fixed chatbot LLM

.gitignore

@@ -1,3 +1,9 @@
 .streamlit/
 app.ipynb
-.ipynb_checkpoints/
+.ipynb_checkpoints/
+app_local.py
+.vscode/
+ecommerce_duckdb.ipynb
+openrouter_testings.ipynb
+appBackup.py
+prompt_instructions.txt

app.py

@@ -2,22 +2,277 @@ import streamlit as st
 from tempfile import NamedTemporaryFile
 
 import pprint
+import re
 import os
-from dotenv import load_dotenv, find_dotenv
-import os
-from langchain_openai import ChatOpenAI
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.document_loaders.csv_loader import CSVLoader
 from langchain.document_loaders import WebBaseLoader
+import duckdb
 
 import pandas as pd
 import numpy as np
 import pprint
+import requests
+import json
 
 defaultGoogleURL = "https://www.google.com/search?q=google+earnings"
+OPEN_ROUTER_MODEL = "meta-llama/llama-3.3-70b-instruct:free"
+DEFAULT_ECOMMERCE_CSV = "EcommerceDataset.csv"
+
+# Input for OpenRouter API Key
 OPEN_ROUTER_KEY = st.secrets["OPEN_ROUTER_KEY"]
-OPEN_ROUTER_MODEL = "meta-llama/llama-3.1-70b-instruct:free"
-isPswdValid = False # Set to True to temporarily disable password checking
+
+if not OPEN_ROUTER_KEY:
+    st.warning("Please enter your OpenRouter API Key to proceed.")
+    st.stop()
+
+def call_openrouter(content: str) -> str:
+    """Send a chat request to OpenRouter and return a safe string response."""
+    try:
+        response = requests.post(
+            url="https://openrouter.ai/api/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {OPEN_ROUTER_KEY}",
+                "Content-Type": "application/json"
+            },
+            data=json.dumps({
+                "model": OPEN_ROUTER_MODEL,
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": content
+                    }
+                ]
+            }),
+            timeout=60,
+        )
+    except Exception as exc:
+        return f"Request error: {exc}"
+
+    if not response.ok:
+        # Return status code plus body so the user knows what went wrong.
+        return f"Request failed ({response.status_code}): {response.text}"
+
+    try:
+        data = response.json()
+    except Exception as exc:
+        return f"Invalid JSON response: {exc} | body: {response.text}"
+
+    try:
+        return data["choices"][0]["message"]["content"]
+    except Exception:
+        return f"Unexpected response format: {data}"
+
+def call_openrouter_messages(messages) -> str:
+    """Generic OpenRouter call that accepts a messages list."""
+    try:
+        response = requests.post(
+            url="https://openrouter.ai/api/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {OPEN_ROUTER_KEY}",
+                "Content-Type": "application/json"
+            },
+            data=json.dumps({
+                "model": OPEN_ROUTER_MODEL,
+                "messages": messages
+            }),
+            timeout=60,
+        )
+    except Exception as exc:
+        return f"Request error: {exc}"
+
+    if not response.ok:
+        return f"Request failed ({response.status_code}): {response.text}"
+
+    try:
+        data = response.json()
+    except Exception as exc:
+        return f"Invalid JSON response: {exc} | body: {response.text}"
+
+    try:
+        return data["choices"][0]["message"]["content"]
+    except Exception:
+        return f"Unexpected response format: {data}"
+
+def ask_llm(question: str, schema_text: str) -> str:
+    """Ask the model to generate a DuckDB SQL query for the given question and schema."""
+    messages = [
+        {
+            "role": "system",
+            "content": f"""
+You are a data analyst.
+
+You MUST use ONLY this table:
+- Table name: data
+
+Schema:
+{schema_text}
+
+Rules:
+- Use ONLY table name "data"
+- Return ONE valid DuckDB SQL query
+- Do NOT explain
+- Do NOT use markdown
+"""
+        },
+        {"role": "user", "content": question},
+    ]
+    return call_openrouter_messages(messages)
+
+def explain_result(question: str, df: pd.DataFrame) -> str:
+    """Ask the model to explain the result set in plain language."""
+    try:
+        result_text = df.to_string(index=False)
+    except Exception:
+        result_text = str(df)
+
+    messages = [
+        {
+            "role": "system",
+            "content": """
+You are a data analyst.
+
+Given a user's question and a query result,
+produce a concise, human-like explanation.
+
+Rules:
+- Do NOT mention SQL, databases, or tables
+- Do NOT explain how the data was computed
+- Be clear and business-friendly
+"""
+        },
+        {
+            "role": "user",
+            "content": f"""
+Question:
+{question}
+
+Query Result:
+{result_text}
+"""
+        },
+    ]
