Update app.py

app.py

@@ -1,7 +1,8 @@
 import subprocess
-subprocess.run('pip install -r requirements.txt', shell = True)
+#subprocess.run('pip install -r requirements.txt', shell = True)
 
 import gradio as gr
+import cv2
 import os
 from PIL import Image
 import numpy as np
@@ -17,7 +18,6 @@ from langchain_core.runnables import RunnablePassthrough
 from langchain_fireworks import ChatFireworks
 from langchain_community.llms import Ollama
 from langchain_core.prompts import ChatPromptTemplate
-from transformers import AutoModelForImageClassification, AutoImageProcessor
 from rich.console import Console
 from rich.markdown import Markdown
 
@@ -34,7 +34,9 @@ def image_to_query(image):
     output: Query for the LLM
     """
     #image = Image.open(image)
-    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    #image = cv2.imread(image)
+    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image =  Image.fromarray(image)
 
     model = AutoModelForImageClassification.from_pretrained("nprasad24/bean_classifier", from_tf=True)
     image_processor = AutoImageProcessor.from_pretrained("nprasad24/bean_classifier")
@@ -125,7 +127,8 @@ def generate_response(rag_chain, query):
 
     output: generated response by the llm
     """
-    return Markdown(rag_chain.invoke(f"{query}"))
+    #return Markdown(rag_chain.invoke(f"{query}"))
+    return rag_chain.invoke(f"{query}")
 
 def main(image):
     console = Console()
@@ -135,8 +138,10 @@ def main(image):
     output = generate_response(chain, query)
     return output
 
+#main('test2.jpeg')
+
 title = "Bean Classifier and Instructor"
 description = "Professor Bean is an agricultural expert. He will guide you on how to protect your plants from bean diseases"
 app = gr.Interface(fn=main, inputs="image", outputs="text", title=title,
     description=description)
-app.launch(share=True)
+app.launch(share=True)