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ochatv.py

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+import ollama
+import streamlit as st
+import httpx
+import io
+import pandas as pd
+from PyPDF2 import PdfReader
+from PIL import Image
+
+st.set_page_config(layout="wide")
+st.title("LLM Chatbot")
+
+# Initialize history
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+# Initialize models and system prompt
+if "model" not in st.session_state:
+    st.session_state["model"] = ""
+if "system_prompt" not in st.session_state:
+    st.session_state["system_prompt"] = "analyze file and summarize in bullet points"
+if "new_message" not in st.session_state:
+    st.session_state["new_message"] = False
+if "user_query" not in st.session_state:
+    st.session_state["user_query"] = ""
+if "uploaded_file_content" not in st.session_state:
+    st.session_state["uploaded_file_content"] = ""
+if "uploaded_files" not in st.session_state:
+    st.session_state["uploaded_files"] = []
+
+st.sidebar.write("local vision, rag")
+# Sidebar menu
+with st.sidebar:
+    try:
+        models = [model["name"] for model in ollama.list()["models"]]
+        st.session_state["model"] = st.selectbox("Choose your model", models)
+    except httpx.ConnectError:
+        st.error("Unable to connect to the local Ollama instance. Please ensure it is running.")
+    st.session_state["system_prompt"] = st.text_area("System Prompt", value="analyze file and summarize in bullet points")
+    
+    if st.button("Reset"):
+        st.session_state["messages"] = []
+        st.session_state["new_message"] = False
+        st.session_state["user_query"] = ""
+        st.session_state["uploaded_file_content"] = ""
+        st.session_state["uploaded_files"] = []
+        st.rerun()
+
+    uploaded_files = st.file_uploader("Upload images (PNG, JPG) or text files (PDF, CSV)", type=["png", "jpg", "pdf", "csv"], accept_multiple_files=True)
+    if uploaded_files:
+        st.session_state["uploaded_files"] = uploaded_files
+
+    # Add a radio button to decide if the uploaded file should be part of the query
+    include_files_in_query = st.radio("Include uploaded files in query?", ("Yes", "No"))
+
+def process_uploaded_files():
+    file_contents = []
+    image_files = []
+    for uploaded_file in st.session_state["uploaded_files"]:
+        file_type = uploaded_file.type
+        if "image" in file_type:
+            image_files.append(uploaded_file)
+            file_contents.append(f"Image file: {uploaded_file.name}")
+        elif "pdf" in file_type:
+            pdf_reader = PdfReader(io.BytesIO(uploaded_file.read()))
+            pdf_text = ""
+            for page in pdf_reader.pages:
+                pdf_text += page.extract_text()
+            file_contents.append(f"PDF file: {uploaded_file.name}\nContent:\n{pdf_text}")
+        elif "csv" in file_type:
+            csv_data = pd.read_csv(uploaded_file)
+            csv_text = csv_data.to_string()
+            file_contents.append(f"CSV file: {uploaded_file.name}\nContent:\n{csv_text}")
+    return "\n".join(file_contents), image_files
+
+def model_res_generator(messages):
+    try:
+        stream = ollama.chat(
+            model=st.session_state["model"],
+            messages=messages,
+            stream=True,
+        )
+        for chunk in stream:
+            yield chunk["message"]["content"]
+    except (httpx.ConnectError, ollama.ResponseError) as e:
+        st.error(f"An error occurred: {e}")
+        return
+
+# Display chat messages from history on app rerun
+for message in st.session_state["messages"]:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Add some space before the query box
+st.write("")
+st.write("")
+
+if st.session_state["new_message"]:
+    st.session_state["user_query"] = ""
+    st.session_state["new_message"] = False
+    st.rerun()
+
+if prompt := st.text_input("What is your query?", key="user_query"):
+    # Process and include uploaded file content in the query if the user chose to include it
+    if include_files_in_query == "Yes":
+        st.session_state["uploaded_file_content"], image_files = process_uploaded_files()
+        augmented_prompt = prompt + "\n\n" + st.session_state["uploaded_file_content"]
+    else:
+        augmented_prompt = prompt
+
+    # Prepare messages with possible images
+    messages = [{"role": "user", "content": augmented_prompt}]
+    if st.session_state["system_prompt"]:
+        messages.insert(0, {"role": "system", "content": st.session_state["system_prompt"]})
+    if include_files_in_query == "Yes" and image_files:
+        with io.BytesIO() as file_obj:
+            file_obj.write(image_files[0].read())
+            file_obj.seek(0)
+            messages[0]["images"] = [file_obj.read()]
+    
+    # Add latest message to history in format {role, content}
+    st.session_state["messages"].append({"role": "user", "content": prompt})
+
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            # Generate response based on the augmented prompt
+            try:
+                message = "".join(model_res_generator(messages))
+                st.session_state["messages"].append({"role": "assistant", "content": message})
+            except Exception as e:
+                st.error(f"Failed to generate response: {e}")
+
+    # Set flag for new message
+    st.session_state["new_message"] = True
+    st.rerun()
+
+st.sidebar.info("built by dw")

requirements.txt

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+streamlit
+httpx
+pandas
+PyPDF2
+Pillow
+ollama