Create app.py

app.py

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+import faiss
+import pickle
+import gradio as gr
+from PIL import Image
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import LLMChain
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+
+# Load OpenAI API key (Replace with Hugging Face Secrets later)
+openai_api_key = "your_openai_api_key"
+
+# Load FAISS vectorstore
+load_path = "faiss_index"
+vectorstore = FAISS.load_local(
+    load_path, OpenAIEmbeddings(openai_api_key=openai_api_key), allow_dangerous_deserialization=True
+)
+
+# Define prompt template
+prompt_template = """
+You are an expert assistant. Answer based on the given context (text, tables, images).
+Context: {context}
+
+Question: {question}
+
+If you cannot find relevant data, reply with: "Sorry, I don't have enough information."
+Answer:
+"""
+
+qa_chain = LLMChain(
+    llm=ChatOpenAI(model="gpt-4", openai_api_key=openai_api_key, max_tokens=1024),
+    prompt=PromptTemplate.from_template(prompt_template)
+)
+
+# Function to handle queries
+def answer(query):
+    relevant_docs = vectorstore.similarity_search(query)
+    context = ""
+    relevant_images = []
+
+    for doc in relevant_docs:
+        if doc.metadata['type'] == 'text':
+            context += '[text] ' + doc.metadata['original_content'] + "\n"
+        elif doc.metadata['type'] == 'table':
+            context += '[table] ' + doc.metadata['original_content'] + "\n"
+        elif doc.metadata['type'] == 'image':
+            context += '[image] ' + doc.page_content + "\n"
+            relevant_images.append(doc.metadata['original_content'])  # Store image file paths
+
+    response = qa_chain.run({'context': context, 'question': query})
+    
+    # Load images (if available)
+    images = []
+    for img_path in relevant_images:
+        try:
+            images.append(Image.open(img_path))
+        except:
+            pass  # Ignore errors
+
+    return response, images
+
+# Gradio UI
+def chatbot_interface(question):
+    response, images = answer(question)
+    return response, images if images else None
+
+iface = gr.Interface(
+    fn=chatbot_interface,
+    inputs="text",
+    outputs=["text", "gallery"],
+    title="Text & Image Retrieval Chatbot",
+    description="Ask a question and get an answer with relevant images if available.",
+)
+
+iface.launch()