Create app.py

app.py

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+import os
+import tempfile
+import gradio as gr
+from PIL import Image
+from pdf2image import convert_from_path
+import pytesseract
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain_groq import ChatGroq
+
+
+class ChatbotModel:
+    def __init__(self):
+        os.environ["GROQ_API_KEY"] = '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'
+
+        self.embeddings = HuggingFaceEmbeddings(
+            model_name="sentence-transformers/all-MiniLM-L6-v2",
+            model_kwargs={'device': 'cpu'},
+            encode_kwargs={'normalize_embeddings': True}
+        )
+
+        self.llm = ChatGroq(
+            model='llama3-70b-8192',
+            temperature=0.5,
+            max_tokens=None,
+            timeout=None,
+            max_retries=2,
+        )
+
+        self.memory = ConversationBufferMemory(memory_key="history", input_key="question")
+
+        self.template = """You are an intelligent assistant... (Rest of your prompt as is)"""
+
+        self.QA_CHAIN_PROMPT = PromptTemplate(
+            input_variables=["history", "context", "question"],
+            template=self.template
+        )
+        self.db1 = None
+        self.qa_chain = None
+
+    def ocr_image(self, image_path, language='eng+guj'):
+        img = Image.open(image_path)
+        return pytesseract.image_to_string(img, lang=language)
+
+    def ocr_pdf(self, pdf_path, language='eng+guj'):
+        images = convert_from_path(pdf_path)
+        return "\n".join([pytesseract.image_to_string(img, lang=language) for img in images])
+
+    def process_file(self, uploaded_file):
+        _, file_extension = os.path.splitext(uploaded_file.name)
+        file_extension = file_extension.lower()
+
+        with tempfile.NamedTemporaryFile(delete=False, suffix=file_extension) as temp_file:
+            temp_file.write(uploaded_file.read())
+            temp_path = temp_file.name
+
+        if file_extension == '.pdf':
+            raw_text = self.ocr_pdf(temp_path, language='guj+eng')
+        elif file_extension in ['.jpg', '.jpeg', '.png', '.bmp']:
+            raw_text = self.ocr_image(temp_path, language='guj+eng')
+        else:
+            return "Unsupported file format."
+
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+        text_chunks = text_splitter.split_text(raw_text)
+
+        self.db1 = FAISS.from_documents(text_chunks, self.embeddings)
+        self.qa_chain = RetrievalQA.from_chain_type(
+            self.llm,
+            retriever=self.db1.as_retriever(),
+            chain_type='stuff',
+            verbose=True,
+            chain_type_kwargs={
+                "verbose": True,
+                "prompt": self.QA_CHAIN_PROMPT,
+                "memory": self.memory
+            }
+        )
+
+        return "File processed successfully!"
+
+    def get_response(self, user_input):
+        if not self.qa_chain:
+            return "Please upload and process a file before asking questions."
+        response = self.qa_chain({"query": user_input})
+        return response["result"]
+
+
+chatbot = ChatbotModel()
+
+
+def upload_and_process(file):
+    return chatbot.process_file(file)
+
+
+def ask_question(question):
+    return chatbot.get_response(question)
+
+
+interface = gr.Blocks()
+
+with interface:
+    gr.Markdown("# Educational Chatbot with Document Analysis")
+    with gr.Row():
+        file_upload = gr.File(label="Upload PDF or Image")
+        upload_btn = gr.Button("Process File")
+    output = gr.Textbox(label="File Processing Status")
+
+    with gr.Row():
+        question_box = gr.Textbox(label="Ask a Question")
+        ask_btn = gr.Button("Submit")
+    answer = gr.Textbox(label="Answer")
+
+    upload_btn.click(upload_and_process, inputs=file_upload, outputs=output)
+    ask_btn.click(ask_question, inputs=question_box, outputs=answer)
+
+interface.launch()