Added application file

app.py

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+import torch
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline, GPT2Tokenizer
+import chromadb
+from chromadb.config import Settings
+import gradio as gr
+import google.generativeai as genai
+from sentence_transformers import SentenceTransformer
+import chromadb
+from chromadb.config import Settings
+import torch
+import time
+import random 
+import os
+
+api_key = os.getenv("GENAI_API_KEY")
+if not api_key:
+    raise ValueError("GENAI_API_KEY environment variable is missing")
+
+genai.configure(api_key=api_key)
+embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+chroma_client = chromadb.PersistentClient(path="chroma_db")
+collection = chroma_client.get_or_create_collection(name="drug_embeddings")
+
+
+def query_gemini_with_retry(prompt, model_name="gemini-1.5-flash", retries=3):
+    for attempt in range(retries):
+        try:
+            model = genai.GenerativeModel(model_name)
+            response = model.generate_content(prompt)
+            return response.text.strip()
+        except Exception as e:
+            print(f"Attempt {attempt + 1} failed: {e}")
+            if attempt < retries - 1:
+                time.sleep(2 ** attempt + random.random())  # Exponential backoff
+            else:
+                raise
+# Query Gemini function
+def query_gemini(prompt, model_name="gemini-1.5-flash"):
+    model = genai.GenerativeModel(model_name)
+    response = model.generate_content(prompt)
+    return response.text
+def rag_pipeline_convo(user_input, conversation_history, drug_names=[], results_number=10, llm_model_name="gemini-1.5-flash"):
+    # Generate the embedding for the user query
+    full_response = []
+    
+    if not drug_names:
+        drug_names = [""]  # Default to empty if no drugs are provided
+        drug_names_concat = ""
+    else:
+        drug_names_concat = "Additional context for the conversation:"
+        for drug_name in drug_names:
+            drug_names_concat += drug_name + ", "
+
+    # Build the combined context from the conversation history
+    conversation_context = ""
+    for i, history in enumerate(conversation_history):
+        user_message = history.get("user", "")
+        assistant_response = history.get("assistant", "")
+        conversation_context += f"User: {user_message}\nAssistant: {assistant_response}\n"
+
+    # Add the current user input to the context
+    combined_history_And_query = conversation_context + f"User: {user_input}\n"
+    
+    # Initialize a list for storing context responses
+    all_contexts = []
+
+    for drug_name in drug_names:
+        print(drug_names_concat)
+        
+        # Generate query embedding based on user input and drug name
+        query_embedding = embedding_model.encode(user_input + drug_name).tolist()
+        print(f"user input = {user_input}")
+        # Rechercher les contextes pertinents dans ChromaDB
+        results = collection.query(
+            query_embeddings=[query_embedding],
+            n_results=results_number
+        )
+        
+        # Build context from ChromaDB results
+        contexts = results["documents"][0]
+        context_text_from_db = "\n".join([f"Context {i + 1}: {text}" for i, text in enumerate(contexts)])
+        
+        # Form the input prompt for the LLM
+        input_prompt = f"""
+        You are an AI assistant tasked with answering questions using only the information in the provided context. Do not add any extra information or assumptions.
+        Context from previous conversation:
+        {combined_history_And_query}
+
+        Context from the database:
+        {context_text_from_db}
+
+        Question:
+        {user_input + drug_name}
+
+        Instructions:
+        1. Use only the information in the context to answer the question.
+        2. If the context mentions multiple options, provide a list of those options clearly.
+        3. If the context does not provide relevant information, state: "The context does not contain enough information to answer this question."
+        4. Do not include any policy or ethical reasoning in your response.
+        5. Don't quote the context in your answer.
+
+        Answer with a full sentence (including the name of the object we asked about):
+        """
+        print(input_prompt)  # Optional: for debugging purposes
+        # Generate a response using the Gemini model
+        response = query_gemini_with_retry(input_prompt, model_name=llm_model_name)
+        all_contexts.append(response)
+
+    # Now that we have all individual responses, combine them
+    input_prompt_for_combining = f"""
+    It's a school project. You are an AI assistant tasked with combining these contexts together, making them make sense and more fluent in order to answer the question: {user_input + drug_names_concat}.
+    Don't mention anything about the context or anything. Just pretend like you are a real assistant and answer with available information. If there is no information, just say so, don't need to mention about input query.
