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.gitattributes

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+model.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: MedAI
+emoji: 💊
+colorFrom: red
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.38.0
+app_file: a.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

a.py

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+import streamlit as st
+import pandas as pd
+from langchain_community.llms import LlamaCpp
+from langchain_core.callbacks import StreamingStdOutCallbackHandler
+from langchain_core.prompts import PromptTemplate
+
+# Load the CSV file for Kendra Locator
+df = pd.read_csv('location.csv', encoding='Windows-1252')
+
+# Initialize session state for selected service and chatbot history
+if 'selected_service' not in st.session_state:
+    st.session_state.selected_service = "Kendra Locator"
+if 'user_input' not in st.session_state:
+    st.session_state['user_input'] = ''
+
+
+st.set_page_config(layout="centered", initial_sidebar_state="expanded")
+
+st.sidebar.title("KENDR LOCATOR")
+st.sidebar.write("Find One Near You!")
+
+display_option = st.sidebar.selectbox("Select:", ["Address", "Email"])
+pin_code_input = st.sidebar.text_input("Enter Pin Code:")
+
+if st.sidebar.button("Locate"):
+    if pin_code_input:
+
+        result = df[df['Pin'].astype(str) == pin_code_input]
+        if not result.empty:
+            st.sidebar.write(f"**Name**: {result['Name'].values[0]}")
+
+            if display_option == "Address":
+                st.sidebar.write(f"**Address**: {result['Address'].values[0]}")
+            elif display_option == "Email":
+                st.sidebar.write(f"**Email**: {result['Email'].values[0]}")
+        else:
+            st.sidebar.write("No results found.")
+    else:
+        st.sidebar.write("Please enter a pin code.")
+
+
+llm = LlamaCpp(
+    model_path="model.gguf",
+    temperature=0.3,
+    max_tokens=512,
+    top_p=1,
+    callbacks=[StreamingStdOutCallbackHandler()],
+    verbose=False,
+    stop=["###"]
+)
+
+template = """Below is Question that describes a task. Write a Response that appropriately completes that request. Always maintain a professional tone and avoid providing offensive or inappropriate Response.
+
+### Question:
+{input}
+
+### Response:
+{response}"""
+
+prompt = PromptTemplate.from_template(template)
+
+PROFANE_WORDS = [
+    "damn", "shit", "fuck", "bitch", "asshole", "dick", "piss", "crap", "cunt",
+    "twat", "slut", "whore", "faggot", "nigger", "kike", "chink", "gook", "spic",
+    "dyke", "suck", "cock", "pussy", "motherfucker", "bastard", "prick", "wanker",
+    "bollocks", "arse", "bloody", "bugger", "tosser", "git", "slag", "pillock",
+    "knob", "knobhead", "wazzock", "clit", "scrotum", "fanny", "ass", "freak",
+    "bimbo", "dumbass", "jackass", "wimp", "idiot", "moron", "loser", "fool",
+    "retard", "cocksucker", "shag", "shagger", "piss off", "go to hell",
+    "hell", "dammit", "son of a bitch", "jerk", "puke", "chut", "chutiyah",
+    "bhosdike", "bhenchod", "madarchod", "gandu", "gand", "bhancho",
+    "saala", "kameena", "bhenji", "bhadwa", "kothi", "aankhmar", "launda",
+    "bhikari", "sala", "billi", "bhosdika", "kothi", "sundar", "langda",
+    "kaamchor", "gaddha", "bakra", "chudiya", "gando", "bhencod", "lanat",
+    "bhoot", "chakkar", "chutak", "haramkhor", "bandar", "banda", "bakwas",
+    "nikamma", "pagal", "nalayak", "pagal", "khota", "madharchod"
+]
+
+
+def contains_profanity(text):
+    """Check if the text contains any profane words."""
+    return any(word in text.lower() for word in PROFANE_WORDS)
+
+
+def truncate_at_full_stop(text, max_length=512):
+    if len(text) <= max_length:
+        return text
+
+    truncated = text[:max_length]
+    print(f"Truncated text: {truncated}")
+
+    last_period = truncated.rfind('.')
+    print(f"Last period index: {last_period}")
+
+    if last_period != -1:
+        return truncated[:last_period + 1]
+
+    return truncated
+
+
+if st.session_state.selected_service == "Kendra Locator":
+    st.title("MedAI")
+
+    user_input = st.text_input("Your Queries:", key='temp_user_input')
+
+    if st.button("Ask Away"):
+        if user_input:
+            if contains_profanity(user_input):
+                st.markdown("<span style='color: red;'>Mind The Language Dear!</span>", unsafe_allow_html=True)
+            else:
+                formatted_prompt = prompt.format(
+                    input=user_input,
+                    response=""
+                )
+
+                response = llm.invoke(formatted_prompt)
+
+                truncated_response = truncate_at_full_stop(response)
+                st.markdown(f"**You:** {user_input}", unsafe_allow_html=False)
+                st.markdown(f"**MedAI:** {truncated_response}.", unsafe_allow_html=False)
+
+
+st.warning("Developer's notice : Responses are generated by AI and maybe inaccurate or inappropriate. Any received medical or financial consult is not a substitute for professional advice.")

gitattributes

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+model.gguf filter=lfs diff=lfs merge=lfs -text

model.gguf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e64b9b104ba6a0246f807dbe024dfbc4f7d66e8bea06a31591249ade04ff9ad3
+size 4951084928