+    return call_openrouter_messages(messages)
+
+def sanitize_dataframe(df: pd.DataFrame):
+    """Return a copy of df with column names sanitized for SQL identifiers."""
+    if df is None or not isinstance(df, pd.DataFrame):
+        return df, {}
+    rename_map = {}
+    used = set()
+    for col in df.columns:
+        new_col = re.sub(r"[^0-9a-zA-Z_]+", "_", str(col))
+        new_col = new_col.strip("_")
+        if re.match(r"^[0-9]", new_col):
+            new_col = f"col_{new_col}"
+        if not new_col:
+            new_col = "col"
+        base = new_col
+        idx = 1
+        while new_col in used:
+            new_col = f"{base}_{idx}"
+            idx += 1
+        used.add(new_col)
+        rename_map[col] = new_col
+    return df.rename(columns=rename_map), rename_map
+
+def run_duckdb_qa(question: str, dataframe: pd.DataFrame) -> str:
+    """Generate SQL via LLM, run it on DuckDB, and explain the result."""
+    if not question.strip():
+        return "Please enter a question."
+    if dataframe is None or not isinstance(dataframe, pd.DataFrame):
+        return "No CSV data loaded."
+    clean_df, rename_map = sanitize_dataframe(dataframe)
+    con = duckdb.connect()
+    try:
+        con.register("data", clean_df)
+        schema_df = con.execute("DESCRIBE data").fetch_df()
+        schema_text = schema_df.to_string(index=False)
+        sql = ask_llm(question, schema_text)
+        if not isinstance(sql, str):
+            return f"Unexpected SQL response: {sql}"
+        sql = sql.strip().strip(";")
+        sql = re.sub(r"\bSTDEV\s*\(", "STDDEV(", sql, flags=re.IGNORECASE)
+        result_df = con.execute(sql).fetch_df()
+    except Exception as exc:
+        return f"SQL error: {exc}\nSQL used:\n{locals().get('sql', 'N/A')}"
+    finally:
+        con.close()
+
+    return explain_result(question, result_df)
+
+def format_data_preview(data, max_chars: int = 12000) -> str:
+    """Return a trimmed, human-friendly preview to keep prompts under token limits."""
+    if data is None:
+        return "No data loaded."
+    try:
+        if isinstance(data, pd.DataFrame):
+            preview = data.head(20).to_csv(index=False)
+        elif isinstance(data, list):
+            chunks = []
+            for doc in data[:5]:
+                text = getattr(doc, "page_content", str(doc))
+                if len(text) > 1500:
+                    text = text[:1500] + "...[truncated]"
+                chunks.append(text)
+            preview = "\n\n".join(chunks)
+        else:
+            preview = str(data)
+    except Exception as exc:
+        preview = f"Could not format data preview: {exc}"
+    if len(preview) > max_chars:
+        preview = preview[:max_chars] + "...[truncated]"
+    return preview
+
+def summarize_csv(dataframe: pd.DataFrame) -> str:
+    """Build a compact summary (top items, payment mix) from a CSV DataFrame."""
+    if dataframe is None or not isinstance(dataframe, pd.DataFrame):
+        return ""
+    summary_lines = []
+
+    quantity_col = next((c for c in dataframe.columns if c.lower().startswith("quantity")), None)
+    desc_col = None
+    for candidate in ("Description", "Product", "Item", "Product_Name"):
+        if candidate in dataframe.columns:
+            desc_col = candidate
+            break
+    payment_col = next((c for c in dataframe.columns if "payment" in c.lower()), None)
+
+    if quantity_col and desc_col:
+        try:
+            top_items = (
+                dataframe.groupby(desc_col)[quantity_col]
+                .sum()
+                .sort_values(ascending=False)
+                .head(10)
+            )
+            summary_lines.append("Top items by quantity (sum):")
+            summary_lines.append(top_items.to_string())
+        except Exception as exc:
+            summary_lines.append(f"Could not compute top items: {exc}")
+
+    if payment_col:
+        try:
+            payment_counts = dataframe[payment_col].value_counts().head(10)
+            summary_lines.append("\nPayment method counts:")
+            summary_lines.append(payment_counts.to_string())
+        except Exception as exc:
+            summary_lines.append(f"Could not compute payment counts: {exc}")
+
+    return "\n".join(summary_lines)
+
+def build_prompt(label: str, data, question: str, summary: str = "") -> str:
+    preview = format_data_preview(data)
+    summary_text = summary.strip()
+    summary_block = f"\nData summary:\n{summary_text}\n" if summary_text else ""
+    return f"""Do not reply with a python code.
+Data preview ({label}, truncated to avoid context limits):
+
+{preview}
+
+{summary_block}
+User question: {question}
+"""
 