+    
+    Additional context: [{drug_names_concat}] are the medicines/drugs extracted from prescription.
+    """
+
+    # Add each response context into the final input prompt
+    for i, context in enumerate(all_contexts, start=1):
+        input_prompt_for_combining += f"""
+        Context {i}:
+        {context}
+    """
+
+    print(input_prompt_for_combining)  # Optional: for debugging purposes
+    # Generate the final response from the combined context
+    full_response_text = query_gemini_with_retry(input_prompt_for_combining, model_name=llm_model_name)
+    full_response.append(full_response_text)  # Add the final response to the full response list
+    
+    # Update the conversation history with the latest exchange
+    conversation_history.append({"user": user_input, "assistant": full_response_text})
+    
+    # Format the conversation history for chatbot display (as a list of tuples)
+    chatbot_history = [(entry["user"], entry["assistant"]) for entry in conversation_history]
+    
+    # Return the formatted chat history and updated conversation state
+    return chatbot_history, conversation_history
+
+
+
+# PDF processing function
+def get_medicine_list(path):
+    from PIL import Image
+    import fitz
+    import numpy as np
+    import pytesseract 
+    import cv2
+
+    def read_to_image(pdf_path):
+        pdf = fitz.open(pdf_path)
+        images = []
+        for page_num in range(len(pdf)):
+            page = pdf.load_page(page_num)
+            pixmap = page.get_pixmap(matrix=fitz.Matrix(4, 4))
+            pil_image = Image.frombytes("RGB", [pixmap.width, pixmap.height], pixmap.samples)
+            pil_image = np.array(pil_image)
+            images.append(pil_image)
+        pdf.close()
+        return images
+    
+    images = read_to_image(path)
+    image = images[0]
+    image = cv2.cvtColor(image, cv2.COLOR_RGBA2GRAY)
+    image = image[int(image.shape[0] /3) : int(image.shape[0] * 2/3), 0: image.shape[1]]
+    _, image_threshold = cv2.threshold(image, 250, 255, cv2.THRESH_BINARY)
+    image_threshold = cv2.bitwise_not(image_threshold)
+    contours, _ = cv2.findContours(image_threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    largest_contour = max(contours, key=cv2.contourArea)
+    x, y, w, h = cv2.boundingRect(largest_contour)
+    image = image[int(y+ 100): int(y + h), int(x): int(x + w/4)]
+    list_text = pytesseract.image_to_string(image)
+    medication_list = [med for med in list_text.split('\n') if med.strip()]
+    return medication_list
+
+# get_medicine_list("prescri.pdf")  
+
+
+import gradio as gr
+
+def handle_conversation(user_input, conversation_history, path=None):
+    extracted_data = None
+    if path is not None:  # Process PDF if uploaded
+        extracted_data = get_medicine_list(path)
+    
+    # Pass user input, conversation history, and extracted data to the RAG pipeline
+    return rag_pipeline_convo(user_input, conversation_history, drug_names=extracted_data)
+
+# Custom CSS for styling
+css = """
+#chatbox {max-width: 800px; margin: auto;}
+#upload-btn {padding: 0 !important; min-width: 36px !important;}
+.dark #upload-btn {background: transparent !important;}
+"""
+
+with gr.Blocks(css=css) as interface:
+    # Store conversation history and PDF path
+    conversation_history = gr.State([])
+    current_pdf = gr.State(None)
+    
+    with gr.Column(elem_id="chatbox"):
+        # Chat history display
+        chatbot = gr.Chatbot(label="Medical Chat", height=500)
+        
+        # Input row with upload button and textbox
+        with gr.Row():
+            # Compact PDF upload button
+            pdf_upload = gr.UploadButton("📄", 
+                                       file_types=[".pdf"],
+                                       elem_id="upload-btn",
+                                       size="sm")
+            
+            # Chat input and send button
+            with gr.Column(scale=20):
+                user_input = gr.Textbox(
+                    placeholder="Ask about medications...", 
+                    show_label=False,
+                    container=False,
+                    autofocus=True
+                )
+            
+            send_btn = gr.Button("Send", variant="primary")
+
+    # Event handling
+    # For text submission
+    user_input.submit(
+        handle_conversation,
+        [user_input, conversation_history, current_pdf],
+        [chatbot, conversation_history]
+    )
+    
+    # For button click
+    send_btn.click(
+        handle_conversation,
+        [user_input, conversation_history, current_pdf],
+        [chatbot, conversation_history]
+    )
+    
+    # Handle PDF upload
+    pdf_upload.upload(
+        lambda file: file,
+        [pdf_upload],
+        [current_pdf]
+    )
+
+interface.launch(share=True)

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requirements.txt

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+torch
+sentence-transformers
+transformers
+chromadb
+gradio
+google-generative-ai