question"], q["context"]) for q in questions]
-
     answers = {}
     for i, q in enumerate(questions): 
         answer_text = process_question(q, document)
@@ -79,36 +78,36 @@ def parse_ticket_image(image, question):
         
         
     ticket_number = answers[0]
-    # Extract answers from the results
-    #ticket_number = results[0][0]['answer']
-    #date = results[1][0]['answer']
-    #time = results[2][0]['answer']
-    #gross_weight = results[3][0]['answer']
-    #tare_weight = results[4][0]['answer']
-    #net_weight = results[5][0]['answer']
-    #moisture = results[6][0]['answer']
-    #damage = results[7][0]['answer']
-    #gross_units = results[8][0]['answer']
-    #dock_units = results[9][0]['answer']
-    #comment = results[10][0]['answer']
-    #assembly_number = results[11][0]['answer']
+    grain_type = answers[1]
+    date = answers[2]
+    time = answers[3]
+    gross_weight = answers[4]
+    tare_weight = answers[5]
+    net_weight = answers[6]
+    moisture = answers[7]
+    damage = answers[8]
+    gross_units = answers[9]
+    dock_units = answers[10]
+    comment = answers[11]
+    assembly_number = answers[12]
+
     
     # Create a structured format (like a table) using pandas
     data = {
-        "Ticket Number": [ticket_number]
+        "Ticket Number": [ticket_number],
+        "Grain Type": [grain_type],
+        "Assembly Number": [assembly_number],
+        "Date": [date],
+        "Time": [time],
+        "Gross Weight": [gross_weight],
+        "Tare Weight": [tare_weight],
+        "Net Weight": [net_weight],
+        "Moisture": [moisture],
+        "Damage": [damage],
+        "Gross Units": [gross_units],
+        "Dock Units": [dock_units],
+        "Comment": [comment],
     }
-        #"Assembly Number": [assembly_number],
-        #"Date": [date],
-        #"Time": [time],
-        #"Gross Weight": [gross_weight],
-        #"Tare Weight": [tare_weight],
-        #"Net Weight": [net_weight],
-        #"Moisture": [moisture],
-        #"Damage": [damage],
-        #"Gross Units": [gross_units],
-        #"Dock Units": [dock_units],
-        #"Comment": [comment],
-    #}
     df = pd.DataFrame(data)
     
     return df