Create app.py

app.py

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+import gradio as gr
+import os
+from PIL import Image
+import pytesseract
+from pdf2image import convert_from_path
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain.memory import ConversationBufferMemory
+from langchain_groq import ChatGroq
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+# Initialize the Groq API Key and the model
+os.environ["GROQ_API_KEY"] = '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'
+llm = ChatGroq(
+    model='llama3-70b-8192',
+    temperature=0.5,
+    max_tokens=None,
+    timeout=None,
+    max_retries=2
+)
+
+# OCR functions
+def ocr_image(image_path, language='eng+guj'):
+    img = Image.open(image_path)
+    return pytesseract.image_to_string(img, lang=language)
+
+def ocr_pdf(pdf_path, language='eng+guj'):
+    images = convert_from_path(pdf_path)
+    all_text = "\n".join(pytesseract.image_to_string(img, lang=language) for img in images)
+    return all_text
+
+def ocr_file(file_path):
+    ext = os.path.splitext(file_path)[1].lower()
+    if ext == ".pdf":
+        return ocr_pdf(file_path)
+    elif ext in [".jpg", ".jpeg", ".png", ".bmp"]:
+        return ocr_image(file_path)
+    else:
+        return "Unsupported file format."
+
+def get_text_chunks(text):
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+    return splitter.split_text(text)
+
+def get_vector_store(chunks):
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_store = FAISS.from_texts(chunks, embedding=embeddings)
+    os.makedirs("faiss_index", exist_ok=True)
+    vector_store.save_local("faiss_index")
+    return vector_store
+
+# Conversational chain
+def get_conversational_chain():
+    template = """<Insert your prompt here>"""
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-MiniLM-L6-v2")
+    vector_store = FAISS.load_local("faiss_index", embeddings)
+    qa_chain = RetrievalQA.from_chain_type(
+        llm,
+        retriever=vector_store.as_retriever(),
+        chain_type='stuff',
+        verbose=True,
+        chain_type_kwargs={
+            "prompt": PromptTemplate(input_variables=["history", "context", "question"], template=template),
+            "memory": ConversationBufferMemory(memory_key="history", input_key="question"),
+        }
+    )
+    return qa_chain
+
+# File and question handling
+def process_files(files, question):
+    text = ""
+    for file in files:
+        file_path = os.path.join("temp", file.name)
+        with open(file_path, "wb") as f:
+            f.write(file.read())
+        text += ocr_file(file_path) + "\n"
+
+    chunks = get_text_chunks(text)
+    vector_store = get_vector_store(chunks)
+
+    qa_chain = get_conversational_chain()
+    response = qa_chain({"query": question})
+    return response.get("result", "No result found.")
+
+# Gradio Interface
+def app(files, question):
+    return process_files(files, question)
+
+iface = gr.Interface(
+    fn=app,
+    inputs=[gr.File(file_types=[".pdf", ".jpg", ".jpeg", ".png", ".bmp"], label="Upload Files"), gr.Textbox(label="Ask a Question")],
+    outputs="text",
+    title="OCR and Document Query System",
+    description="Upload PDF or image files and ask questions based on their content."
+)
+
+if __name__ == "__main__":
+    iface.launch()