 def pretty_print_columns(text):
     """
@@ -31,172 +286,146 @@ def pretty_print_columns(text):
     """
     return "  ".join([line.strip() for line in text.splitlines() if line.strip()])
 
-try:
-    pswdVal = st.experimental_get_query_params()['pwd'][0]
-    if pswdVal==st.secrets["PSWD"]:
-        isPswdValid = True
-except:
-    pass
-
-if not isPswdValid:
-    st.write("Invalid Password")
-else:
-    radioButtonList = ["E-commerce CSV (https://www.kaggle.com/datasets/mervemenekse/ecommerce-dataset)",
-    "Upload my own CSV",
-    "Upload my own PDF",
-    f"URL Chat with Google's Latest Earnings ({defaultGoogleURL})",
-    "Enter my own URL"]
-
-    # Add some designs to the radio buttons
-    st.markdown("""
-    <style>
-    .stRadio {
-      padding: 10px;
-      border-radius: 5px;
-      background-color: #f5f5f5;
-    }
-
-    .stRadio input[type="radio"] {
-      position: absolute;
-      opacity: 0;
-      cursor: pointer;
-    }
-
-    .stRadio label {
-      display: flex;
-      justify-content: center;
-      align-items: center;
-      cursor: pointer;
-      font-size: 16px;
-      color: #333;
-    }
-
-    .stRadio label:hover {
-      color: #000;
-    }
-
-    .stRadio.st-selected input[type="radio"] ~ label {
-      color: #000;
-      background-color: #d9d9d9;
-    }
-    </style>
-    """, unsafe_allow_html=True)
-
-    genre = st.radio(
-        "Tired of reading your files? Chat with it using AI! Choose dataset to finetune", radioButtonList, index=0
-    )
-
-    # Initialize language model
-    load_dotenv(find_dotenv()) # read local .env file
-    llm = ChatOpenAI(model=OPEN_ROUTER_MODEL, temperature=0.1, openai_api_key=OPEN_ROUTER_KEY, openai_api_base="https://openrouter.ai/api/v1")
-
-    pdfCSVURLText = ""
-    if genre==radioButtonList[1]:
-        pdfCSVURLText = "CSV"
-        exampleQuestion = "What are the data columns?"
-    elif genre==radioButtonList[2]:
-        pdfCSVURLText = "PDF"
-        exampleQuestion = "Can you summarize the contents?"
-    elif genre==radioButtonList[3]:
-        pdfCSVURLText = "URL"
-        exampleQuestion = "What is Google's latest earnings?"
-    elif genre==radioButtonList[4]:
-        pdfCSVURLText = "URL"
-        exampleQuestion = "Can you summarize the contents?"
-    else: # Default,  E-commerce CSV
-        pdfCSVURLText = "CSV"
-        exampleQuestion = "Question1: What was the most sold item? Question2: What was the most common payment?"
-        loader = CSVLoader(file_path='EcommerceDataset.csv')
-        csv_data = loader.load()
-
-    isCustomURL = genre==radioButtonList[4]
-    urlInput = st.text_input('Enter your own URL', '', placeholder=f"Type your URL here (e.g. {defaultGoogleURL})", disabled=not isCustomURL)
-
-    isCustomPDF = genre==radioButtonList[1] or genre==radioButtonList[2]
-    uploaded_file = st.file_uploader(f"Upload your own {pdfCSVURLText} here", type=pdfCSVURLText.lower(), disabled=not isCustomPDF)
-    uploadedFilename = ""
-    if uploaded_file is not None:
-        with NamedTemporaryFile(dir='.', suffix=f'.{pdfCSVURLText.lower()}') as f:
-            f.write(uploaded_file.getbuffer())
