Add app.py

app.py

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+import streamlit as st
+import requests
+import pandas as pd
+import faiss
+from sentence_transformers import SentenceTransformer
+import json
+
+# Load resources (MedQuAD and FAISS index)
+@st.cache_resource
+def load_resources():
+    data = pd.read_csv("medquad.csv")  # Load MedQuAD dataset
+    questions = data['question'].tolist()
+    answers = data['answer'].tolist()
+
+    # Load FAISS index
+    index = faiss.read_index("medquad_index.faiss")
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+
+    return questions, answers, model, index
+
+questions, answers, embed_model, index = load_resources()
+
+# Retrieval function using FAISS
+def retrieve_context(query, top_k=3):
+    """
+    Retrieve the top-k most relevant answers from the MedQuAD dataset.
+    """
+    query_embedding = embed_model.encode([query])
+    distances, indices = index.search(query_embedding, top_k)
+    relevant_answers = [answers[i] for i in indices[0]]
+    return " ".join(relevant_answers)
+
+
+def query_llama_stream(query, context):
+    """
+    Stream and process responses from Llama3.2:3b via Ollama API.
+    """
+    url = "http://localhost:11434/api/generate"
+    payload = {
+        "model": "llama3.2:3b",
+        "prompt": f"Context: {context}\n\nQuestion: {query}"
+    }
+    headers = {"Content-Type": "application/json"}
+
+    try:
+        with requests.post(url, json=payload, headers=headers, stream=True) as response:
+            response.raise_for_status()
+            for chunk in response.iter_lines():
+                if chunk:
+                    # Parse each JSON chunk
+                    data = json.loads(chunk)
+                    if "response" in data:
+                        yield data["response"]  # Extract and yield the "response" field
+    except requests.RequestException as e:
+        yield f"Error: {e}"
+
+
+def main():
+    st.title("🩺 DigiDoctor: AI-Powered Medical Assistant")
+    st.markdown("Ask your medical queries below and get real-time responses!")
+
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat history
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # Accept user input
+    if prompt := st.chat_input("Enter your question:"):
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        # Retrieve context and generate response
+        with st.spinner("Retrieving relevant context..."):
+            context = retrieve_context(prompt)
+
+        response_text = ""
+        with st.chat_message("assistant"):
+            response_placeholder = st.empty()
+            for chunk in query_llama_stream(prompt, context):
+                response_text += chunk
+                response_placeholder.markdown(response_text)
+
+        # Add assistant message to chat history
+        st.session_state.messages.append({"role": "assistant", "content": response_text})
+
+
+if __name__ == "__main__":
+    main()