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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Document[[:space:]](1).pdf filter=lfs diff=lfs merge=lfs -text

Document (1).pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cdb4d733f0f28ec3927a30f31d927e39b56e65d305081111a0c9a020d893c54b
+size 4574439

app (1).py

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+# FINAL COMBINE GRADIO INTERFACE ,WITH THR DEFAULT VALUES and STOP FACILITIES
+
+import PIL.Image as Image
+import gradio as gr
+from ultralytics import YOLO
+import os
+import time
+from langchain_groq import ChatGroq
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
+from dotenv import load_dotenv
+
+load_dotenv()
+groq_api_key = os.getenv('GROQ_API_KEY')
+
+# Initialize object detection model
+model = YOLO("version4c.pt")
+
+# Set default confidence and IoU thresholds
+CONF_THRESHOLD = 0.25
+IOU_THRESHOLD = 0.45
+
+def predict_image(img):
+    # Perform object detection
+    results = model.predict(source=img, conf=CONF_THRESHOLD, iou=IOU_THRESHOLD, show_labels=True, show_conf=True, imgsz=640)
+    # Plot the result
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
+    return im
+
+# Initialize chatbot components
+llm = ChatGroq(groq_api_key=groq_api_key, model_name="Llama3-8b-8192")
+prompt = ChatPromptTemplate.from_template(
+    """ Answer the questions based on the provided context only. Please provide the most accurate response based on the question <context> {context} <context> Questions:{input} """
+)
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+loader = PyPDFLoader("Document.pdf")
+docs = loader.load()
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+final_documents = text_splitter.split_documents(docs)
+# Extract text content from the Document instances
+doc_texts = [doc.page_content for doc in final_documents]
+embeddings_result = embeddings.embed_documents(doc_texts)
+if embeddings_result:
+    vectors = FAISS.from_documents(final_documents, embeddings)
+else:
+    raise ValueError("Failed to generate embeddings. Please check your input documents or try a different embedding model.")
+
+document_chain = create_stuff_documents_chain(llm, prompt)
+retriever = vectors.as_retriever()
+retrieval_chain = create_retrieval_chain(retriever, document_chain)
+
+def print_like_dislike(x: gr.LikeData):
+    print(x.index, x.value, x.liked)
+
+def add_message(history, message):
+    if message is not None:
+        history.append((message, None))
+    return history, gr.Textbox(value=None, interactive=False)
+
+stop_generation = False
+
+def bot(history):
+    global stop_generation
+    stop_generation = False
+    message = history[-1][0]
+    start_time = time.time()
+    response = retrieval_chain.invoke({'input': message})['answer']
+    response_time = time.time() - start_time
+    if response_time > 6:
+        return [(f"Sorry, I couldn't generate a response within 6 seconds. Please try again.", None)]
+    history[-1][1] = ""
+    for character in response:
+        if stop_generation:
+            break
+        history[-1][1] += character
+        time.sleep(0.05)
+        yield history
+
+def stop_response(dummy_placeholder):
+    global stop_generation
+    stop_generation = True
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=2):
+            model_input = gr.Image(type="pil", label="Upload Image")
+            model_output = gr.Image(type="pil", label="Result")
+
+            model_btn = gr.Button("Detect Results")
+            model_btn.click(predict_image, inputs=model_input, outputs=model_output)
+
+        with gr.Column(scale=1):
+            chatbot = gr.Chatbot(
+                [],
+                elem_id="chatbot",
+                bubble_full_width=False
+            )
+
+            chat_input = gr.Textbox(interactive=True, placeholder="Enter message...", show_label=False)
+            chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
+            bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
+            bot_msg.then(lambda: gr.Textbox(interactive=True), None, [chat_input])
+            chatbot.like(print_like_dislike, None, None)
+
+            stop_btn = gr.Button("Stop Generation")
+            stop_btn.click(stop_response, None, None)
+
+    demo.queue()
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -1,8 +1,10 @@
-gradio
-ultralytics
-opencv-python
-pypdf
-langchain_groq
-langchain_community
-langchain==0.1.17
-HuggingFaceEmbeddings
+gradio
+ultralytics
+opencv-python
+pypdf
+sentence-transformers
+langchain_groq
+faiss-cpu
+langchain_community
+langchain==0.1.17
+HuggingFace