-            uploadedFilename = f.name
-            if genre==radioButtonList[1]: # Custom CSV Upload
-                loader = CSVLoader(file_path=uploadedFilename)
-                csv_data = loader.load()
-            elif genre==radioButtonList[2]: # Custom PDF Upload
-                loader = PyPDFLoader(uploadedFilename)
-                pdf_pages = loader.load_and_split()
-
-    enableChatBox = False
+radioButtonList = ["E-commerce CSV (https://www.kaggle.com/datasets/mervemenekse/ecommerce-dataset)",
+"Upload my own CSV",
+"Upload my own PDF",
+f"URL Chat with Google's Latest Earnings ({defaultGoogleURL})",
+"Enter my own URL"]
+
+# Add some designs to the radio buttons
+st.markdown("""
+<style>
+.stRadio {
+  padding: 10px;
+  border-radius: 5px;
+  background-color: #f5f5f5;
+}
+
+.stRadio input[type="radio"] {
+  position: absolute;
+  opacity: 0;
+  cursor: pointer;
+}
+
+.stRadio label {
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  cursor: pointer;
+  font-size: 16px;
+  color: #333;
+}
+
+.stRadio label:hover {
+  color: #000;
+}
+
+.stRadio.st-selected input[type="radio"] ~ label {
+  color: #000;
+  background-color: #d9d9d9;
+}
+</style>
+""", unsafe_allow_html=True)
+
+genre = st.radio(
+    "Tired of reading your files? Chat with it using AI! Choose dataset to finetune", radioButtonList, index=0
+)
+
+pdfCSVURLText = ""
+exampleQuestion = ""
+csv_data = None
+pdf_pages = None
+
+if genre==radioButtonList[1]:
+    pdfCSVURLText = "CSV"
+    exampleQuestion = "What are the data columns?"
+elif genre==radioButtonList[2]:
+    pdfCSVURLText = "PDF"
+    exampleQuestion = "Can you summarize the contents?"
+elif genre==radioButtonList[3]:
+    pdfCSVURLText = "URL"
+    exampleQuestion = "What is Google's latest earnings?"
+elif genre==radioButtonList[4]:
+    pdfCSVURLText = "URL"
+    exampleQuestion = "Can you summarize the contents?"
+else: # Default,  E-commerce CSV
+    pdfCSVURLText = "CSV"
+    exampleQuestion = "Question1: What was the most sold item? Question2: What was the most common payment?"
+    if os.path.exists(DEFAULT_ECOMMERCE_CSV):
+        try:
+            csv_data = pd.read_csv(DEFAULT_ECOMMERCE_CSV)
+        except Exception as exc:
+            st.warning(f"Problem loading {DEFAULT_ECOMMERCE_CSV} ({exc}). Falling back to a small sample dataset.")
+    if csv_data is None:
+        # Keep a tiny inline sample so the app still works even when the CSV is missing locally.
+        csv_data = pd.DataFrame(
+            [
+                {"InvoiceNo": "536365", "StockCode": "85123A", "Description": "White hanging heart", "Quantity": 6, "UnitPrice": 2.55, "Country": "United Kingdom"},
+                {"InvoiceNo": "536366", "StockCode": "71053", "Description": "White metal lantern", "Quantity": 6, "UnitPrice": 3.39, "Country": "United Kingdom"},
+                {"InvoiceNo": "536367", "StockCode": "84406B", "Description": "Pink mini hanging heart", "Quantity": 8, "UnitPrice": 1.65, "Country": "United Kingdom"},
+            ]
+        )
+        st.info(f"{DEFAULT_ECOMMERCE_CSV} not found. Using an inline sample instead. Upload your own CSV if you need the full dataset.")
+
