Create app.py

app.py

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+# from langgraph.graph import Graph
+# from langchain_groq import ChatGroq
+# llm = langchain_groq(model="llama3-70b-8192")
+# llm.invoke("hi how are you")
+import streamlit as st
+import os
+import base64
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+from langchain.chains import LLMMathChain, LLMChain
+from langchain.prompts import PromptTemplate
+from langchain_community.utilities import WikipediaAPIWrapper
+from langchain.agents.agent_types import AgentType
+from langchain.agents import Tool, initialize_agent
+from langchain_community.callbacks.streamlit import StreamlitCallbackHandler
+from groq import Groq
+
+load_dotenv()
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+if not groq_api_key:
+    st.error("Groq API Key not found in .env file")
+    st.stop()
+
+st.set_page_config(page_title="Medical Bot", page_icon="👨‍🔬")
+st.title("Medical Bot")
+llm_text = ChatGroq(model="gemma2-9b-it", groq_api_key=groq_api_key)
+llm_image = ChatGroq(model="llama-3.2-90b-vision-preview", groq_api_key=groq_api_key)
+
+wikipedia_wrapper = WikipediaAPIWrapper()
+wikipedia_tool = Tool(
+    name="Wikipedia",
+    func=wikipedia_wrapper.run,
+    description="A tool for searching the Internet to find various information on the topics mentioned."
+)
+math_chain = LLMMathChain.from_llm(llm=llm_text)
+calculator = Tool(
+    name="Calculator",
+    func=math_chain.run,
+    description="A tool for solving mathematical problems. Provide only the mathematical expressions."
+)
+
+prompt = """
+You are a mathematical problem-solving assistant tasked with helping users solve their questions. Arrive at the solution logically, providing a clear and step-by-step explanation. Present your response in a structured point-wise format for better understanding.
+Question: {question}
+Answer:
+"""
+
+prompt_template = PromptTemplate(
+    input_variables=["question"],
+    template=prompt
+)
+# Combine all the tools into a chain for text questions
+chain = LLMChain(llm=llm_text, prompt=prompt_template)
+
+reasoning_tool = Tool(
+    name="Reasoning Tool",
+    func=chain.run,
+    description="A tool for answering logic-based and reasoning questions."
+)
+
+# Initialize the agents for text questions
+assistant_agent_text = initialize_agent(
+    tools=[wikipedia_tool, calculator, reasoning_tool],
+    llm=llm_text,
+    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+    verbose=False,
+    handle_parsing_errors=True
+)
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [
+        {"role": "assistant", "content": "Welcome! I am your Assistant. How can I help you today?"}
+    ]
+
+for msg in st.session_state.messages:
+    if msg["role"] == "user" and "image" in msg:
+        st.chat_message(msg["role"]).write(msg['content'])
+        st.image(msg["image"], caption='Uploaded Image', use_column_width=True)
+    else:
+        st.chat_message(msg["role"]).write(msg['content'])
+
+st.sidebar.header("Navigation")
+if st.sidebar.button("Text Question"):
+    st.session_state["section"] = "text"
+if st.sidebar.button("Image Question"):
+    st.session_state["section"] = "image"
+
+if "section" not in st.session_state:
+    st.session_state["section"] = "text"
+
+def clean_response(response):
+    if "```" in response:
+        response = response.split("```")[1].strip()
+    return response
+
+if st.session_state["section"] == "text":
+    st.header("Text Question")
+    st.write("Please enter your question below, and I will provide a detailed description of the problem and suggest a solution for it.")
+    question = st.text_area("Your Question:")
+    if st.button("Get Answer"):
+        if question:
+            with st.spinner("Generating response..."):
+                st.session_state.messages.append({"role": "user", "content": question})
+                st.chat_message("user").write(question)
+
+                st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
+                try:
+                    response = assistant_agent_text.run(st.session_state.messages, callbacks=[st_cb])
+                    cleaned_response = clean_response(response)
+                    st.session_state.messages.append({'role': 'assistant', "content": cleaned_response})
+                    st.write('### Response:')
+                    st.success(cleaned_response)
+                except ValueError as e:
+                    st.error(f"An error occurred: {e}")
+        else:
+            st.warning("Please enter a question to get an answer.")
+
+elif st.session_state["section"] == "image":
+    st.header("Image Question")
+    st.write("Please enter your question below and upload the medical image. I will provide a detailed description of the problem and suggest a solution for it.")
+    question = st.text_area("Your Question:", "Example: What is the patient suffering from?")
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+    if st.button("Get Answer"):
+        if question and uploaded_file is not None:
+            with st.spinner("Generating response..."):
+                image_data = uploaded_file.read()
+                image_data_url = f"data:image/jpeg;base64,{base64.b64encode(image_data).decode()}"
+                st.session_state.messages.append({"role": "user", "content": question, "image": image_data})
+                st.chat_message("user").write(question)
+                st.image(image_data, caption='Uploaded Image', use_column_width=True)
+
+                client = Groq()
+
+                messages = [
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": question
+                            },
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": image_data_url
+                                }
+                            }
+                        ]
+                    }
+                ]
+                try:
+                    completion = client.chat.completions.create(
+                        model="llama-3.2-90b-vision-preview",
+                        messages=messages,
+                        temperature=1,
+                        max_tokens=1024,
+                        top_p=1,
+                        stream=False,
+                        stop=None,
+                    )
+                    response = completion.choices[0].message.content
+                    cleaned_response = clean_response(response)
+                    st.session_state.messages.append({'role': 'assistant', "content": cleaned_response})
+                    st.write('### Response:')
+                    st.success(cleaned_response)
+                except ValueError as e:
+                    st.error(f"An error occurred: {e}")
+        else:
+            st.warning("Please enter a question and upload an image to get an answer.")