Update app.py

app.py

@@ -1,147 +1,226 @@
+from dotenv import load_dotenv
+import os
+from docx import Document
+from llama_index.llms.together import TogetherLLM
+from llama_index.core.llms import ChatMessage, MessageRole
+from Bio import Entrez
+import ssl
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import streamlit as st
-from openai_client import (
-    get_code_review_response,
-    refactor_code,
-    code_feedback,
-    suggest_best_practices,
-    remove_code_errors,
-)
-
+from googleapiclient.discovery import build
+from typing import List, Optional
+
+load_dotenv()
+# 995d5f1a8de125c5b39bb48c2613e85f57d53c0e498a87d1ff33f0ec89a26ec7
+os.environ["TOGETHER_API"] = os.getenv("TOGETHER_API")
+os.environ["GOOGLE_SEARCH_API_KEY"] = os.getenv("GOOGLE_SEARCH_API_KEY")
+
+def search_pubmed(query: str) -> Optional[List[str]]:
+    """
+    Searches PubMed for a given query and returns a list of formatted results
+    (or None if no results are found).
+    """
+    Entrez.email = "harisellahi888@gmail.com"  # Replace with your email
+
+    try:
+        ssl._create_default_https_context = ssl._create_unverified_context
+
+        handle = Entrez.esearch(db="pubmed", term=query, retmax=3)
+        record = Entrez.read(handle)
+        id_list = record["IdList"]
+
+        if not id_list:
+            return None
+
+        handle = Entrez.efetch(db="pubmed", id=id_list, retmode="xml")
+        articles = Entrez.read(handle)
+
+        results = []
+        for article in articles['PubmedArticle']:
+            try:
+                medline_citation = article['MedlineCitation']
+                article_data = medline_citation['Article']
+                title = article_data['ArticleTitle']
+                abstract = article_data.get('Abstract', {}).get('AbstractText', [""])[0]
+
+                result = f"**Title:** {title}\n**Abstract:** {abstract}\n"
+                result += f"**Link:** https://pubmed.ncbi.nlm.nih.gov/{medline_citation['PMID']} \n\n"
+                results.append(result)
+            except KeyError as e:
+                print(f"Error parsing article: {article}, Error: {e}")
+
+        return results
+
+    except Exception as e:
+        print(f"Error accessing PubMed: {e}")
+        return None
+
+def chat_with_pubmed(article_text, article_link):
+    """
+    Engages in a chat-like interaction with a PubMed article using TogetherLLM.
+    """
+    try:
+        llm = TogetherLLM(model="QWEN/QWEN1.5-14B-CHAT", api_key=os.environ['TOGETHER_API'])
+        messages = [
+            ChatMessage(role=MessageRole.SYSTEM, content="You are a helpful AI assistant summarizing and answering questions about the following medical research article: " + article_link),
+            ChatMessage(role=MessageRole.USER, content=article_text)
+        ]
+        response = llm.chat(messages)
+        return str(response) if response else "I'm sorry, I couldn't generate a summary for this article."
+    except Exception as e:
+        print(f"Error in chat_with_pubmed: {e}")
+        return "An error occurred while generating a summary."
+
+def search_web(query: str, num_results: int = 3) -> Optional[List[str]]:
+    """
+    Searches the web using the Google Search API and returns a list of formatted results
+    (or None if no results are found).
+    """
+    try:
+        service = build("customsearch", "v1", developerKey=os.environ["GOOGLE_SEARCH_API_KEY"])
+
+        # Execute the search request
+        res = service.cse().list(q=query, cx="e31a5857f45ef4d2a", num=num_results).execute()
+
+        if "items" not in res:
+            return None
+
+        results = []
+        for item in res["items"]:
+            title = item["title"]
+            link = item["link"]
+            snippet = item["snippet"]
+            result = f"**Title:** {title}\n**Link:** {link} \n**Snippet:** {snippet}\n\n"
+            results.append(result)
+
+        return results
+
+    except Exception as e:
+        print(f"Error performing web search: {e}")
+        return None
+
+
+from together import Together
+def medmind_chatbot(user_input, chat_history=None):
+    """
+    Processes user input, interacts with various resources, and generates a response.
+    Handles potential errors, maintains chat history,
+    """
+    if chat_history is None:
+        chat_history = []
+
+    response_parts = []  # Collect responses from different sources
+    final_response = "";
+
+    try:
+        # PubMed Search and Chat
+        pubmed_results = search_pubmed(user_input)
+        if pubmed_results:
+            for article_text in pubmed_results:
+                title, abstract, link = article_text.split("\n")[:3]
+                # print(article_text)
+                response_parts.append(f"{title}\n{abstract}\n{link}\n")
+        else:
+            response_parts.append("No relevant PubMed articles found.")
 
