Upload app.py

app.py

@@ -0,0 +1,137 @@
+import subprocess
+subprocess.run('pip install -r requirements.txt', shell = True)
+
+import gradio as gr
+import os
+from PIL import Image
+import numpy as np
+from transformers import pipeline
+import transformers
+transformers.logging.set_verbosity_error()
+from langchain_community.document_loaders import TextLoader
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_fireworks import ChatFireworks
+from langchain_community.llms import Ollama
+from langchain_core.prompts import ChatPromptTemplate
+from rich.console import Console
+from rich.markdown import Markdown
+
+transformers.logging.set_verbosity_error()
+
+
+
+def image_to_query(image):
+    """
+    input: Image
+
+    function: Performs image classification using fine-tuned model
+
+    output: Query for the LLM
+    """
+    image = Image.open('test.jpg')
+    classifier = pipeline("image-classification", model = "nprasad24/bean_classifier")
+
+    scores = classifier(image)
+
+    # Get the dictionary with the maximum score
+    max_score_dict = max(scores, key=lambda x: x['score'])
+
+    # Extract the label with the maximum score
+    label_with_max_score = max_score_dict['label']
+
+    # script to check if the image uploaded is indeed a leaf or not
+    counter = 0
+    for ele in scores:
+        if 0.2 <= ele['score'] <= 0.4:
+            counter += 1
+
+    if label_with_max_score == 'healthy' and counter != 3:
+        query = "The plant is healthy. Give tips on maintaining the plant"
+    elif label_with_max_score == 'bean_rust' and counter != 3:
+        query = "The detected disease is bean rust. Explain the disease"
+    elif label_with_max_score == 'angular_leaf_spot' and counter != 3:
+        query = "The detected disease is angular leaf spot. Explain the disease"
+    else:
+        query = "Given image is not of a plant."
+
+    return query
+
+def ragChain():
+    """
+    function: creates a rag chain
+
+    output: rag chain
+    """
+    loader = TextLoader("knowledgeBase.txt")
+    docs = loader.load()
+
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    docs = text_splitter.split_documents(docs)
+
+    vectorstore = FAISS.from_documents(documents = docs, embedding = HuggingFaceEmbeddings())
+    retriever = vectorstore.as_retriever(search_type = "similarity", search_kwargs = {"k": 5})
+
+    APIKEY = "o7T3gVx9Vt8GSJbLyPV1974vF8LXVp01CWqOkWQuHgoHm07H"
+    os.environ["FIREWORKS_API_KEY"] = APIKEY
+
+    llm = ChatFireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct")
+
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                """You are a knowledgeable agricultural assistant. If a disease is detected, you have to give information on the disease. 
+                If the plant is healthy, just give maintenance tips. """
+            ),
+            (
+                "human",
+                """Provide information about the leaf disease in question in bullet points. 
+                Start your answer by mentioning the disease (if any) or healthy in this format: 'Condition: disease name'.
+                If the image is not of a plant, ask human to upload image of a plant and stop generating any response.
+                """,
+            ),
+            
+            ("human", "{context}, {question}"),
+        ]
+    )
+
+    rag_chain = (
+    { 
+    "context": retriever,
+    "question": RunnablePassthrough()
+    }
+    | prompt
+    | llm
+    | StrOutputParser()
+    )
+
+    return rag_chain
+
+def generate_response(rag_chain, query):
+    """
+    input: rag chain, query
+
+    function: generates response using llm and knowledge base
+
+    output: generated response by the llm
+    """
+    return Markdown(rag_chain.invoke(f"{query}"))
+
+def main():
+    console = Console()
+
+    query = image_to_query('test2.jpeg')
+    chain = ragChain()
+    output = generate_response(chain, query)
+    output = Markdown(output)
+    return output
+
+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)