Update app.py

app.py

@@ -1,136 +1,136 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.document_loaders import PyPDFLoader
-import os
-
-# Load the model client
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-# Initialize vector store
-vector_store = None
-
-# Preload and process the PDF document
-#PDF_PATH = "generalsymptoms.pdf"  # Path to the pre-defined PDF document
-
-PDF_PATH = "C:/Users/palanive/Documents/generalsymptoms.pdf" 
-
-def preload_pdf():
-    global vector_store
-
-    # Load PDF and extract text
-    loader = PyPDFLoader(PDF_PATH)
-    documents = loader.load()
-
-    # Split the text into smaller chunks for retrieval
-    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-    docs = text_splitter.split_documents(documents)
-
-    # Compute embeddings for the chunks
-    embeddings = HuggingFaceEmbeddings()
-    vector_store = FAISS.from_documents(docs, embeddings)
-
-    print(f"PDF '{PDF_PATH}' loaded and indexed successfully.")
-
-# Response generation
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    # max_tokens,
-    # temperature,
-    # top_p,
-):
-    global vector_store
-
-    if vector_store is None:
-        return "The PDF document is not loaded. Please check the code setup."
-
-    # Retrieve relevant chunks from the PDF
-    relevant_docs = vector_store.similarity_search(message, k=3)
-    context = "\n".join([doc.page_content for doc in relevant_docs])
-
-    # Combine system message, context, and user message
-    full_system_message = (
-        f"{system_message}\n\nContext from the document:\n{context}\n\n"
-    )
-
-    messages = [{"role": "system", "content": full_system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        # max_tokens=max_tokens,
-        stream=True,
-        # temperature=temperature,
-        # top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-        response += token
-        yield response
-
-# Gradio interface
-#demo = gr.Blocks()
-
-demo = gr.Blocks(css="""
-
-.gr-chat-container {
-    display: flex;
-    background-color: skyblue;
-    justify-content: center;
-    align-items: center;
-    height: 100vh;
-    padding: 20px;
-}
-
-.gr-chat {
-    height: 80vh;
-    width: 70vw;
-    justify-content: center;
-    align-items: center;
-    border: 1px solid #ccc;
-    padding: 10px;
-    box-shadow: 2px 2px 10px rgba(0, 0, 0, 0.1);
-    overflow-y: auto;
-}
-""")
-
-
-with demo:
-    with gr.Row(elem_classes=["gr-chat-container"]):
-        with gr.Column(elem_classes=["gr-chat"]):
-            chatbot = gr.ChatInterface(
-                respond,
-                additional_inputs=[
-                    gr.Textbox(
-                        value=(
-                            "You are going to act like a medical practitioner. Hear the symptoms, "
-                            "diagnose the disease, mention the disease name as heading, and suggest tips "
-                            "to overcome the issue. Base your answers on the provided document. limit the response to 3-4 sentences. list out the response point by point" 
-                        ),visible=False,
-                        label="System message",
-                    ),
-                ],
-                examples=[
-                    ["I am not well and feeling feverish, tired"],
-                    ["Can you guide me through quick health tips?"],
-                    ["How do I stop worrying about things I can't control?"],
-                ],
-                title="Diagnify 🕊️",
-            )
-    
-    
-if __name__ == "__main__":
-    preload_pdf()
-    demo.launch()
+import gradio as gr
+from huggingface_hub import InferenceClient
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.document_loaders import PyPDFLoader
+import os
+
+# Load the model client
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+# Initialize vector store
+vector_store = None
+
+# Preload and process the PDF document
+#PDF_PATH = "general symptoms.pdf"  # Path to the pre-defined PDF document
+
+PDF_PATH ="general symptoms.pdf"
+
+def preload_pdf():
+    global vector_store
+
+    # Load PDF and extract text
+    loader = PyPDFLoader(PDF_PATH)
+    documents = loader.load()
+
+    # Split the text into smaller chunks for retrieval
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+    docs = text_splitter.split_documents(documents)
+
+    # Compute embeddings for the chunks
+    embeddings = HuggingFaceEmbeddings()
+    vector_store = FAISS.from_documents(docs, embeddings)
+
+    print(f"PDF '{PDF_PATH}' loaded and indexed successfully.")
+
+# Response generation
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    # max_tokens,
+    # temperature,
+    # top_p,
+):
+    global vector_store
+
+    if vector_store is None:
+        return "The PDF document is not loaded. Please check the code setup."
+
+    # Retrieve relevant chunks from the PDF
+    relevant_docs = vector_store.similarity_search(message, k=3)
+    context = "\n".join([doc.page_content for doc in relevant_docs])
+
+    # Combine system message, context, and user message
+    full_system_message = (
+        f"{system_message}\n\nContext from the document:\n{context}\n\n"
+    )
+
+    messages = [{"role": "system", "content": full_system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        # max_tokens=max_tokens,
+        stream=True,
+        # temperature=temperature,
+        # top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+        response += token
+        yield response
+
+# Gradio interface
+#demo = gr.Blocks()
+
+demo = gr.Blocks(css="""
+
+.gr-chat-container {
+    display: flex;
+    background-color: skyblue;
+    justify-content: center;
+    align-items: center;
+    height: 100vh;
+    padding: 20px;
+}
+
+.gr-chat {
+    height: 80vh;
+    width: 70vw;
+    justify-content: center;
+    align-items: center;
+    border: 1px solid #ccc;
+    padding: 10px;
+    box-shadow: 2px 2px 10px rgba(0, 0, 0, 0.1);
+    overflow-y: auto;
+}
+""")
+
+
+with demo:
+    with gr.Row(elem_classes=["gr-chat-container"]):
+        with gr.Column(elem_classes=["gr-chat"]):
+            chatbot = gr.ChatInterface(
+                respond,
+                additional_inputs=[
+                    gr.Textbox(
+                        value=(
+                            "You are going to act like a medical practitioner. Hear the symptoms, "
+                            "diagnose the disease, mention the disease name as heading, and suggest tips "
+                            "to overcome the issue. Base your answers on the provided document. limit the response to 3-4 sentences. list out the response point by point" 
+                        ),visible=False,
+                        label="System message",
+                    ),
+                ],
+                examples=[
+                    ["I am not well and feeling feverish, tired"],
+                    ["Can you guide me through quick health tips?"],
+                    ["How do I stop worrying about things I can't control?"],
+                ],
+                title="Diagnify 🕊️",
+            )
+    
+    
+if __name__ == "__main__":
+    preload_pdf()
+    demo.launch()