-def main():
-    st.title("CodeMentor - (AI-Enhanced Code Collaboration Tool)")
-    st.subheader("Collaborate, Refactor, and Optimize with AI.")
-    st.write(
-        "A smart tool for distributed teams to automate code reviews, refactor efficiently, and get real-time AI-driven feedback."
-    )
-
-    # Instructions
-    st.write(
-        "Upload a file or paste your code below to get an AI-generated code review."
-    )
-
-    # Input Methods: File Upload or Text Area
-    uploaded_file = st.file_uploader(
-        "Upload a code file (Max 500 lines)", type=["py", "js", "txt"]
-    )
-    code_input = st.text_area("Or paste your code here (Max 1000 words)", height=300)
-
-    # Limit input size for code
-    if uploaded_file:
-        code = uploaded_file.read().decode("utf-8")
-        if len(code.splitlines()) > 500:
-            st.error(
-                "File is too large! Please upload a file with a maximum of 500 lines."
-            )
-            code = None  # Reset code if it's too large
+        # Web Search
+        web_results = search_web(user_input)
+        if web_results:
+            response_parts.append("\n\n**Web Search Results:**")
+            response_parts.extend(web_results)
         else:
-            st.success(f"File uploaded: {uploaded_file.name}")
-    elif code_input:
-        code = code_input
-        if len(code.split()) > 1000:
-            st.error("Code exceeds 1000 words! Please shorten your code.")
-            code = None  # Reset code if it's too large
-    else:
-        code = None
-
-    # Button to trigger code review
-    if st.button("Get Code Review") and code:
-        with st.spinner("Processing..."):
-            # Call the OpenAI API to get code review
-            review = get_code_review_response(code)
-            st.subheader("Code Review Results:")
-            st.write(review)
-
-            # Provide download option
-            st.download_button(
-                label="Download Code Review",
-                data=review,
-                file_name="code_review.txt",
-                mime="text/plain",
-            )
-            st.success("You can download the code review as code_review.txt")
-
-        # Button to trigger code refactoring
-    if st.button("Refactor Code") and code:
-        with st.spinner("Refactoring your code..."):
-            refactored_code = refactor_code(code)
-            st.subheader("Refactored Code:")
-            st.write(refactored_code)
-
-            # Provide download option for refactored code
-            st.download_button(
-                label="Download Refactored Code",
-                data=refactored_code,
-                file_name="refactored_code.txt",
-                mime="text/plain",
-            )
-            st.success("You can download the refactored code as refactored_code.txt")
-
-    # Button to trigger code feedback
-    if st.button("Get Code Feedback") and code:
-        with st.spinner("Getting feedback on your code..."):
-            feedback = code_feedback(code)
-            st.subheader("Code Feedback:")
-            st.write(feedback)
-
-            # Ensure feedback is a string for download
-            feedback_text = feedback if isinstance(feedback, str) else str(feedback)
-
-            # Provide download option for code feedback
-            st.download_button(
-                label="Download Code Feedback",
-                data=feedback_text,  # Use the extracted string here
-                file_name="code_feedback.txt",
-                mime="text/plain",
-            )
-            st.success("You can download the code feedback as code_feedback.txt")
-
-    # Add button to suggest best practices
-    if st.button("Suggest Best Practices") and code:
-        with st.spinner("Getting best practices..."):
-            best_practices = suggest_best_practices(code)
-            st.subheader("Best Practices Suggestions:")
-            st.write(best_practices)
-
-            # Provide download option for best practices suggestions
-            best_practices_text = (
-                best_practices
-                if isinstance(best_practices, str)
-                else str(best_practices)
-            )
-            st.download_button(
-                label="Download Best Practices Suggestions",
-                data=best_practices_text,
-                file_name="best_practices.txt",
-                mime="text/plain",
-            )
-            st.success(
-                "You can download the best practices suggestions as best_practices.txt"
-            )
-
-    # Button to trigger error removal
-    if st.button("Remove Code Errors") and code:
-        with st.spinner("Removing errors from your code..."):
-            error_removal_suggestions = remove_code_errors(code)
-            st.subheader("Error Removal Suggestions:")
-            st.write(error_removal_suggestions)
-
-            # Provide download option for error removal suggestions
-            error_removal_text = (
-                error_removal_suggestions
-                if isinstance(error_removal_suggestions, str)
-                else str(error_removal_suggestions)
-            )
-            st.download_button(
-                label="Download Error Removal Suggestions",
-                data=error_removal_text,
-                file_name="error_removal_suggestions.txt",
-                mime="text/plain",
-            )
-            st.success(
-                "You can download the error removal suggestions as error_removal_suggestions.txt"
-            )
+            response_parts.append("No relevant web search results found.")
+
+        # Combine response parts into a single string
+        response_text = "\n\n".join(response_parts)
+
+        prompt = f"""You are a Health Assistant AI designed to provide detailed responses to health-related questions.
+        Based on the information retrieved from the PubMed and Web Search below, answer the user's query appropriately.
+
+        - If the user's query is health-related, provide a detailed and helpful response based on the retrieved information. Or if there is
+        some previous conversation then answer the health by seeing the previous conversation also.
+        - If the query is a general greeting (e.g., 'Hello', 'Hi'), respond as a friendly assistant.
+        - If the query is irrelevant or unrelated to health, respond with: 'I am a health assistant. Please ask only health-related questions.'
+        - Don't mention in response that where you reterived the information.
+        Previous Conversation:
+        {chat_history}
+
+        User's Query: {user_input}
+
+        Information retrieved from PubMed and Web Search:
+        {response_text}
+
+        Your response:"""
+
+        client = Together(api_key=os.environ.get('TOGETHER_API'))
 
