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app.py

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+import os
+from crewai import Agent, Task, Crew
+from langchain_groq import ChatGroq
+import streamlit as st
+from PIL import Image
+import base64
+from io import BytesIO
+import pandas as pd  # Import pandas for handling data in tabular format
+
+# Initialize the LLM for the Doctor Assistant
+llm = ChatGroq(
+    groq_api_key="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",
+    model_name="llama3-70b-8192",  # Replace with the actual model name
+)
+
+# Define the Doctor Assistant with a diagnostic goal
+doctor_assistant = Agent(
+    role='Doctor Assistant',
+    goal='Collect detailed health information dynamically through a series of questions based on user responses.',
+    backstory=(
+        "You are a virtual doctor assistant who asks diagnostic questions based on user responses. "
+        "Your role is to gather health information before the user’s doctor consultation, adapting your questions as needed."
+    ),
+    verbose=True,
+    llm=llm,
+)
+
+# Function to process user response and generate the next question
+def get_next_question(response):
+    # Define the task for generating the next question based on user response
+    task_description = f"Generate the next diagnostic question based on the user's response: '{response}'"
+    
+    # Set up the task for the assistant to generate a follow-up question
+    follow_up_task = Task(
+        description=task_description,
+        agent=doctor_assistant,
+        human_input=False,
+        expected_output="A contextually relevant follow-up question based on user response"  # Placeholder for expected output
+    )
+
+    # Instantiate the crew and execute the task to get the next question
+    crew = Crew(
+        agents=[doctor_assistant],
+        tasks=[follow_up_task],
+        verbose=2,
+    )
+    result = crew.kickoff()
+    
+    return result
+
+# Load the image from the specified path
+image_path = "Einstein\image\image-removebg-preview (2).png"  # Adjust path if necessary
+image = Image.open(image_path)
+image = image.resize((300, 300))
+
+# Convert image to base64 and display it
+buffered = BytesIO()
+image.save(buffered, format="PNG")
+img_str = base64.b64encode(buffered.getvalue()).decode()
+st.markdown("<h1 style='text-align: center;'>Doctor Assistant Chatbot</h1>", unsafe_allow_html=True)
+st.markdown(f"<div style='text-align: center;'><img src='data:image/png;base64,{img_str}' width='300' height='300'/></div>", unsafe_allow_html=True)
+
+# Initialize session states for storing conversation history and user details
+if "conversation" not in st.session_state:
+    st.session_state.conversation = []
+if "user_details" not in st.session_state:
+    st.session_state.user_details = {}
+
+# Display the conversation history
+for turn in st.session_state.conversation:
+    role, content = turn
+    with st.chat_message(role):
+        st.markdown(content)
+
+import pandas as pd
+
+# Function to generate a concise report
+def generate_report():
+    # Prepare patient detail data with key information only
+    patient_details = {
+        "Patient Name": st.session_state.user_details.get("name", 'N/A'),
+        "Patient Age": st.session_state.user_details.get("age", 'N/A'),
+        "Patient Gender": st.session_state.user_details.get("gender", 'N/A'),
+        "Patient Phone Number": st.session_state.user_details.get("phone_number", 'N/A'),  # Assuming you have the phone number
+    }
+
+    # Create a DataFrame for patient details
+    details_df = pd.DataFrame.from_dict(patient_details, orient='index', columns=['Value'])
+    
+    # Prepare keywords and summary of symptoms
+    symptoms_summary = []
+    for turn in st.session_state.conversation:
+        role, content = turn
+        if role == "user":
+            # Extract main keywords from user responses
+            symptoms_summary.append(content)
+
+    # Select only unique symptoms and key information
+    unique_symptoms = list(set(symptoms_summary))
+
+    # Prepare symptom keywords for display
+    symptoms_df = pd.DataFrame(unique_symptoms, columns=["Main Symptoms"])
+
+    # Display the report
+    report = f"""
+    ## Patient Report
+    """
+
+    return details_df, symptoms_df
+
+
+
+
+# Initial input for user details
+if not st.session_state.user_details:
+    name = st.text_input("Please enter your name:")
+    age = st.text_input("Please enter your age:")
+    gender = st.selectbox("Please select your gender:", ["Male", "Female", "Other"])
+
+    # Store user details in session state
+    if st.button("Submit Details"):
+        if name and age and gender:
+            st.session_state.user_details = {"name": name, "age": age, "gender": gender}
+            initial_question = "Thank you! Now, could you tell me what symptoms you're experiencing?"
+            st.session_state.conversation.append(("assistant", initial_question))
+            with st.chat_message("assistant"):
+                st.markdown(initial_question)
+        else:
+            st.warning("Please fill out all fields.")
+
+# Check for user's response and generate the next question
+if user_response := st.chat_input("Your response:"):
+    # Check for the end conversation keyword
+    if user_response.lower() in ["finish", "done", "end"]:
+        st.session_state.conversation.append(("user", user_response))
+        with st.chat_message("user"):
+            st.markdown(user_response)
+
+        # Generate and display the final report
+        report_df = generate_report()
+        st.table(report_df)  # Display the report in table format
+        st.markdown("Thank you for your responses! The consultation has ended. Take care!", unsafe_allow_html=True)
+        st.stop()  # Stop the app from proceeding further
+
+    # Append the user's response to the conversation
+    st.session_state.conversation.append(("user", user_response))
+
+    # Display the user's response immediately
+    with st.chat_message("user"):
+        st.markdown(user_response)
+
+    # Generate the next question based on the user's response
+    with st.spinner("Processing..."):
+        next_question = get_next_question(user_response)
+
+    # Append the assistant's next question to the conversation
+    st.session_state.conversation.append(("assistant", next_question))
+
+    # Display the assistant's response
+    with st.chat_message("assistant"):
+        st.markdown(next_question)
+if st.button("Generate Report"):
+    details_df, symptoms_df = generate_report()
+
+    # Display patient details
+    st.markdown("### Patient Details")
+    st.table(details_df)
+
+    st.markdown("### Main Symptoms")
+    st.table(symptoms_df)
+
+    # Add an end line
+    st.markdown("---")
+

requirements.txt

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+crewai
+langchain_groq
+streamlit
+Pillow
+base64
+io
+pandas