Create app.py

app.py

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+
+from huggingface_hub import hf_hub_download
+
+model_name_or_path = "hlhr202/llama-7B-ggml-int4"
+model_basename = "ggml-model-q4_0.bin" # the model is in bin format
+
+model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename)
+
+n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool.
+n_batch = 256 
+
+import paperscraper
+from paperqa import Docs
+from langchain.llms import LlamaCpp
+from langchain import PromptTemplate, LLMChain
+from langchain.callbacks.manager import CallbackManager
+from langchain.embeddings import LlamaCppEmbeddings
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
+# Make sure the model path is correct for your system!
+llm = LlamaCpp(
+    model_path="./ggml-model-q4_0.bin", callbacks=[StreamingStdOutCallbackHandler()]
+)
+embeddings = LlamaCppEmbeddings(model_path="./ggml-model-q4_0.bin")
+
+docs = Docs(llm=llm, embeddings=embeddings)
+
+keyword_search = 'bispecific antibody manufacture'
+papers = paperscraper.search_papers(keyword_search, limit=2)
+for path,data in papers.items():
+    try:
+        docs.add(path,chunk_chars=500)
+    except ValueError as e:
+        print('Could not read', path, e)
+
+answer = docs.query("What manufacturing challenges are unique to bispecific antibodies?")
+print(answer)
+
+def re(r):
+
+    print(answer)
+    return r 
+
+gr.Interface(fn=re,inputs=gr.Textbox(),outputs=gr.Textbox).launch()