+        response = client.chat.completions.create(
+            model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
+            messages=[{"role": "user", "content": prompt}],
+        )
+
+        final_response = response.choices[0].message.content
+
+    except Exception as e:
+        print(f"Error in chatbot: {e}")
+        response_text = "An error occurred. Please try again later."
+
+    chat_history.append((user_input, final_response))
+    return final_response, chat_history
+
+medmind_chatbot("What are the symptoms of COVID-19?")
+
+import gradio as gr
+
+def show_info_popup():
+    info = """
+    **HealthHive is an AI-powered chatbot designed to assist with medical information.**
+    ...
+    """
+    return info
+
+
+def main():
+    # Initialize Gradio Interface
+    with gr.Blocks() as demo:
+        gr.Markdown("# HealthHive Chatbot")
+        gr.Markdown("Ask your medical questions and get reliable information!")
+
+        # Example Questions (Sidebar)
+        gr.Markdown("### Example Questions")
+        example_questions = [
+            "What are the symptoms of COVID-19?",
+            "How can I manage my diabetes?",
+            "What are the potential side effects of ibuprofen?",
+            "What lifestyle changes can help prevent heart disease?"
+        ]
+        for question in example_questions:
+            gr.Markdown(f"- {question}")
+
+        # Chat History and User Input
+        with gr.Row():
+            user_input = gr.Textbox(label="You:", placeholder="Type your medical question here...", lines=2)
+            chat_history = gr.State([])
+
+        # Output Container
+        with gr.Row():
+            response = gr.Textbox(label="HealthHive:", placeholder="Response will appear here...", interactive=False, lines=10)
+        def clear_chat():
+            return "", ""
+
+        # Define function to update chat history and response
+        def on_submit(user_input, chat_history):
+            result, updated_history = medmind_chatbot(user_input, chat_history)
+            info = show_info_popup()
+            return result, updated_history, info
+
+        # Link the submit button to the chatbot function
+        gr.Button("Submit").click(on_submit, inputs=[user_input, chat_history], outputs=[response, chat_history])
+        # gr.Button("Start New Chat").click(lambda: [], outputs=[chat_history])
+        gr.Button("Start New Chat").click(clear_chat, outputs=[user_input, response])
+
+
+    demo.launch()
 
 if __name__ == "__main__":
     main()