Update app.py

app.py

@@ -78,7 +78,7 @@ def update_vectors(files, parser):
     
     return f"Vector store updated successfully. Processed {total_chunks} chunks from {len(files)} files using {parser}."
 
-def generate_chunked_response(prompt, model, max_tokens=1000, max_chunks=5, temperature=0.7):
+def generate_chunked_response(prompt, model, max_tokens=1000, max_chunks=5, temperature=0.2):
     client = InferenceClient(
         model,
         token=huggingface_token,
@@ -119,7 +119,7 @@ class CitingSources(BaseModel):
         description="List of sources to cite. Should be an URL of the source."
     )
 
-def get_response_from_pdf(query, model, temperature=0.7):
+def get_response_from_pdf(query, model, temperature=0.2):
     embed = get_embeddings()
     if os.path.exists("faiss_database"):
         database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
@@ -143,7 +143,7 @@ Do not include a list of sources in your response. [/INST]"""
 
     return clean_text
 
-def get_response_with_search(query, model, temperature=0.7):
+def get_response_with_search(query, model, temperature=0.2):
     search_results = duckduckgo_search(query)
     context = "\n".join(f"{result['title']}\n{result['body']}\nSource: {result['href']}\n" 
                         for result in search_results if 'body' in result)
@@ -220,8 +220,8 @@ with gr.Blocks() as demo:
     use_web_search = gr.Checkbox(label="Use Web Search", value=False)
     
     with gr.Row():
-        model_dropdown = gr.Dropdown(choices=MODELS, label="Select Model", value=MODELS[2])
-        temperature_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
+        model_dropdown = gr.Dropdown(choices=MODELS, label="Select Model", value=MODELS[1])
+        temperature_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.2, step=0.1, label="Temperature")
     
     submit = gr.Button("Submit")