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Update app.py
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app.py
CHANGED
@@ -1,7 +1,7 @@
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import gradio as gr
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from transformers import pipeline
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from
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from
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from langchain_google_genai import ChatGoogleGenerativeAI
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import os
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@@ -95,10 +95,10 @@ Notes:
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- The text may have been obtained via OCR, which could result in some errors.
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- Disregard any text enclosed in <>. They separate different parts of the text.
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- If the
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- If the specific question is not present in the task content, mention that the question does not exist in the task response.
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Task type: {task_type}
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Task number: {task_number}
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@@ -114,11 +114,11 @@ llm_model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.7, top_p=0.
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# Define the prompt template
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prompt = PromptTemplate(input_variables=['task_type', 'task_number', 'question', 'content', 'description'], template=initial_prompt)
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# Define the
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)
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def evaluate(task_type, task_number, question, image):
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# Process the image to extract text
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@@ -128,8 +128,8 @@ def evaluate(task_type, task_number, question, image):
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# Select the appropriate description based on user input
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description = descriptions.get((task_type, task_number), "")
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# Run the
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result =
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'task_type': task_type,
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'task_number': task_number,
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'question': question,
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import gradio as gr
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from transformers import pipeline
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from langchain_core.prompts import PromptTemplate
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from langchain_core.runnables import RunnableSequence
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from langchain_google_genai import ChatGoogleGenerativeAI
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import os
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- The text may have been obtained via OCR, which could result in some errors.
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- Disregard any text enclosed in <>. They separate different parts of the text.
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- If the task type is empty, try to identify the task type from the question. If you cannot determine the task type, mention that the task type is unclear.
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- If the question section is empty, try to identify the question in the task content.
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- If the specific question is not present in the task content, mention that the question does not exist in the task response.
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Task type: {task_type}
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Task number: {task_number}
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# Define the prompt template
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prompt = PromptTemplate(input_variables=['task_type', 'task_number', 'question', 'content', 'description'], template=initial_prompt)
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# Define the RunnableSequence
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sequence = RunnableSequence([
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prompt,
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llm_model
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])
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def evaluate(task_type, task_number, question, image):
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# Process the image to extract text
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# Select the appropriate description based on user input
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description = descriptions.get((task_type, task_number), "")
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# Run the sequence
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result = sequence({
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'task_type': task_type,
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'task_number': task_number,
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'question': question,
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