+isCustomURL = genre==radioButtonList[4]
+urlInput = st.text_input('Enter your own URL', '', placeholder=f"Type your URL here (e.g. {defaultGoogleURL})", disabled=not isCustomURL)
+
+isCustomUpload = genre==radioButtonList[1] or genre==radioButtonList[2]
+uploaded_file = st.file_uploader(f"Upload your own {pdfCSVURLText} here", type=pdfCSVURLText.lower(), disabled=not isCustomUpload)
+uploadedFilename = ""
+if uploaded_file is not None:
     if genre==radioButtonList[1]: # Custom CSV Upload
-        enableChatBox = uploadedFilename[-4:]==".csv"
+        try:
+            csv_data = pd.read_csv(uploaded_file)
+        except Exception as exc:
+            st.error(f"Could not read uploaded CSV: {exc}")
     elif genre==radioButtonList[2]: # Custom PDF Upload
-        enableChatBox = uploadedFilename[-4:]==".pdf"
-    elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
-        enableChatBox = True
-    elif genre==radioButtonList[4]: # Custom URL
-        enableChatBox = True
-    else: # E-commerce CSV
-        enableChatBox = True
-
-    chatTextStr = st.text_input(f'Ask me anything about this {pdfCSVURLText}', '', placeholder=f"Type here (e.g. {exampleQuestion})", disabled=not enableChatBox)
-    chatWithPDFButton = "CLICK HERE TO START CHATTING"
-    if st.button(chatWithPDFButton, disabled=not enableChatBox and not chatTextStr): #  Button Cliked
-        if genre==radioButtonList[0]: # E-commerce CSV
-            # Initializing the agent
-            answer = llm.predict(f'''
-            Do not reply with a python code.
-            I have CSV file contents below:
-
-            {str(csv_data)}
-
-            {chatTextStr}
-            ''')
-            st.write(answer)
-
-        elif genre==radioButtonList[1]: # Custom CSV Upload
-            # Initializing the agent
-            answer = llm.predict(f'''
-            Do not reply with a python code.
-            I have CSV file contents below:
-
-            {str(csv_data)}
-
-            {chatTextStr}
-            ''')
-            st.write(answer)
-
-        elif genre==radioButtonList[2]: # Custom PDF Upload
-            pdf_answer = llm.predict(f'''
-            Do not reply with a python code.
-            I have PDF file contents below:
-
-            {str(pdf_pages)}
-
-            {chatTextStr}
-            ''')
-            st.write(pdf_answer)
-        elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
-            loader = WebBaseLoader(defaultGoogleURL)
-            web_data = loader.load()
-            answer = llm.predict(f'''
-            Do not reply with a python code.
-            I have website contents below:
+        with NamedTemporaryFile(dir='.', suffix=f'.{pdfCSVURLText.lower()}', delete=False) as f:
+            f.write(uploaded_file.getbuffer())
+            uploadedFilename = f.name
+        try:
+            loader = PyPDFLoader(uploadedFilename)
+            pdf_pages = loader.load_and_split()
+        except Exception as exc:
+            st.error(f"Could not read uploaded PDF: {exc}")
+        finally:
+            if uploadedFilename and os.path.exists(uploadedFilename):
+                os.remove(uploadedFilename)
 
-            {str(web_data)}
+enableChatBox = False
+if genre==radioButtonList[1]: # Custom CSV Upload
+    enableChatBox = isinstance(csv_data, pd.DataFrame)
+elif genre==radioButtonList[2]: # Custom PDF Upload
+    enableChatBox = pdf_pages is not None
+elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
+    enableChatBox = True
+elif genre==radioButtonList[4]: # Custom URL
+    enableChatBox = True
+else: # E-commerce CSV
+    enableChatBox = True
 
-            {chatTextStr}
-            ''')
+chatTextStr = st.text_input(f'Ask me anything about this {pdfCSVURLText}', '', placeholder=f"Type here (e.g. {exampleQuestion})", disabled=not enableChatBox)
+chatWithPDFButton = "CLICK HERE TO START CHATTING"
+if st.button(chatWithPDFButton, disabled=not enableChatBox and not chatTextStr): #  Button Cliked
+    if genre==radioButtonList[0]: # E-commerce CSV
+        st.write(run_duckdb_qa(chatTextStr, csv_data))
 
-            st.write(answer)
-        elif genre==radioButtonList[4]: # Custom URL
+    elif genre==radioButtonList[1]: # Custom CSV Upload
+        st.write(run_duckdb_qa(chatTextStr, csv_data))
+
+    elif genre==radioButtonList[2]: # Custom PDF Upload
+        content = build_prompt("Uploaded PDF", pdf_pages, chatTextStr)
+        st.write(call_openrouter(content))
+    elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
+        loader = WebBaseLoader(defaultGoogleURL)
+        web_data = loader.load()
+        content = build_prompt("Google earnings URL", web_data, chatTextStr)
+        st.write(call_openrouter(content))
+    elif genre==radioButtonList[4]: # Custom URL
+        if not urlInput.strip():
+            st.warning("Please enter a URL first.")
+        else:
             loader = WebBaseLoader(urlInput)
             web_data = loader.load()
-            answer = llm.predict(f'''
-            Do not reply with a python code.
-            I have website contents below:
-
-            {str(web_data)}
-
-            {chatTextStr}
-            ''')
-            st.write(answer)
+            content = build_prompt("Custom URL", web_data, chatTextStr)
+            st.write(call_openrouter(content))

requirements.txt

@@ -1,6 +1,12 @@
-pdf2image
-langchain_openai==0.2.9
-python-dotenv
-langchain==0.3.8
-langchain_community==0.3.8
-beautifulsoup4
+streamlit==1.52.1
+pandas==2.3.3
+numpy==1.26.4
+requests==2.32.5
+langchain==0.3.8
+langchain_community==0.3.8
+langchain_openai==0.2.9
+beautifulsoup4==4.14.3
+pypdf==6.4.1
+duckdb==1.4.3
+pdf2image==1.17.0
+python-dotenv==1.2.1

sklearn_wine.csv

@@ -0,0 +1,179 @@
+alcohol,malic_acid,ash,alcalinity_of_ash,magnesium,total_phenols,flavanoids,nonflavanoid_phenols,proanthocyanins,color_intensity,hue,od280/od315_of_diluted_wines,proline,target
+14.23,1.71,2.43,15.6,127.0,2.8,3.06,0.28,2.29,5.64,1.04,3.92,1065.0,0
+13.2,1.78,2.14,11.2,100.0,2.65,2.76,0.26,1.28,4.38,1.05,3.4,1050.0,0
+13.16,2.36,2.67,18.6,101.0,2.8,3.24,0.3,2.81,5.68,1.03,3.17,1185.0,0
+14.37,1.95,2.5,16.8,113.0,3.85,3.49,0.24,2.18,7.8,0.86,3.45,1480.0,0
+13.24,2.59,2.87,21.0,118.0,2.8,2.69,0.39,1.82,4.32,1.04,2.93,735.0,0
+14.2,1.76,2.45,15.2,112.0,3.27,3.39,0.34,1.97,6.75,1.05,2.85,1450.0,0
+14.39,1.87,2.45,14.6,96.0,2.5,2.52,0.3,1.98,5.25,1.02,3.58,1290.0,0
+14.06,2.15,2.61,17.6,121.0,2.6,2.51,0.31,1.25,5.05,1.06,3.58,1295.0,0
+14.83,1.64,2.17,14.0,97.0,2.8,2.98,0.29,1.98,5.2,1.08,2.85,1045.0,0
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